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Cervical cancer: a new era

Affiliations.

1 Division of Gynecologic Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy.
2 Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy.
3 Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Mayo Clinic, Rochester, Minnesota, USA.
4 Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA.
5 Department of Obstetrics and Gynecology, National Taiwan University Hospital, Taipei, Taiwan [email protected].
6 Department of Surgery, National Taiwan University Cancer Center, Taipei, Taiwan.
7 Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
8 Obstetrics and Gynecology, Division of Gynecological Oncology, Clínica Maternidad Santa Ana, IVSS, Caracas, Venezuela, Bolivarian Republic of.
9 Division of Gynecologic Oncology, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
10 Laboratorio de Genética Molecular, Instituto de Oncología y Hematología, Caracas, Venezuela, Bolivarian Republic of.
11 Gynecologic Oncology Unit, Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale", Naples, Italy.
12 Gynecologic Oncology, Kayseri City Education and Research Hospital, Kayseri, Turkey.
13 Clinical Anatomy, Ankara University, Ankara, Turkey.
14 South Tees NHS Foundation Trust, James Cook University, Middlesbrough, UK.
15 Department of Gynecologic Oncology, Ospedale Michele e Pietro Ferrero, Verduno, Italy.
16 Gynecology, Gynecologic Oncology, Clinica ASTORGA, Medellin, and Instituto Nacional de Cancerología, Bogotá, Colombia.
17 Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, Texas, USA.
PMID: 39117381
DOI: 10.1136/ijgc-2024-005579

Cervical cancer is a major global health issue, ranking as the fourth most common cancer in women worldwide. Depending on stage, histology, and patient factors, the standard management of cervical cancer is a combination of treatment approaches, including (fertility- or non-fertility-sparing) surgery, radiotherapy, platinum-based chemotherapy, and novel systemic therapies such as bevacizumab, immune checkpoint inhibitors, and antibody-drug conjugates. While ambitious global initiatives seek to eliminate cervical cancer as a public health problem, the management of cervical cancer continues to evolve with major advances in imaging modalities, surgical approaches, identification of histopathological risk factors, radiotherapy techniques, and biomarker-driven personalized therapies. In particular, the introduction of immune checkpoint inhibitors has dramatically altered the treatment of cervical cancer, leading to significant survival benefits in both locally advanced and metastatic/recurrent settings. As the landscape of cervical cancer therapies continues to evolve, the aim of the present review is to provide a comprehensive discussion of the current state and the latest practice-changing updates in cervical cancer.

Keywords: Cervical Cancer; Immunotherapy; Radiotherapy; SLN and Lympadenectomy; Surgery.

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Competing interests: None declared.

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Effectiveness of...

Effectiveness of cervical screening with age: population based case-control study of prospectively recorded data

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This article has a correction. Please see:

Errata - July 31, 2009
Peter Sasieni , professor of biostatistics and cancer epidemiology ,
Alejandra Castanon , epidemiologist ,
Jack Cuzick , John Snow professor of epidemiology
1 Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Bart’s and The London School of Medicine, Queen Mary University of London, London EC1M 6BQ
Correspondence to: P Sasieni p.sasieni{at}qmul.ac.uk
Accepted 16 July 2009

Objective To study the effect of cervical screening on incidence of cervical cancer as a function of age with particular focus on women screened under the age of 25.

Design Population based case-control study with prospectively recorded data on cervical screening.

Setting Selected centres in the United Kingdom.

Participants 4012 women aged 20-69 with invasive cancer diagnosed in participating centres and two controls per case individually matched on age and area of residence.

Main outcome measures Odds ratios for strength of association between cervical cancer and screening at particular ages.

Results There is no evidence that screening women aged 22-24 reduced the incidence of cervical cancer at ages 25-29 (odds ratio 1.11, 95% confidence interval 0.83 to 1.50). Similar results were seen for cancers restricted to squamous carcinoma or FIGO (International Federation of Gynaecology and Obstetrics) stage IB or worse, but the numbers are insufficient to provide narrow confidence intervals. Screening was associated with a 60% reduction of cancers in women aged 40, increasing to 80% at age 64. Screening was particularly effective in preventing advanced stage cancers.

Conclusions Cervical screening in women aged 20-24 has little or no impact on rates of invasive cervical cancer up to age 30. Some uncertainly still exists regarding its impact on advanced stage tumours in women under age 30. By contrast, screening older women leads to a substantial reduction in incidence of and mortality from cervical cancer. These data should help policy makers balance the impact of screening on cancer rates against its harms, such as overtreatment of lesions with little invasive potential.

Introduction

Cervical screening is a complex process that requires careful analysis to determine the balance between its benefits and harms. For society it is important to show that screening will provide a net benefit at an affordable cost. These issues have been given prominence in the recent public controversy regarding screening in women aged 20-24.

Unfortunately policy makers are often forced to make decisions based on limited evidence. Such was the situation in 2003, when the screening programme in England was reorganised. One of those changes—to first invite women for cervical screening only once they reached the age of 25 (instead of between 20 and 24)—was and has remained controversial. 1 2 3 4 5 6 The decision to change and standardise the age at first invitation was based, in part, on an earlier paper of ours, which showed that the relative reduction in frank invasive cervical cancer associated with screening was substantially less in women aged 20-34 than it was in older women. 7

The existing literature is limited partly because causal inference from case-control studies is hampered by several biases and the possibility that factors other than those studied are driving the observed associations. Nevertheless, in the absence of randomised controlled trials addressing the particular question of interest, careful analysis of well designed observational studies provides the best evidence on which to modify existing screening programmes. The landmark meta-analysis from the International Agency for Research on Cancer (IARC) provided no details regarding the age dependence of the results, but stated that “age did not affect either the sensitivity of cytological screening or the distribution of the sojourn time of the disease. In particular, there was no evidence that younger women (under 35) were more at risk of developing fast growing tumours.” 8 We previously carried out a similar analysis on UK data in three age groups: 20-39, 40-54, and 55-69. We found that the reduction in risk three to five years after a normal smear result was greater in the older age groups. 7 Our finding was confirmed by a smaller Italian study 9 and, to a lesser extent, by a recent paper from Australia, 10 but an important Swedish audit found no evidence of screening being less effective in young women. 11

Here we studied how the association between cervical screening and a subsequent decrease in cervical cancer varies with age. We used a substantially enlarged dataset from the UK by estimating the odds ratios associated with screening in overlapping narrow age bands. Additionally, we focused on the age at which screening occurs rather than the age at which the cancers are diagnosed.

Participants

Women diagnosed with cervical cancer were identified from histology laboratory records between January 1990 and April 2008. Local collaborators collected case-control data for a year at a time. Different centres collected cases over differing time periods depending on the availability of a collaborator. (See table on bmj.com for a breakdown of cases by year of diagnosis and region of residence within the UK.) Women with invasive (including micro-invasive) cervical cancer were classed as cases. Eligible controls were women who had ever been registered with a National Health Service (NHS) general practitioner (and had not subsequently died or emigrated). Such women have a record in the national cervical screening call/recall system. All controls were individually matched to cases on age and place of residence: one control had the same general practitioner as the case and a second had a different general practitioner but was within the same administrative area. Occasionally, only one control could be identified. The use of the same general practitioner provided a crude surrogate for socioeconomic status and ethnicity. We selected a control from a different general practitioner to avoid overmatching as screening uptake is closely related to the general practitioner’s enthusiasm for cervical screening. We excluded cases not in the call/recall system at the time of diagnosis (because such women could not be selected as controls). Control selection was done blinded to the screening information and in most cases by random selection (with a computer program). Data were collected on all selected controls so there was no selection or participation bias. Data on screening histories were abstracted from routinely recorded cervical cytology records held on the call/recall system (and as such were not subject to recall bias). These records include all NHS (and many private) smear tests carried out in the UK since 1988. After local NHS staff linked screening data to cases and controls, the data were anonymised before being transferred to us for analysis. Details of the design have been published previously. 7 12

After the publication of the paper in 2003 we found that 40 of the original cases had been duplicated (that is, the same woman was reported twice). As we have now removed the duplicates from the database the numbers of cases reported in the original article and the numbers of “previous” cases reported here are not the same. Figure 1 ⇓ provides a breakdown of cases used throughout this paper. ⇓

Fig 1 Cases included in this paper

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Statistical methods

We used conditional logistic regression to estimate the association between having an adequate smear test taken in a particular three year age band (such as 22-24) with the incidence of cervical cancer in the subsequent five year band (such as 25-29). The association is expressed as the odds ratio for developing cervical cancer in the next five year interval in those screened in a given (three year) age band compared with those not screened in that age band or in the two previous years. We repeated these analyses in overlapping age bands (that is, screening at 22-24, 23-25, 24-26, etc). All age bands are inclusive (that is, 22-24 means ≥22 and <25). See appendix I on bmj.com for further details, including the results of sensitivity analyses.

To ensure that age differences in effectiveness were not attributable to a different impact of screening on micro-invasive cancer or on adenocarcinoma, we looked at the effect of screening separately by stage and histology. Finally, we considered both all available data and only data obtained subsequent to our previous publication. 7 The latter was done to examine possible trends associated with changes in screening policy and practice.

In an attempt to understand the reason for screening being less effective in young women we also looked, by age group, at the proportion of cases classified as screen detected, prevalent, interval, never screened or lapsed and “after an abnormal smear result”. See appendix II on bmj.com for the intuitive description (as well the formal definition) of these categories.

Analyses were done in Stata 10 (StataCorp, College Station, TX).

Association between screening and cervical cancer at different ages

Figure 2 shows the main results, with selected details in table 1 ⇓ ⇑ . We included 4012 women with cervical cancer (any stage, including IA) diagnosed between 1990 and 2008 (including 1709 diagnosed since 2000) and 7889 controls. Figure 2 shows the odds ratio of cervical cancer in screened versus unscreened women as a function of age. ⇓ At older ages the odds ratios are substantially below 1.0. They increase with decreasing age and are greater than 1.0 for those screened at age 20-22. The odds ratios relate to cancers diagnosed in a specific age band for women screened during a previous age band. For example, the estimated odds ratio of 0.26 plotted at a screening age band of 52-54 is to be interpreted as follows: the relative risk of having cervical cancer diagnosed at age 55-59 is about 0.26 in women screened at age 52-54 compared with women not screened between ages 50 and 54. These odds ratios vary from 0.18 to 0.36 in age bands from 40-42 to 62-64, respectively, corresponding to screening being associated with a reduction in the risk of cervical cancer over the subsequent five to eight years of between 64% and 82%. In younger women the effect of screening is substantially and significantly less. Screening at ages 30-37 is associated with a reduction in the risk of cervical cancer over the next five years of between 43% and 60% (see broken lines on fig 2). The odds ratio for screening in the age band 22-24 is 1.11 (95% confidence interval 0.83 to 1.50) (table 1). ⇓ Similar estimates were obtained for screening at 20-22 and 21-23 (fig 2). Thus screening at ages 20-24 has no detectable impact on cervical cancer rates at ages 25-29.

Fig 2 Odds ratio for developing invasive cervical cancer stage IA or worse (in the next five year interval) in those screened in a given (three year) age band compared with those not screened in that age band (or in two previous years). Odds ratios plotted for overlapping age bands. Broken lines indicate risk of developing cervical cancer at ages 33-40 and 43-65. Odds ratios and confidence intervals are truncated at 1.2. Figure is based on 4012 cases (including 437 in women under age 30) and 7889 controls

Protective effect of screening in past against developing cancer (all stages) in future

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We also looked separately at the data on 1821 cases collected since our previous publication (figure not shown). The pattern was similar to that for all the data. In particular, odds ratios were still close to 1.0 for women screened at age 20-25.

Association between screening at different ages and cervical cancer by stage and histological type of cancer

Figure 3 ⇓ shows the sensitivity of these results to stage or histological type of cancer, or both. The overall patterns are similar to those in figure 2. In particular, when restricted to stage IB+, the odds ratios are not significantly different from unity at young ages; as before they fall rapidly with increasing screening age from 25 to 34 years and then become fairly flat with a nadir around age 55. The odds ratios are even lower for more advanced disease, showing that advanced cancer is particularly rare in screened women. The pattern for women with stage II or worse cancer is slightly different with a dip at age 22-24 and a second peak at age 25-28. There were only 38 cases of advanced cancer (stage II or worse) in women under age 30 so the estimated confidence intervals (fig 3d) are wide.

Fig 3 Effect of stage and histology type on odds ratio of developing cervical cancer (in next five year interval) given screening in indicated age bands. Graph shows diagnosed cancer with five years of follow-up and compares those screened in the previous three years with those not screened in the previous five years. A: 2589 cases (303 in women under age 30) and 5122 controls; B: 2448 cases (172 in women under age 30) and 4821 controls; C: 1525 cases (107 in women under age 30) and 3025 controls; D: 897 cases (38 in women under age 30) and 1764 controls

Despite there being over 350 women with cervical cancer (any stage) aged 25-29, there is no indication of any benefit of screening at age 22-24 (compared with those not screened at age 20-24) (1.11, 0.83 to 1.50) (table 1). ⇑ Restriction of analysis to stage IB+ cervical cancer made little difference to the point estimate, but resulted in a wider confidence interval and allowed for the possibility of a greater effect (1.03, 0.63 to 1.7) (fig 3B). ⇑ Further restriction to stage IB+ cervical cancer in women aged 25-27 at diagnosis (see appendix I on bmj.com) limited the analysis to 65 women and yielded an estimated odds ratio of 0.52 (0.23 to 1.2).

Benefit associated with being screened twice by age 26

It has been argued that screening begins to be fully effective only once a woman has been screened twice and that consequently women screened aged 20-24 and again at age 25 will have a greater benefit from screening after age 25 than will those who are first screened at age 25. To study this we compared women screened both at ages 20-22 and 23-25 with those first screened only aged 23-25. In an analysis of cancers diagnosed between ages 26.5 and 29.0 years restricted to women who were screened between ages 23 and 25 (inclusive) the odds ratio for stage IB+ cancer associated with also being screened between ages 20 and 22 (inclusive) was 0.90 (0.38 to 2.2). For all cancers (including stage IA) the odds ratio was 1.1 (0.62 to 2.0).

Screening classification of diagnoses in women before age 25

There were 73 cancers diagnosed in women at age 20-24 (inclusive). Of these 73 women, five had had no previous smear tests, 32 were classified as screen detected (13 on their first screen and 19 on subsequent tests), 15 as interval cancers (last result was normal), and in 21 the diagnosis followed a history of abnormal results (table 2 ⇓ ). In these young women, 75% of all cancers, 76% of stage IA cancers, and 81% of cancers stage IB or worse occurred despite screening. We consider cancers classified as interval, screen detected (previously screened), and “after an abnormal result” to have occurred despite screening.

Screening history for women aged 20-24 at time of diagnosis. Figures are numbers (percentages) of women

Most screen detected (previously screened) cancers were micro-invasive (12 out of 19 with known stage), whereas most of the interval cancers were stage IB or worse (12 out of 15 with known stage) (Fisher’s exact test P=0.017). Of the 18 stage IB+ cancers in women with a previous normal screening history, 11 (61%) occurred within 3.5 years of a negative smear test result. We applied the same classification of screening history to all women with cervical cancer stage IB or worse. Table 3 shows the results in 10 year age groups ⇓ . The proportion of women with stage IB+ cancer who had not been screened in the previous six years (never/lapsed) increases with age. Compared with cases in women aged 40-69, women aged 20-29 with stage IB+ cancer were far less likely to be classified as “never screened.”

Distribution of cancers stage IB or worse according to screening classification. Figures are numbers (percentages) of women

This study confirms our previous findings that cervical screening in women aged 20-34 is less effective than in older women. By studying the effect of screening in smaller age groups, we have shown that the efficacy of screening decreases with decreasing age, even within the age range 20-34.

On average, participation in the UK cervical screening programme by a woman aged 35-64 reduces her risk of cervical cancer over the next five years by 60-80% and her risk of advanced cervical cancer by about 90%. The benefit of screening for women aged 25-34 is more modest. Screening in women aged 20-24 has little or no impact on the incidence of cervical cancer under the age of 30. This applied whether we looked at all cancers or restricted analysis to frankly invasive (that is, stage IB or worse) squamous carcinoma, or even to stage II or worse (fig 3). Because stage IB+ cancer is rare in young women, however, the confidence intervals are wide and our data do not rule out the possibility of screening in women aged 20-24 being effective in reducing stage IB+ cancer in women aged 25-27.

It has also been argued that women should have their first two smear tests close in time to minimise the impact of an initial false negative result. 13 Our results provide no evidence that women screened aged 20-22 and then again at 23-25 are better protected than those screened only at age 23-25.

A careful review of the screening histories of women aged 20-24 with a diagnosis of cervical cancer suggests that few (if any) of the cancers occurred through a lack of screening. Indeed only five of these 73 women had not been screened previously.

Strengths and weaknesses

We used prospectively recorded screening data and selected controls at random, thus eliminating both recall bias and selection bias (data were obtained for all selected controls). We believe this design to be the most appropriate given that a randomised controlled trial was not possible. Other papers have analysed trends in incidence of cancer (or mortality, or both) before and after the introduction of screening to estimate the impact of screening in the population. 14 15 16 17 Such analyses rely on comparison of observed rates with estimates of what would have happened in the absence of screening; they are subject to trends in other factors such as the quality of the cancer registry data. For women aged 20-29, who have been offered screening from the age of 20, it is not possible to reliably estimate what their rates would have been in the absence of screening.

It is always possible to criticise observational studies as women who attend screening might differ from those who do not so that any observed effect might not be causal. For the observed difference in the benefit of screening at different ages to be caused by confounding, however, there would have to be differences in the way confounders affect the results at different ages. We know of no evidence to support such an interaction and suggest that differential benefits with age are not caused by confounding but reflect the true effects of screening. We think there are few biases in this analysis and are comfortable in viewing the associations as causal and using the term “protection offered by screening” to describe the odds ratios.

This large study of the impact of cervical screening on invasive cervical cancer contains more cases than all other studies with detailed screening information combined. Some of the data presented have been published previously, but over 45% are new. The results of analyses limited to the new data are qualitatively similar to those using all the data, suggesting that there have been no changes in the impact of screening in young women on rates of cervical cancer despite improvements in the quality of screening in the UK.

The new analysis considers the association between screening in one age group (for example, 25-29) and cervical cancer in the subsequent five years (at ages 30-34 for this example). With this approach, the exposure is close to the usual definition of screening coverage used by many screening programmes: the proportion of eligible women screened in the past five (or three) years. Furthermore, this approach more closely reflects what one could estimate by prospectively following a cohort of women. With a coverage interval of three years and a follow-up of five years, however, it could be as much as eight years between the last screen and a diagnosis of cancer. Thus the benefit of regular three (or five) yearly screening could be considerably greater than that implied by this model. Nevertheless, the approach does attempt to measure the protection achieved with screening (from the treatment initiated by a positive smear result) rather than the low risk periods associated with a negative smear result.

Comparison with other studies

Much of the earlier literature aiming to study the protection afforded by cervical screening did not consider the possibility of different levels of protection depending on age. An important recent paper reported on the results of an audit of cervical cancer in Sweden. 11 The odds ratios in that paper are similar for all age groups with an odds ratio of 0.42 (1/2.37) for the effect of three yearly screening on incidence of cancer at ages 21-29. It is important to consider the methodological differences between the analyses when interpreting these results. They consider a woman (aged 20-52) to have been screened if she had a smear test between 3.5 years and 6 months before (the date of the case’s) diagnosis. They include stage IA cancers, most of which will have been screen detected, as well as screen detected stage IB cancer. Consider a screen detected cancer in a woman who has two smear tests 3.5 years apart. If the smear test that led to diagnosis is within six months of diagnosis she will be classed as unscreened. A control woman who is screened every 3.5 years will have a chance of 86% (3.0/3.5) of having had a smear test in the particular interval width of three years. Thus the inclusion of screen detected cases introduces a considerable bias in favour of screening. As the proportion of cancers that are stage IA or screen detected stage IB is greater in young women, the bias is particularly strong in young women. To illustrate the point, the same analysis applied to women aged 20-29 in our study yields an odds ratio of 0.46 (0.38 to 0.56).

A case-control study in New South Wales, Australia, found that screening every two years seemed to be more protective in women over the age of 30 than in those aged 20-29. 10 The more favourable results for screening in women aged 20-29 in that study could be because their controls were selected from women who had been for screening (albeit possibly only after the date of diagnosis of the case).

Interpretation of the results

As we designed the study to eliminate most of the biases that affect case-control studies, our observed associations are almost certainly either causal or the result of confounding. The heterogeneity of association in different age bands within the same study argues strongly that these effects are real. There might be biological reasons for cervical screening working better in older women. Undoubtedly the specificity of screening is less in younger women because human papillomavirus (HPV) infections are so much more common. This does not, however, explain why the sensitivity of screening should be less. We favour the explanation that, by necessity, a cancer in a woman aged 25 (infected at, say, 15 years) will have progressed from HPV infection through cervical intraepithelial neoplasia grade III to cancer faster than in a woman aged 55 (infected at perhaps 25). This means that the opportunities for detecting the small proportion of cervical intraepithelial neoplasia grade III in women in their early 20s that will progress to cancer within at most a few years are small. It is an extreme example of length bias: most cases of cervical intraepithelial neoplasia grade III detected will be slow growing and could safely be left for several years; but the rare cases that are progressing rapidly will probably be missed.

It has been argued that screening from age 20 could prevent more cancers in women aged 25-34 than screening from age 25. Our study has little power to detect such an effect. Policy decisions should be based on balancing the benefits and harms of screening and the need to take into account the underlying risk of cervical cancer at different ages. Such an analysis is beyond the scope of this paper. We have provided more accurate estimates of the benefits of cervical screening in different age groups, which should aid policy makers in making their decisions. As screening undoubtedly leads to the detection of many cases of stage IA cancer in women aged 20-24, one might think it must be doing good by preventing more advanced cancers. If this were the case, we should have found that screening at ages 20-24 leads to a reduction in stage IB+ cancers. That we found no such reduction suggests that most of the stage IA cancers detected by screening women aged under age 25 would still be stage IA at ages 25-26 and could be picked up by screening at age 25 without adverse consequences.

This study is based on cancers diagnosed in the UK between 1990 and 2008 and smears taken within the UK from 1988 to 2008. Cervical cancer rates in women in their 20s have been relatively high compared with those in other countries and abnormal cytology results have been more common in those aged 20-34 than in older women throughout this period. 18 19 We believe that the standard of smear taking, cytology reading, and fail safe procedures for cervical screening in this study have mostly been high. Since the early-mid 1990s the UK screening programmes have put great emphasis on quality assurance, and there is evidence that by the late 1990s, UK cytology was as good as (or better than) anywhere in the world. 20 Thus our finding that cervical screening in women aged 20-24 has at best a modest effect on the incidence of cervical cancer at ages 25-29 is almost certainly generalisable to other countries. We have no reason to believe that it would be substantially more effective elsewhere.

Any decision on when to start screening women will have to weigh up benefits and harms and might depend on the local status quo. In a setting where screening is offered to women aged 20-24 policy makers might decide to continue this policy as we have not shown that the harms exceed the benefits. By contrast, where screening is not offered to women aged 20-24, the lack of evidence of any benefit from screening in this age group dictates that the policy should not change.

Unanswered questions and future research

Our study does not consider the harms of screening at different ages nor do we take into account the absolute rate of cervical cancer in screened and unscreened women of different ages. Such a synthesis of research is necessary for rational policy making. Undoubtedly, however, decision making will be complicated because of the uncertainty in many of the estimates of harm and benefit. As we have seen, the confidence intervals for the impact of screening at ages 20-24 on stage II+ cervical cancer are extremely wide. We have not even attempted to estimate the added impact of starting screening five years earlier on cancer at ages 30-44.

The most common harms of screening are the anxiety caused by abnormal test results and the trauma of treating cervical intraepithelial neoplasia that would never have progressed to cancer. These can be easily estimated. Treatment might be associated with premature delivery during subsequent pregnancies. If the association observed in several studies 21 22 is causal then screening might do serious harm, but the association might simply be because of confounding. These issues require careful study.

The question of screening women aged 20-24 will decrease in importance as the cohort of women vaccinated against HPV types 16 and 18 reach their 20s. If it is questionable whether screening is worth while in unvaccinated women aged 20-24, there can be no doubt that the risk of cancer in women aged under 25 who are vaccinated before exposure to HPV will be low enough to make screening at such an age unjustifiable.

What is already known on this topic

Cervical screening has had a substantial impact on the incidence and mortality of cervical cancer in many developed countries

Most of the benefit from screening comes from the prevention of cervical cancer, but it can also lead to downstaging

The relative protection against cervical cancer might be less at ages 20-34 than in older women

What this study adds

Cervical screening in women aged 20-24 is substantially less effective in preventing cancer (and in preventing advanced stage tumours) than is screening in older women

The new methods for the analysis of case-control studies of screening avoid some of the biases associated with previously used statistical methods

Cite this as: BMJ 2009;339:b2968

We thank all those who have helped with the design and conduct of this study and acknowledge the participation of millions of women and nameless healthcare professionals in the cervical screening programmes in the UK.

Working group members : Joanna Adams, Alejandra Castanon, Jack Cuzick, Elaine Farmery, Hillary Fielder, Muir Gray, David Mesher, Julietta Patnick, and Peter Sasieni.

Local collaborators : C Camilleri-Ferrante and A Thompson (East Anglia); P Grey and M J Platt (Macclesfield and Warrington); D Haran (Chester and Wirral); F Fowler (Southend); S Chatterton (Oxfordshire, Northants, Buckinghamshire, Berkshire); S Butterworth, M Vaille and J Underdown (Maidstone); R Swann (Medway); L Robinson (south west Surrey, west Surrey and north east Hampshire); A Burtenshaw (Mid Downs); S Samarsinghe (Kingston and Richmond); C Furlong (Enfield and Haringey); C Singleton (N Derbyshire); K Boyd (East Dorset); A Herbert and C Breen (Southampton and south west Hampshire); E Farmery (Wiltshire and Bath); L Daborn and K Jaber (west Dorset); J Grainger (Shropshire); G D H Thomas (Calderdale); W Young (Humberside); S Jennings (Leicestershire); F Boer (Brighton and Hove); R J Fitzmaurice (Huddersfield); Y Burlay and H Belza (Grimsby); S Barraclough and H Belza (Scunthorpe). I Duncan and K A Hussein (Dundee and Angus); L Reay (Argyll and Clyde); L Caughley (Northern Ireland); S Burgess (Clwyd); DC Watkins (Gwent); Helen Beer (Cervical Screening Wales); Quality Assurance Reference Centre (QARC) in north west England and the QARC in east England.

Contributors: PS participated in the design and establishment of the study, collation of data, design of the database, and analysis of the data. AC participated in the collation and analysis of the data. JC participated in the design and establishment of the study. All authors wrote the paper and saw and approved the final version. PS is guarantor.

Funding: This work was supported by Cancer Research UK (C8162/A6127 and C8162/A9481) and previously by the NHS cervical screening programme. Neither organisation had any input in the analysis or interpretation of the data or the writing of the paper.

Competing interests: None declared.

Ethical approval: Not required. Permission to link cervical screening and histology (cancer registration) data has been granted by the Patient Information Advisory Group (PIAG).

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cervical Cancer Research

The expanded approval of two HPV tests allows the patient to collect a vaginal sample themselves in a health care setting, rather than a health provider collecting a sample during a pelvic exam. The availability of a self-collection option in health care settings could help widen access to cervical cancer screening.

For some people with early-stage cervical cancer, a surgical procedure called a simple hysterectomy may be a safe and effective alternative to treatment with a radical hysterectomy, results from the SHAPE trial show.

It may be worthwhile for some individuals between ages 65 and 69 to get tested for HPV, findings from a Danish study suggest. Specifically, the testing may help prevent cervical cancer among those who haven’t had cervical cancer screening for at least 5 years.

One dose of the HPV vaccine was highly effective in protecting young women against infection from high-risk HPV types, a study in Kenya found. A single dose would make HPV vaccines more accessible worldwide, reducing cervical cancer’s global burden.

The rates of timely cervical cancer screening fell between 2005 and 2019, researchers found, and disparities existed among groups of women. The most common reason for not receiving timely screening was lack of knowledge about screening or not knowing they needed screening.

Fewer women with early-stage cervical cancer are having minimally invasive surgery, including robotic, as part of their treatment, a new study shows. The shift toward more open surgeries follows the release of results from the LACC trial in 2018.

Widespread HPV vaccine use dramatically reduces the number of women who will develop cervical cancer, according to a study of nearly 1.7 million women. Among girls vaccinated before age 17, the vaccine reduced cervical cancer incidence by 90%.

Updated cervical cancer screening guidelines from the American Cancer Society recommend HPV testing as the preferred approach. NCI’s Dr. Nicolas Wentzensen explains the changes and how they compare with other cervical cancer screening recommendations.

In a new study, an automated dual-stain method using artificial intelligence improved the accuracy and efficiency of cervical cancer screening compared with the current standard for follow-up of women who test positive with primary HPV screening.

More than a decade after vaccination, women who had received a single dose of the HPV vaccine continued to be protected against infection with the two cancer-causing HPV types targeted by the vaccine, an NCI-funded clinical trial shows.

Women with cervical or uterine cancer who received radiation to the pelvic region reported side effects much more often using an online reporting system called PRO-CTCAE than they did during conversations with their clinicians, a new study shows.

A research team from NIH and Global Good has developed a computer algorithm that can analyze digital images of the cervix and identify precancerous changes that require medical attention. The AI approach could be valuable in low-resource settings.

A new test can help to improve the clinical management of women who screen positive for HPV infection during routine cervical cancer screening, an NCI-led study has shown.

FDA has approved pembrolizumab (Keytruda) for some women with advanced cervical cancer and some patients with primary mediastinal large B-cell lymphoma (PMBCL), a rare type of non-Hodgkin lymphoma.

By comparing the genomes of women infected with a high-risk type of human papillomavirus (HPV), researchers have found that a precise DNA sequence of a viral gene is associated with cervical cancer.

Investigators with The Cancer Genome Atlas (TCGA) Research Network have identified novel genomic and molecular characteristics of cervical cancer that will aid in subclassification of the disease and may help target therapies that are most appropriate for each patient.

MINI REVIEW article

Advanced, recurrent, and persistent cervical cancer management: in the era of immunotherapy.

1 Department of Clinical Research, Instituto Nacional de Cancerología (INCan), Mexico City, Mexico
2 Department of Medical Oncology, Instituto Nacional de Cancerología (INCan), Mexico City, Mexico
3 Department of Surgical Oncology, Hospital Regional de Alta Especialidad de la Península de Yucatán, Yucatán, Mexico
4 Department of Medical Oncology, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
5 NSR III Researcher assistant, Consejo Nacional de Humanidades, Ciencias y Tecnologías(CONACYT), Mexico City, Mexico
6 Department of Medical Oncology, Centro Médico Nacional 20 de Noviembre, Mexico City, Mexico

Cervical cancer constitutes a significant health burden for women worldwide despite being preventable by vaccination and screening. Advanced stages of the disease are associated with a poor prognosis, and treatment approaches have seen little change over several decades, resulting in an overall survival rate of no more than 17 months. Additionally, there are limited options for second-line treatment. The urgent need for innovative and effective therapies to improve the outlook for this group of patients, along with an enhanced understanding of the interactions between the disease and the host’s immune system, has propelled immunotherapy into a rapidly advancing field with notable achievements. Among various immunotherapeutic approaches, immune checkpoint inhibitors emerge as the most advanced treatment option. Clinical trials assessing these inhibitors as single agents or in combination with chemotherapy show promising results. As immunotherapy begins to redefine standards of care for metastatic, recurrent, or persistent cervical cancer, this review addresses recent advances and current recommendations for its management in both first and second-line treatment. The goal is to provide insights into the evolving landscape of cervical cancer treatment, specifically focusing on immunotherapeutic interventions.

Introduction

Despite being a completely preventable disease, in developing countries, cervical cancer (CC) is a major contributor to cancer-related deaths in women. For a long time, cisplatin as monotherapy or in combination represented the standard treatment for this patient group, with an overall survival not exceeding 13 months. In recent years, the advent of targeted therapies such as immunotherapy has significantly improved the prognosis for these patients.

Immunity and cervical cancer

Human Papillomavirus (HPV) is a necessary but not sufficient etiological factor for developing cervical cancer. Infected basal epithelial cells hosting HPV express only early genes. However, HPV integration into the host genome leads to the expression of oncogenes E6 and E7 ( 1 , 2 ). Integration of the HPV genome is a critical step in the development of HPV-associated cancers. This integration event preferentially occurs at fragile sites within the human DNA, regions characterized by increased susceptibility to breakage and rearrangement. The subsequent expression of viral oncogenes E6 and E7 is not only essential for the initiation and progression of premalignant lesions but also actively promotes genomic instability, further contributing to cellular transformation and malignant progression ( 3 ).

HPV-infected cells can evade immunosurveillance by inhibiting acute inflammation and immunological recognition. This viral and inflammatory cancer environment has been shown to be responsible for inducing PD-L1 expression ( 4 ). There is evidence that PD-L1 expression plays a significant role in creating an “immune privileged” site for initiating and persisting HPV infection by downregulating T-cell activity and generating adaptive immune resistance ( 5 , 6 ). High-level PD-L1 expression is rare in healthy cervical tissue, but is increased in T cells and tumoral cells in 35 to 96% of cervical cancers ( 7 ).

Immunological escape is associated with local negative regulation as well as evasion of immune system detection, including increased regulatory T cells (Treg), loss of major histocompatibility complex (MHC) antigen presentation, chronic inflammation, and regulation of immune checkpoint molecules ( 8 ). Targeting tumor-specific antigens remains a cornerstone of cancer immunotherapy. However, the immunosuppressive tumor microenvironment presents a significant challenge, often hindering the efficacy of such targeted approaches. Therefore, therapeutic strategies aimed at reversing this immunosuppression within the tumor microenvironment are crucial for enhancing the efficacy of cancer immunotherapy. This can involve approaches such as inhibiting checkpoint molecules, depleting regulatory T cells, or promoting the activity of immunostimulatory cells and cytokines. Therapeutic interventions, such as immune checkpoint blockade targeting molecules like PD-1/PD-L1 and CTLA-4, aim to overcome this challenge; they are not specific for HPV antigens, and if successful, they can be efficient in the majority of cervical cancer cases, regardless of the associated HPV type ( 9 ). Tumor cells often exploit immune checkpoint pathways as a mechanism for immune evasion. Therapeutic interventions targeting immune checkpoints, such as PD-1/PD-L1 blockade, can restore T cell function and promote tumor cell killing. By preventing the inhibitory signals mediated by these checkpoints, T cell proliferation and cytotoxic activity against cancer cells are enhanced within the tumor microenvironment.

Immune checkpoint inhibitors

The application of immunotherapy in CC treatment is grounded in several key molecular features observed in these tumors. Elevated Tumor Mutational Burden, Microsatellite Instability, high PD-L1 expression, and elevated Tumor Inflammatory State, suggest an environment conducive to successful immunotherapy intervention. Therefore, the convergence of these molecular features in CC provides a strong rationale for employing immunotherapy as a treatment strategy ( 10 ).

Various tumors, including CC, express PD-L1, an immune checkpoint molecule mediating tumor cell escape from immune system-mediated destruction ( 11 ). PD-L1 expression by tumors enables them to evade destruction by CD8+ T cells. PD-1 is a crucial immune checkpoint molecule involved in maintaining self-tolerance and modulating the immune response. During an inflammatory response to infection, PD-1 expression on activated effector T cells helps prevent autoimmunity by attenuating T cell activation. However, within the tumor microenvironment, PD-1 can contribute to immune resistance. PD-1 is expressed on various immune cells, including activated T cells and regulatory T cells (Tregs). Notably, PD-1 expression on Tregs, coupled with ligand engagement, enhances their proliferation and amplifies their immunosuppressive function. Furthermore, PD-1 expression extends beyond T lymphocytes to include B cells and other immune subsets ( 9 ).

PD-L1 and PD-L2 serve as the two ligands for PD-1. Binding of either ligand to PD-1 triggers a co-inhibitory signal within activated T cells, leading to suppression of their effector functions. In the context of cancer, PD-1 is frequently upregulated on tumor-infiltrating lymphocytes (TILs) across diverse tumor types ( 12 ). This, coupled with the common overexpression of PD-L1 on tumor cells, facilitates immune evasion by inhibiting anti-tumor T cell responses ( 13 , 14 ). Further, PD-L1 expression is an independent prognostic factor for poor outcome, irrespective of established clinicopathological features, including stage, tumor size, depth of invasion, lymphovascular invasion, and lymph node involvement ( 15 ).

First-line treatment and recent advances

Advanced, persistent, or recurrent CC has a 5-year survival rate of 17%. Thus, median progression-free survival (PFS) (2 to 5 months) and overall survival (OS) (5 to 16 months) is low for individuals who can’t undergo surgery or radiotherapy ( 16 ).

For many years, platinum-based chemotherapy represented the standard treatment for this patient group, with response rates of 13% and 36% for monotherapy or combination therapy, respectively ( 17 – 19 ). The GOG 204 study, comparing four cisplatin combinations (cisplatin/paclitaxel, cisplatin/topotecan, cisplatin/gemcitabine, and cisplatin/vinorelbine), found no difference in overall survival. While not statistically significant, a trend towards improved efficacy was observed in the cisplatin/paclitaxel group compared to the other treatment combinations. This trend was evidenced by numerically higher response rates, as well as longer progression-free survival (PFS) and overall survival (OS) (12.9 months vs. 10 months), which have established it as the preferred regimen since 2009 ( 17 ). Subsequently, the phase III JCOG0505 non-inferiority study comparing carboplatin/paclitaxel vs. cisplatin/paclitaxel demonstrated the non-inferiority of the carboplatin/paclitaxel regimen in terms of median overall survival (17.5 months vs. 18.3 months, p=0.032), with a different toxicity profile but consolidating platinum and taxane chemotherapy as the first-line treatment choice ( 19 ).

In 2014, the first targeted therapy with a benefit in metastatic, recurrent or persistent cervical cancer was established with the GOG 240 study. Bevacizumab, a monoclonal antibody, acts as an antiangiogenic agent by neutralizing vascular endothelial growth factor (VEGF), inducing tumor vascular regression, normalizing residual vasculature, and inhibiting neovascularization and, therefore, tumor growth. The combination of bevacizumab with platinum-based chemotherapy showed a 4-month overall survival advantage compared to chemotherapy alone (17 months vs. 13 months) and a response rate of 48% vs. 36% ( 16 ).

In 2021, the KEYNOTE-826 study approved the first-line immunotherapy for palliative treatment in CC. Pembrolizumab, a monoclonal antibody, binds to the PD-1 receptor, preventing its interaction with PD-L1 and PD-L2. This interference enhances the anti-tumor immune response of T cells. This phase 3, double-blind clinical trial randomized patients with persistent, recurrent, or metastatic uterine CC to receive pembrolizumab (200 mg) or placebo every 3 weeks for up to 35 cycles plus platinum-based chemotherapy and, at the investigator’s discretion, bevacizumab. In 548 patients exhibiting PD-L1 expression levels of combined positive score (CPS) ≥1%, treatment with pembrolizumab demonstrated a statistically significant improvement in both progression-free survival (PFS) and overall survival (OS) when compared to the placebo group. Specifically, the median PFS for the pembrolizumab group was 10.4 months, exceeding the 8.2 months observed in the placebo group. This difference translated to a hazard ratio (HR) of 0.62 for disease progression or death (95% confidence interval [CI], 0.50 to 0.77; p <0.001). Furthermore, the 24-month OS rate was notably higher in the pembrolizumab group, reaching 53.0% compared to 41.7% in the placebo group (HR for death, 0.64; 95% CI, 0.50 to 0.81; p <0.001). Regarding safety, the most frequently observed grade 3-5 adverse events were anemia (30.3% in the pembrolizumab group and 26.9% in the placebo group) and neutropenia (12.4% and 9.7%, respectively) ( 20 ). In 2023, the results of the study were presented, with a median follow-up of 39.1 months. In the PD-L1 ≥1% population, the administration of pembrolizumab demonstrated a notable improvement in median overall survival compared to the chemotherapy-placebo group. Specifically, the pembrolizumab group achieved a median overall survival of 28.6 months, whereas the control group reached 16.5 months (HR for death: 0.60; 95% CI: 0.49 to 0.74). This survival benefit was further accentuated in the subgroup analysis of patients with PD-L1 expression >10%. In this cohort, the median overall survival for the pembrolizumab arm was 29.6 months versus 17.4 months in the control arm (HR: 0.58; 95% CI: 0.44 to 0.78). Pembrolizumab also exhibited superiority in PFS compared to the control regimen. This was observed in both the PD-L1 CPS ≥1% population (HR: 0.58; p-value <0.0001) and the PD-L1 CPS ≥10% population (HR: 0.52; p-value <0.0001). Regarding adverse events, the incidence of grade 3 or higher events was 82.4% in the pembrolizumab group and 75.4% in the placebo group.

Recently, an exploratory subgroup analysis of this study demonstrated, in those patients with CPS ≥1%, a benefit in OS in favor of the pembrolizumab groups across all subgroups. The median OS was not reached (95% CI, 24.4-NR) in the pembrolizumab group, compared to 25.0 months (95% CI, 16.3-NR) in the placebo group among those who received bevacizumab (HR, 0.62; 95% CI, 0.45-0.87), and 17.1 months (95% CI, 14.9-20.0) in the pembrolizumab group versus 11.9 months (95% CI, 9.7-14.5) in the placebo group (HR, 0.67; 95% CI, 0.47-0.96). Regarding PFS, the HR for progression or death was significantly lower in the pembrolizumab groups compared to the placebo groups in both bevacizumab [HR of 0.61 (95% CI, 0.46-0.8)] and non-bevacizumab [HR of 0.66 (95% CI, 0.47-0.92)] subgroups. As for the use of platinum, it was shown that the median OS was 24.4 months (95% CI, 18.7-NR) in the pembrolizumab group versus 15.7 months (95% CI, 13.2-18.6) in the placebo group among those who received carboplatin (HR, 0.65; 95% CI, 0.50-0.85), and was not reached (95% CI, 22.3-NR) in the pembrolizumab group versus 24.7 months (95% CI, 16.0-NR) in the placebo group among those who received cisplatin (HR, 0.53; 95% CI, 0.27-10.04), while the PFS was 0.68 (95% CI, 0.53-0.85) in the carboplatin subgroup and 0.39 (95% CI, 0.22-0.68) in the cisplatin subgroup in CPS ≥1% ( 21 ).

Second-line treatment options

Following initial treatment, disease progression historically presented significant challenges due to the scarcity of effective therapeutic interventions. For an extended period, a standardized second-line chemotherapy regimen remained elusive. Commonly employed chemotherapeutic agents, including taxanes, topotecan, and gemcitabine, with response rates of 13.2%, with a median PFS of 3.2 months, and a median OS of 9.3 months ( 22 ).

Until 2018, there were no promising treatments in the palliative second-line setting for patients with CC. The KEYNOTE-158 study in 2018 showed promising results with pembrolizumab. In this phase 3, double-blind study, participants were administered pembrolizumab at a dose of 200 mg every three weeks for two years, or until disease progression, unacceptable toxicity, or withdrawal by either the patient or physician. A total of 98 patients were treated, with 83.7% having PD-L1 positivity. After a median follow-up duration of 10.2 months (range: 0.6 - 22.7 months), the observed objective response rate (ORR) was 12.2% (95% confidence interval [CI]: 6.5% - 20.4%). This included three complete responses and nine partial responses, all occurring within the PD-L1-positive patient subgroup. Consequently, the ORR for PD-L1-positive patients was 14.6% (95% CI: 7.8% - 24.2%). Notably, 14.3% (95% CI: 7.4% - 24.1%) of responders within this subgroup had previously received one or more lines of chemotherapy in the recurrent or metastatic setting. Treatment-related adverse events were observed in 65.3% of the study population. The most frequently reported adverse events included hypothyroidism (10.2%), decreased appetite (9.2%), and fatigue (9.2%). Grade 3-4 treatment-related adverse events were documented in 12.2% of patients ( 23 ).

Tisotumab vedotin is a monoclonal antibody attached to a chemotherapy agent called monomethyl auristatin E (MMAE). The innovaTV 204/GOG-3023/ENGOT-cx6 study, a phase II, multicenter, open-label, single-arm study conducted in 35 centers in Europe and the United States, included 102 patients with recurrent or metastatic CC. The study enrolled patients with cervical cancer who experienced disease progression during or after bevacizumab-based chemotherapy and had undergone no more than two prior systemic treatment regimens. Participants received tisotumab vedotin at a dose of 2.0 mg/kg (maximum 200 mg) intravenously every 3 weeks until disease progression or the onset of intolerable adverse effects. The analysis included 101 patients who received at least one dose of the drug, with a median follow-up duration of 10.0 months (range: 6.1 to 13.0 months). The confirmed ORR was 24% (95% confidence interval [CI]: 16-33%), with 7% complete responses and 17% partial responses. The most frequently observed treatment-related adverse events (TRAEs) were alopecia (38%), epistaxis (30%), nausea (27%), conjunctivitis (26%), fatigue (26%), and dry eye (23%). Grade ≥3 TRAEs occurred in 28% of patients, notably including neutropenia (3%), fatigue (2%), ulcerative keratitis (2%), and various peripheral neuropathies (sensory, motor, sensorimotor, and general peripheral neuropathy). Serious TRAEs were reported in 13% of patients, with sensory-motor peripheral neuropathy (2%) and pyrexia (2%) being the most common ( 24 ).

In 2022, another treatment showed promising results in this patient group. The EMPOWER-Cervical 1 study, a phase 3 trial, in patients who had disease progression after first-line platinum-based chemotherapy, regardless of their PD-L1 status. Cemiplimab, a monoclonal antibody, similarly to pembrolizumab, targets PD-1, preventing T-cell inactivation, and enhancing T-cell mediated immune responses against tumors. This randomized controlled trial investigated the efficacy and safety of cemiplimab versus the investigator’s choice chemotherapy in women with advanced cervical cancer. A total of 608 patients were equally randomized to receive either cemiplimab (350mg every 3 weeks) or chemotherapy. The cemiplimab group demonstrated a significant improvement in median OS compared to the chemotherapy group (12.0 months vs. 8.5 months, respectively). This survival benefit was reflected in a HR for death of 0.69 (95% CI, 0.56 to 0.84; p < 0.001) favoring cemiplimab. Notably, this survival advantage remained consistent across both squamous cell carcinoma and adenocarcinoma (including adenosquamous carcinoma) histological subgroups. PFS was also significantly longer in the cemiplimab group compared to the chemotherapy group, as evidenced by a HR for disease progression or death of 0.75 (95% CI, 0.63 to 0.89; p < 0.001). The ORR was notably higher in the cemiplimab group (16.4%; 95% CI, 12.5 to 21.1) compared to the chemotherapy group (6.3%; 95% CI, 3.8 to 9.6). Interestingly, within the cemiplimab group, the response rate was 18% (95% CI, 11 to 28) for patients with PD-L1 expression ≥1% and 11% (95% CI, 4 to 25) for those with PD-L1 expression <1%. Grade ≥3 adverse events were observed in 45.0% of patients in the cemiplimab group and 53.4% of patients in the chemotherapy group. This study suggests that cemiplimab provides a significant improvement in OS and PFS compared to chemotherapy in women with advanced CC. The observed benefit was consistent across histological subgroups and PD-L1 expression levels. While adverse events were noted in both groups, the incidence of grade ≥3 events was numerically lower in the cemiplimab group ( 25 ).

Table 1 summarizes clinical trials investigating the efficacy and safety of immunotherapy for the treatment of metastatic, recurrent, or persistent cervical cancer.

Table 1 Advanced, recurrent, and persistent cervical cancer targeted therapies.

Currently, there are several phase I-II clinical trials evaluating the use of immunotherapy as second-line treatment for recurrent and persistent metastatic cervical cancer, with promising outcomes expected for this patient group ( Table 2 ). Furthermore, the related mechanisms of combined immunotherapy with other treatments such as chemotherapy or targeted therapies, as well as combinations of immunotherapies, are being assessed in different clinical studies and are anticipated to alter current treatment guidelines in the future.

Table 2 Therapies under investigation for the treatment of advanced, persistent, and recurrent cervical cancer.

Conclusions

Cervical cancer patients facing metastatic, persistent, or recurrent disease experience a dismal prognosis, with a 5-year survival rate below 20%. This underscores the critical need for novel therapeutic interventions to improve outcomes for this patient population. Recent advancements in understanding the mechanisms of immunosuppression within the tumor microenvironment have paved the way for the development of innovative immunotherapeutic strategies. These approaches aim to counteract immunosuppressive pathways and bolster effector immune cell function, leading to promising improvements in both progression-free survival (PFS) and overall survival (OS), particularly in the first-line treatment setting.

Recommendations

● The use of pembrolizumab with platinum + paclitaxel ± bevacizumab is recommended for patients with metastatic, recurrent, or persistent CC with PD-L1 CPS ≥1%, of squamous, adenocarcinoma, or adenosquamous histology, as first-line treatment. Quality of evidence (GRADE: High) Level of recommendation IA.

● The use of platinum + paclitaxel ± bevacizumab is recommended for patients with metastatic, recurrent, or persistent CC with PD-L1 CPS <1%, of squamous, adenocarcinoma, or adenosquamous histology, as first-line treatment. Quality of evidence (GRADE: High) Level of recommendation IA.

● For patients not eligible for combination antiangiogenic or immunotherapy plus chemotherapy, chemotherapy following international guidelines is recommended. Quality of evidence (GRADE: Moderate) Level of recommendation IIB.

● The use of pembrolizumab as monotherapy is recommended in patients with metastatic, recurrent, or persistent CC, of squamous, adenocarcinoma, or adenosquamous histology, with progression on at least one line of platinum-based chemotherapy (PD-L1 CPS ≥1%, MSI-H, dMMR, TMB-H). Quality of evidence (GRADE: Moderate) Level of recommendation IIB.

● The use of tisotumab/vedontin is recommended in patients with metastatic, recurrent, or persistent CC, of squamous, adenocarcinoma, or adenosquamous histology, with progression on chemotherapy, as second-line treatment. Quality of evidence (GRADE: Moderate) Level of recommendation IIB.

● The use of cemiplimab monotherapy is recommended in patients with metastatic, recurrent, or persistent CC, of squamous, adenocarcinoma, or adenosquamous histology, with progression on chemotherapy, as second-line treatment. Quality of evidence (GRADE: High) Level of recommendation IB.

● The use of chemotherapy as monotherapy (paclitaxel, docetaxel, gemcitabine, topotecan, vinorelbine) may be an option in second-line treatment for patients with advanced, recurrent, or persistent CC, not eligible for immunotherapy. Quality of evidence (GRADE: Moderate) Level of recommendation IIB.

Author contributions

TG-C: Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing. EA-B: Investigation, Resources, Writing – original draft, Writing – review & editing. JC-M: Investigation, Writing – original draft, Writing – review & editing. LC-P: Project administration, Writing – original draft, Writing – review & editing. EV-C: Writing – original draft, Writing – review & editing. RV-V: Writing – original draft, Writing – review & editing. JG-P: Data curation, Investigation, Visualization, Writing – original draft, Writing – review & editing. PC-E: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The authors have secured a sponsor, Laboratorios PiSA S.A. de C.V. (PiSA), to cover the publication fee. Please note that PiSA's involvement is limited to the payment of this fee; the research and authorship were entirely conducted by the authors.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: cervical cancer, metastatic cervical cancer, immunotherapy, recurrent and persistent disease, checkpoint inhibitors

Citation: Galicia-Carmona T, Arango-Bravo EA, Coronel-Martínez JA, Cetina-Pérez L, Vanoye-Carlo EG, Villalobos-Valencia R, García-Pacheco JA and Cortés-Esteban P (2024) Advanced, recurrent, and persistent cervical cancer management: in the era of immunotherapy. Front. Oncol. 14:1392639. doi: 10.3389/fonc.2024.1392639

Received: 27 February 2024; Accepted: 02 July 2024; Published: 05 August 2024.

Reviewed by:

Copyright © 2024 Galicia-Carmona, Arango-Bravo, Coronel-Martínez, Cetina-Pérez, Vanoye-Carlo, Villalobos-Valencia, García-Pacheco and Cortés-Esteban. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Patricia Cortés-Esteban, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Tertiary lymphoid structures are associated with enhanced macrophage activation, immune checkpoint expression and predict outcome in cervical cancer.

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Cervical tumors are usually treated using surgery, chemotherapy, and radiotherapy, and would benefit from immunotherapies. However, the immune microenvironment in cervical cancer remains poorly described. Tertiary lymphoid structures (TLS) were recently described as markers for better immunotherapy response and overall better prognosis in cancer patients. We integratedly evaluated the cervical tumor immune microenvironment, and specifically TLS importance, using combined high-throughput phenotyping, soluble factor dosage, spatial interaction analyses, and statistical analyses. We demonstrate that TLS presence is associated with a more inflammatory soluble microenvironment, with the presence of B cells as well as more activated macrophages and dendritic cells (DCs). Furthermore, this myeloid cell activation is associated with expression of immune checkpoints, such as PD-L1 and CD40, and close proximity of activated conventional DC2 to CD8+ T cells, therefore indicating better immune interactions and tumor control. Finally, we associate TLS presence, greater B cell density, and activated DC density to improved progression-free survival, and present it as an additional prognostic marker. To conclude, our results provide an exhaustive depiction of the cervical tumor immune microenvironment where TLS presence marks cell activation and immunotherapy target expression. These findings provide predictive clues for patient response to targeted immunotherapies.

Competing Interest Statement

D.O. is a cofounder and shareholder of Imcheck Therapeutics, Alderaan Biotechnology, Emergence Therapeutics, and Stealth IO, which did not take part in any part of the manuscript. Other authors declare no conflict of interest. RS declares to have received research grants from Astra-Zeneca, consulting fees from GSK and EISAI, and non-financial support from MSD, Astra-Zeneca, GSK, and Novartis, none of which participated to this study. EL declares to have received consulting fees from GSK and MSD, none of which participated to this study.

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PMC10217496

Cervical Cancer Detection Techniques: A Chronological Review

Wan azani mustafa.

1 Faculty of Electrical Engineering Technology, Campus Pauh Putra, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia

2 Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia

Shahrina Ismail

3 Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Bandar Baru Nilai 71800, Negeri Sembilan, Malaysia

Fahirah Syaliza Mokhtar

4 Faculty of Business, Economy and Social Development, Universiti Malaysia Terengganu, Kuala Nerus 21300, Terengganu, Malaysia

Hiam Alquran

5 Department of Biomedical Systems and Informatics Engineering, Yarmouk University, 556, Irbid 21163, Jordan

Yazan Al-Issa

6 Department of Computer Engineering, Yarmouk University, Irbid 22110, Jordan

Associated Data

Not applicable.

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.

1. Introduction

In 2020, cervical cancer recorded 604,127 new cases and death in 341,831 cases, according to the Global Cancer Observatory (GCO) [ 1 ]. In Malaysia, cervical cancer is the fourth most common cancer among women, accounting for around 1740 newly diagnosed cases and 991 yearly fatalities in 2020 [ 2 ]. Every year, between 2000 and 3000 cases of cervical cancer are hospitalized in Malaysia, according to the Ministry of Health (MoH). The majority of these cases come late in the course of the disease. Malaysia’s mortality rate from cervical cancer is more than twice as high as that of the United Kingdom, the Netherlands, and Finland. The mortality rate has not decreased despite the implementation of screening programs and immunization campaigns against cervical cancer. The economic burden of cervical cancer is significant. In Malaysia, managing cervical cancer (from prevention to handling invasive diseases) costs around RM 312 million (USD 76 million). The majority of this (67%) goes towards treating aggressive cancer patients [ 3 ]. Pap smear screening is employed for early cervical cancer detection. The most crucial step is analyzing the Pap smear slide, and the identification of any condition or disease is crucial in order to administer the appropriate treatment [ 4 , 5 ]. Additionally, the Pap smear diagnostic reaction to a medication or treatment must be viewed or measured for clinical research. Clinically, microscope images are frequently utilized to diagnose Pap smear results. The sample images in the traditional approach, which involves taking a sample image under a microscope, run the risk of blurring effects, noise, shadows, lighting issues, as well as artifact issues on the images of thin smears [ 6 , 7 ]. Images from a Pap smear may have noise or other artifacts. Images from Pap smears may have poorer quality owing to noise or low contrast. Since the diagnosis relies on an individual, there are risks associated with the conventional procedure that might result in incorrect findings. A woman’s cervix is where cervical cancer first develops. The female reproductive system is depicted in Figure 1 [ 8 ]. It happens as a result of abnormal cervix cell growth [ 9 ]. The cervix and tissues nearby, as well as organs consisting of the liver or lungs, will be invaded by this. Human papillomavirus (HPV) infection is linked to an increased risk of generating abnormal cells. Abnormal menstruation, irregular menstruation, heavy menstruation, weight loss, pelvic pain, and vaginal discomfort are the initial indications of cervical cancer.

An external file that holds a picture, illustration, etc.
Object name is diagnostics-13-01763-g001.jpg

Female reproductive system [ 10 ].

Cervical cancer is caused by a group of viruses called HPV. Having sexual activity with another person may transmit HPV. There is evidence that HPV plays a role in the occurrence of penis, vagina, vulva, and anus cancers. There are more than 100 types of HPV, and HPV types 16 and 18 account for approximately 70% of all cervical cancer cases globally [ 11 ]. All women ranging in age from 25 to 74 are invited to screening tests. There are various methods to screen the cervical lining using a colposcopy, which is used to magnify the area that the doctor wants to check after inserting the speculum into the vagina to check both the vagina and the cervix [ 12 ].

Early detection of cervical cancer is crucial since late diagnosis reduces the chance of survival in the entire world’s female population [ 13 ]. According to Logeswaran (2020), 90% of women with cervical cancer diagnoses in low- and middle-income countries such as India may die unexpectedly as a consequence of inadequate detection, early diagnosis, effective screening, and treatment [ 14 ]. J. Lu et al. (2020) conducted a similar study and discovered that early screening is the most successful strategy for reducing the worldwide cervical cancer burden. Nonetheless, because of a lack of information, limited access to medical facilities, and prohibitively costly processes in developing countries, vulnerable patient populations are unable to bear routine examinations [ 15 ].

It may be diagnosed using a variety of screening tests, but the Papanicolaou smear test, which employs cell cytology, is the most common. It is a reliable method for detecting cervical cancer, although there is always a possibility of misinterpretation owing to human observational mistakes [ 16 , 17 ]. According to a study conducted in the medical field by Jaya and Latha (2019), image processing plays a crucial role in making the correct choice by utilizing a variety of techniques and algorithms. However, it is difficult to detect Pap smear images through microscopes. Traditional cervical cell screening also relies heavily on the pathologists’ experience, which has the disadvantages of poor efficiency as well as low accuracy. Cervical cancer cells do not differ much in texture or color from normal cells, making their detection with smear tests very difficult [ 18 ].

However, cone biopsy screening is used when an abnormal cell is suspected in the cervix in order to detect it early. The most common screen test, as well as the Pap smear, also called the Papanicolaou test, is based mainly on using a brush to remove a small part of the lining tissue and checking it under microscopic levels to see if there are changes in the cell. This type of test can be used to discover if there is an infection or inflammation in the cervix or the presence of the HPV virus. The resultant images that have been obtained are called Pap smear images, which form a huge factor in early cervical cancer detection as well as classification. The new method for screening is based on the detection of HPV absence or presence [ 19 ]. Much research is carried out on the detection and classification of this type of cancer utilizing nanotechnology and building a biosensor to detect HPV, as well as using Pap smear images to detect and classify abnormal cells utilizing the benefits of deep and machine learning (ML) techniques. Other research focused on electrical impedance matching of affected signals with a 3D finite element model for cancer and non-cancerous cells. Cervical cancer affects the female reproductive system and is strongly associated with HPV infection, obesity, smoking, and sexually transmitted diseases (STDs). Manual Pap tests (Papanicolaou tests) are widely used for the early detection of cancer, but they are costly, stagnant, and highly dependent on the pathologist’s expertise. Several computer aided diagnostic (CAD) systems were developed to automatically detect cervical cancer. Developing automatic prediction models to identify vulnerable patients can improve the efficacy of screening programs and eliminate inconsistencies and subjectivity resulting from cytopathologists’ lack of expertise.

2. Materials and Methods

The primary goal of this study is to explore and understand the methodology of cervical cancer detection around the world between 1996 and 2022. The purpose of the current narrative analysis is to respond to the primary research question: (1) What types of cervical cancer detection have been proposed around the world? (2) How effective were computer-aided diagnostics for the Pap smear screening process? Contrary to that, cervical cancer detection has evolved significantly over the years, with several different techniques now available. The Pap smear test remains the most frequently employed method. Still, newer techniques such as visual inspection with acetic acid (VIA) and HPV testing, as well as Lugol’s iodine, are becoming more widely used. Early detection is the key to successful treatment and improved outcomes, and women should undergo regular cervical cancer screening according to recommended guidelines. In addition, this part discusses the requirement for a comprehensive evaluation of the cervical cancer situation. The outline of this review paper consists of three sections: Section 1 discusses an introduction and related research, and Section 2 describes the review data. The conclusions of this research are discussed in Section 3 .

A method for obtaining the literature is shown in Table 1 . The systematic review approach comprises three primary phases that were employed to determine the many relevant publications for this study. The initial phase is keyword recognition and the search for connected, related phrases utilizing the encyclopedia, dictionaries, and thesaurus, as well as prior research. Therefore, search strings were developed for the Scopus database once all pertinent terms were chosen. Considering literature (research papers) is the main source of pertinent information, it was the initial criterion. It also covers the exclusion of conference proceedings, chapters, books, book series, meta-synthesis, meta-analysis, reviews, and systematic reviews from the present research. Additionally, the review was limited to English-language studies only. A total of 108 publications were chosen in accordance with particular parameters.

The specification for primary data searching.

Keyword	Cervix, Cervical, Cancer, Tumor, Detect, Diagnosis
Inclusion	Article, Journal, English, computer science, and engineering
Exclusion	Pure medicine, review article, other languages
Final Search String (Scopus)	TITLE ((cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)) AND (LIMIT-TO (PUBSTAGE, “final”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “COMP”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”))
Number of Primary Article	108

Figure 2 represents the number of documents about cervical cancer per year. Obviously, interest in this topic started in 1996 with only one paper, and no production from 1997 to 2001 appeared in other documents. The settlement of ignorance was shown from 2002 to 2007, and two documents appeared in 2008. The steady increasing pattern appeared from 2009 to 2011. The sharp growth appeared from 2009 to 2015. In 2015–2018, there were swings between increasing and decreasing, but the average number was around eight documents per year. The total number sharply increased from 2018 to 2022 to be the mean of around 15 documents per year as well. That reflects people’s consideration of the danger of cervical cancer as well as the significance of research to build a solid understanding of the nature of the disease and the tools to overcome or reduce its impacts on women.

An external file that holds a picture, illustration, etc.
Object name is diagnostics-13-01763-g002.jpg

Number of documents per year.

3. Review of the Study

3.1. 1996–2015.

Many studies have been conducted in the past to investigate cervical cancer diagnosis. Worldwide research is being conducted by doctors to better understand cervical cancer, how to prevent it, how to cure it, and how to provide treatment for those who have been diagnosed with the disease. For example, in 1996, an innovative method for the creation of segmentation and diagnostic algorithms for biomedical image analysis was given by [ 20 ]. In this case, a prototype expert system was created to give gynecologists a reliable and objective tool. Moreover, a collection of knowledge sources was created using specialized image-analysis methods. The robust control method employed by the expert system reduces the need for domain-specific control knowledge and has been shown to efficiently identify cervical cancer. The composition of segmentation and diagnostic methods for biomedical image analysis was also discussed in this paper, employing a new technique.

After many years, cervical cancer diagnosis evolved due to technological development. Following that, in 2001 [ 21 ], it was stated that the principal component analysis (PCA) in the wavelet domain delivers robust novel features with regard to the non-invasive detection of cervical intraepithelial neoplasia (CIN) employing fluorescence imaging spectroscopy. The term “principal wavelet components” (PWCs) refers to these characteristics. Average accurate classification rates for five cervical tissue classes—low-grade dysplasia (CIN 1), squamous, columnar, and metaplasia—as well as a fifth class for other unidentified tissue types, blood, and mucus—were 95% when PWC characteristics were employed as inputs to a 5-class NN. Apart from these [ 22 ], we presented a new technique to determine cervical cancer employing microwaves to measure the dielectric properties of the smear at microwave frequencies. This measuring approach is easy, and the smear collection is non-surgical and painless. The findings propose another option to the Papanikolaou or Papanicolaou tests and demonstrate a new technique for detecting cervical cancer using microwave measurement that may offer a less invasive alternative to these surgical procedures for detecting the disease.

On the other hand, in vivo, cervical dysplasia and cancer detection utilizing model-based analysis of reflectance and fluorescence spectra have been proven [ 23 ]. Here, a theory-based diffusion model is employed along with two analytical methods for calculating reflectance spectra that are contrasted with Monte Carlo simulations. A diagnostic algorithm is also created and tested utilizing cross-validation based on these obtained parameters. This algorithm’s sensitivity/specificity for each measurement in comparison to the gold standard of histopathology are 85/51%. The accuracy described in previous research using optical technology to identify cervical cancer and its precursors corresponds to this.

Meanwhile, in [ 24 ], a quantitative colposcopic imaging system for early cervical cancer diagnosis is assessed in a clinical study. The cervix of living human beings is employed to assess the kinetics of the acetowhitening process in order to obtain diagnostic information. The imaging method relies on 3D active stereo vision as well as motion tracking. It was possible to distinguish between normal tissue and HPV-infected tissue, as well as low-grade and high-grade CIN lesions, utilizing a diagnostic algorithm with 91% SE and 90% SP. The findings show that the quantitative colposcopic imaging system may be able to deliver unbiased screening and diagnostic information for the early detection of cervical cancer.

Additionally, [ 25 ] immobilized anti-HPV18 and E. coli O157: H7 antibodies on magnetic silica-coated Fe 3 O 4 for early diagnosis of cervical cancer as well as diarrhea. Uncoated Fe 3 O 4 nanoparticles having a 9–16 nm average diameter as well as a saturation magnetization of around 66 emu/g were first prepared using the co-precipitation method. The findings revealed that magnetic SiO 2 -coated Fe 3 O 4 nanoparticles could be an auspicious contender for diagnosing cervical cancer at an early stage, specifically with high accuracy.

In 2011, [ 26 ] employed an optoelectronic method to detect CIN as well as cervical cancer. The pNOR number and the sensitivity/specificity of the optoelectronic approach were shown by the authors to be correlated. The specificity of the optoelectronic approach was calculated to be 65.70% for LGSIL and 90.38% for HGSIL and cervix squamous cell carcinoma. The optoelectronic technique utilized to validate the absence of cervical pathology was assessed to have a 78.89% specificity. Here, CIN, which exists in the squamous epithelium as well as squamous cell carcinoma of the cervix, is easily detected using the optoelectronic approach.

In the same year, [ 27 ] investigated the hWAPL histological expression value assessment in the cytological as well as histological diagnosis with regard to cervical intraepithelial neoplasia and cervical cancer. The expression intensity of hWAPL protein in the HSIL group, LSIL group, ASCUS group, and ASC-H group was obviously greater than that in the NILM group ( p < 0.05), and the expression intensity in the ASCUS group and ASC-H group was higher than that in the LSIL group ( p < 0.05). Furthermore, in the ASCUS and ASC-H groups, the frequency of SCC + CIN III was above 50%. Therefore, hWAPL may be a promising candidate for diagnosing low-grade CIN. Furthermore, the histological expression of hWAPL is consistent with the cervical lesions’ cytological type.

A year later, in 2012, in order to enhance cervical cancer risk classification, [ 28 ] investigated the automated detection of dual p16/Ki67 nuclear immunoreactivity in liquid-based Pap tests. Algorithms were created to digitize and examine smears stained with p16 as well as Ki67 antibodies. The nuclear mask was produced employing a gradient-based radial symmetry operator along with adaptive symmetry image processing. This was subsequently followed by the extraction of attributes from each nucleus, such as pixel data as well as immunoreactivity signatures. The quantitative analysis of immunoreactivity offered by the further emphasis on classified nuclei, according to the authors, may have a positive influence on the effectiveness and screening results of the Pap test.

In the same year, which is 2012 [ 29 ], a new technique was proposed to construct a tumor probability map while gradually determining the boundaries of an organ of interest on the basis of the accomplished nonrigid transformation. The technique dealt with the difficulties of considerable tumor regression and its impact on nearby tissues. Findings indicate that the suggested technique greatly surpasses the current registration algorithms and reaches a precision equivalent to manual segmentation. Additionally, there is excellent agreement between the suggested method’s tumor detection results and manual delineation by an experienced doctor.

Moreover, in [ 30 ], blood and urine samples from cervical cancer patients were collected, and their fluorescence emission spectra (FES) as well as Stokes shift spectra (SSS) were contrasted to those of normal controls. Both spectra demonstrated that in cervical cancer patients, the relative levels of biomolecules, which include flavin, nicotinamide, adenine dinucleotide, collagen, and porphyrin, were out of balance. The author also stated that this is the first study on FES and SSS of blood and urine samples from patients with cervical cancer that provides a sensitivity of 80% as well as a specificity of 78%.

A total of 2 years later, in 2014 [ 31 ], it was proposed to use time-resolved blood component spectra to identify cervical cancer. Porphyrin served as the biomarker indicative of cancer in this instance, with samples from cancer patients having fluorescence decay times that are 60% greater than those from normal controls. A randomized set of samples from cancer patients and controls ( n = 27 in total) could be categorized with sensitivity (92%) and specificity (86%) using these parameters.

Utilizing reduced graphene oxide–tetraethylene pentamine as electrode materials and distinct redox probes as labels [ 32 ], this suggested simultaneous electrochemical detection of cervical cancer indicators in the same year. In accordance with the peak current change of neutral red and thionine prior to and following the antigen-antibody reaction, the immunosensor was constructed with a sandwich structure. According to the findings, the immunosensor exhibited a broad linear range, a small detection limit, high reproducibility, and stability. Furthermore, the technique has been employed successfully to examine serum samples.

Moreover, [ 33 ] utilized extracted intrinsic fluorescence as well as PCA to identify the advancement of cervical cancer. Here, along with the intrinsic fluorescence, the effectiveness of PCA in separating the aggregate behavior from smaller associated clusters in a dimensionally diminished space is tested. By closely observing the sectorial behavior of the dominant eigenvectors of PCA, it is possible to determine the various activities of the dominant fluorophores, flavins, nicotinamide adenine dinucleotide, collagen, and porphyrin of various classes of precancers. The Mahalanobis distance was also computed utilizing the scores of the chosen major components in order to better categorize the various grades.

A year later, [ 34 ] presented a method for colposcopic images-based automated cervical cancer diagnosis. Here, abnormal and normal tissue are distinguished using wavelet and statistically based attributes. The wavelet-decomposed image is employed to obtain the wavelet energies. The feature vector produced from the combination of these features is then applied to the detection. The segmented cancer region demonstrates that the suggested fusion technique is capable of identifying the cancer-affected region with more accuracy over the wavelet, along with statistical features-based approaches.

In addition, a hybrid classifier-based computer-aided detection (CAD) of cervical cancer utilizing Pap smear images was also suggested by [ 35 ] in 2015. It is utilized to divide the cell image from the test Pap smear into normal and dysplastic cell images. Following that, morphological techniques are employed to identify and segment the abnormal cell region. On images from databases with free access to the public, the suggested technique is evaluated. A unique illumination correction and intensity normalization approach on cervigrams was put out by [ 36 ] in the same year in order to aid in the early detection of uterine cervical cancer. In light of our study’s results, we draw the conclusion that the peak of the squamous epithelium (SE) region’s intensity distribution and the peak of the entire cervix region are significantly associated.

Furthermore, by using the nested structure of its data to extract patient-level features from the cell-level data, utilizing a statistical model that takes advantage of the hierarchical data structure, and classifying the cellular level [ 37 ], it executed comparative research on three primary methods for solving problems. With an estimated 61% sensitivity and 89% specificity on independent data, the optimal method was to classify at the cellular level and count the number of cells with a posterior probability larger than a threshold value. In addition, recent advancements in statistical learning make it feasible to reach great accuracy. Apart from that, new clinical studies that support the use of HPV E6/E7 mRNA as a marker in advanced cervical cancer screening programs were reported in 2015 [ 38 ]. The authors give a general review of the research study sample size, age, recruitment setting, HPV mRNA, and HPV DNA tests. It was demonstrated by the pooled evaluation of clinical research that HPV mRNA may be a useful diagnostic biomarker. To draw a firm conclusion, however, further research must be conducted.

On the other hand, in the same year, [ 39 ] investigated the degree of squaraine dye aggregation that affects the strength of surface-enhanced Raman signal scattering (SERS) after adsorption on a gold surface that has been nano-roughened. When chemisorbed on spherical gold nanoparticles, the SQ2 (mono lipoic acid appended), SQ5 (conjugated with hexyl and dodecyl side chains), and SQ6 (conjugated with hexyl and dodecyl side chains) squaraine derivatives demonstrated a substantial rise in Raman scattering in the fingerprint region. HeLa cells demonstrated pronounced SERS mapping intensity and selectivity towards the cell surface and nucleus after further conjugating this nanotag with monoclonal antibodies that targeted overexpressed receptors, EGFR and p16/Ki-67, in cervical cancer cells.

Subsequently, [ 40 ] proposes a system for automatically classifying and segmenting cervical cells. Radiating Gradient Vector Flow (RGVF) Snake is employed to separate the cytoplasm, nucleus, and background of a single cervical cell image. For system training, several cellular and nuclear properties are retrieved. Artificial neural networks (ANN) are employed to examine the dataset’s ability to categorize seven distinct cell types and distinguish between abnormal and normal cells. The clinical research on styping identification of HPV infection using microarrays from paraffin-embedded tissues of precursor lesions as well as cervical cancer was also explored by [ 41 ]. This led to the identification of the prevalence and type distribution of HPV in cervical cancer and CIN in Jiangsu, China. The findings indicate that Jiangsu’s (China’s) high rate of HPV 16, 18, 33, 31, and 58 warrants further notice. It has significant repercussions for the effective administration of the HPV vaccination and the selection of testing techniques.

Apart from that, [ 42 ] examined the fractal dimension of AFM images of human cervical epithelial cells at various stages of cancer growth to evaluate the early detection of cervical cancer. Individual human cervical epithelial cells at three phases of cancer progression—normal, immortal (pre-malignant), and carcinoma cells—were examined using the AFM HarmoniX modality by the author. The authors were successful in distinguishing between abnormal and normal cells by utilizing AFM to examine the surface characteristics of human cervical epithelial cells. This technique could supplement current techniques to improve the accuracy of diagnosis.

Moreover, [ 43 ] proposed using nanotechnology and biomarkers for cervical cancer’s early detection and treatment. Nanomaterials are special in their optical, physical, and electrical characteristics, which has made them particularly advantageous for sensing. Cancer biomarkers, which are employed as targets in the detection and monitoring of cancer, are mostly composed of RNA fragments, DNA fragments, antibody fragments, and proteins. In a few decades, it is expected to be feasible to identify cancer at a very early stage, giving a significantly greater probability of treatment.

Subsequently, [ 44 ] describes an ultrasensitive electrochemical immunosensor for accurate detection of p16 and shows how effectively it performs when used with patient cell lysates to detect solubilized p16 protein. Furthermore, the authors also reported that the suggested immunosensor successfully detected raised p16 levels in cervical swab samples taken from 10 patients who had received positive results from a standard Pap smear test, demonstrating that electrochemical immunosensors hold great potential for the early detection of cervical cancer in a clinical setting.

3.2. 2016–2018

Several studies tried to diagnose cervical cancer using various techniques. For instance, in 2015, Yulan Wang et al. recommended the use of fluorescence lifetime imaging microscopy (FLIM) for the early detection of cervical cancer. They discovered that the lifetime of cancerous cells was shorter compared to normal cells. They recommend FLIM as a highly precise and specific method that can detect the occurrence of precancerous as well as cancerous cells quickly [ 45 ].

In 2016, S. Athinarayanan et al. [ 46 ] suggested an automatic multistage cervical cancer diagnostic system using Pap smear images (obtained from the Herlev dataset described in Table 2 ) and machine learning (ML) methods. In the preprocessing stage, images were denoised, intensity and texture features were extracted, and finally, images were differentiated using SVM into normal and abnormal classes. They succeeded in detecting cervical cancer with 94% accuracy. Moreover, Anousouya Devi et al. [ 47 ] developed an image analysis algorithm to replace time-consuming Pap smear screening tests. The authors discussed a variety of segmentation algorithms and feature extraction techniques with regard to the efficient segmentation of Pap smear slides.

Pap smear image classification in the Herlev dataset [ 9 ].

Cell	Class Name	Cell Count	Sub-Total
Normal	Normal Superficial Squamous	74	242
	Normal Intermediate Squamous	70
	Normal Columnar	98
Abnormal	Carcinoma In Situ	150	675
	Light Dysplastic	182
	Moderate Dysplastic	146
	Severe Dysplastic	197
	Total	917	917

Furthermore, Xianfeng Xu et al. [ 48 ] investigated the value of PET/CT scanning in detecting cervical carcinoma in 51 patients. Note that PET/CT diagnosis capability is superior to the classical FIGO discrimination technique. For example, PET/CT detected primary tumors with 84.31% accuracy, 80.77% specificity, and 88% sensitivity. On the other hand, it detected lymph nodes with 76.47% accuracy, 71.43% specificity, and 82.61% sensitivity. Subsequently, Jose Amaya et al. [ 49 ] designed a high-stability voltage current source for the recognition of cervical cancer using electrical bio-impedance spectroscopy. Here, the medical kit they designed was compatible with international standards. Finally, Rizanda Sobar et al. [ 50 ] determined seven behavior features and surveyed 72 respondents (including 22 cancer patients) in Indonesia. They used two machine learning (ML) techniques, particularly logistic regression (LR) and Naïve Bayes, to forecast the risk of becoming a cervical cancer patient. With respect to accuracy, Naïve Bayes outperformed LR (91.67% compared to 87.5%), and with respect to AUC, LR outperformed Naïve Bayes (0.97 compared to 0.96).

One year later, Irvin Sitompul et al. [ 51 ] conducted a descriptive qualitative study using a questionnaire to evaluate the knowledge of aged women in the Cakung health center regarding the early detection and prevention of cervical cancer. They concluded that knowledge of the Human Papilloma Virus (HPV) vaccine is weak. Meanwhile, Branislava Jeftic et al. [ 52 ] presented a cervical cancer detection method relying on optomagnetic imaging spectroscopy (OMIS) and compared the findings utilizing unstained and stained Papanicolaou smears. Using the Naïve Bayes classifier, they separated the samples into four groups: the II Pap group (normal cells), the III Pap group (abnormal cells), and the IV and V Pap group (cancerous cells). Unstained sample classification with Naïve Bayes achieved 96% accuracy, whereas stained sample classification achieved 85.18% accuracy. Apart from these, Abdullah Iliyasu et al. [ 53 ] proposed a quantum hybrid technique that uses quantum particle swarm optimization (QPSO) for selecting 7 out of 17 features, as well as a fuzzy KNN for the classification of cervical cells in smeared images. They used 917 images from the Herlev dataset and achieved 86% recall, 85% precision, and F1 score of 85%. On the other hand, Wen Wu et al. [ 54 ] employed three SVM-based combinations for the diagnosis of cervical cancer. All four target variables were identified, and the performance of SVM was superior to SVM-RFE and SVM-PCA. SVM achieved high precision using all 30 features, but the computation cost was high. The authors showed that the SVM-RFE and SVM-PCA gave comparable performance to the SVM using only 8 features, improving classification time considerably.

In the same year, Katrin Carow et al. [ 55 ] presented evidence that the incorporation of HPV-DNA into the host genome is an initial step in the formation of cervical cancer. They recommend using viral-cellular junction sites as biomarkers when examining circulating tumors. Meanwhile, Vidya Kudva et al. [ 56 ] proposed an image-processing approach that can be used as an image treatment step in any cervix cancer detection system. They presented a cervix region segmentation method and detected specular reflections with high precision, irrespective of lighting conditions and color variations. Apart from these, Guanglu Sun et al. [ 57 ] suggested an ML framework relying on relief feature selection and a Random Forest (RF) classifier to diagnose cervical cancer. They used 917 Pap smear images obtained from the Herlev dataset together with 10-fold cross-validation to perform binary classification. RF outperformed LR, C4.5, and Naïve Bayes classifiers with 94.44% accuracy and 0.9804 AUC using 13 features. In addition, Rubina Shaikh et al. [ 58 ] compared two optical modalities, particularly Raman (RS) and Diffuse Reflectance Spectroscopy (DRS), in differentiating between normal and abnormal cells. One hundred forty-six recorded spectra (67 tumors and 79 normal) were analyzed using a combination of Principal Component and Linear Discriminant Analysis ML techniques. They used Leave One Out Cross Validation (LOOCV) and concluded that DRS is more suited for rural areas, whereas RS is suited for developing countries. Furthermore, Muljo et al. developed an online learning management prototype to educate health workers and the public in Indonesia about early cervical cancer detection as well as treatment [ 59 ].

In 2018, Mithlesh Arya et al. [ 60 ] used SVM as well as ANN to classify single-cell images captured from Pap smear slides into benign and malignant tumors. The accuracy obtained using the suggested texture-based features exceeds that obtained using shape-based features. Additionally, the performance obtained using a combination of features was better than that obtained using a single feature. Using quadratic SVM, they achieved 99.5% accuracy, 99% sensitivity, and 99% specificity. Meanwhile, Ashutosh Sharma et al. [ 61 ] successfully employed fluoranthene-based yellow fluorescent lipid probes with respect to the detection of lipid droplets in cervical cancer tissues. FLUN-550 and FLUN-552 quantitatively detected the excess lipid accumulation and were really useful in the early diagnosis of human cervical cancer. Additionally, Kelwin Fernandes et al. [ 62 ] developed a supervised deep learning (DL) method to diagnose cervical cancer with high accuracy using the medical records of 858 patients. To study the impact of their architecture, they applied their methodology to different datasets and demonstrated that their efficiency is not limited to cervical cancer. They used a loss function for dimensionality reduction, achieving an AUC of 0.6875. Furthermore, Yueyue Jing et al. [ 63 ] established quick, highly sensitive, and highly specific label-free imaging and spectroscopy for the detection of cervical tumors compared to the traditional clinical staining method. They studied unstained tissues extracted from 38 patients and achieved 100% sensitivity and 91% specificity.

In the same year, Rocky Dillak et al. [ 64 ] suggested an early alarm system to diagnose cervical cancer based on a combination of chaos optimization and ridge polynomial neural networks. They achieved an accuracy of 96%, a sensitivity of 95.56%, and a specificity of 96.67%. Apart from these, Vidya Kudva et al. [ 65 ] manually extracted 102 images obtained during visual inspection with acetic acid; 42 images were pathologic, and the remaining 60 were negative. They used a shallow-layer CNN to discriminate between cancer and non-cancer lesions by automatically extracting features from 684 representative patches with 100% accuracy. Following that, Sherif Abdoh et al. [ 66 ] identified 32 risk factors to build a cervical cancer diagnosis framework. They employed two feature reduction techniques, namely Recursive Feature Elimination (RFE) and PCA. Furthermore, they used an RF classifier combined with the Synthetic Minority Oversampling Technique (SMOTE) to correctly classify cervical cancers. The obtained results were validated using 10-fold cross-validation, and SMOTE-RF outperformed SMOTE-RF-RFE and SMOTE-RF-PCA in detecting all 4 cancer groups.

3.3. 2019–2020

As artificial intelligence (AI) and image processing technology advance, we have reviewed progressively intelligent diagnosis tools that are being applied in cervical cancer screening. In this section, we offer a brief review of some methods available in the literature, starting with the year 2019 and progressing to the current cervical screening. Lavanya Devi et al. (2019), for instance, investigate the various automated methods for detecting abnormal cells in Pap images. Cancer screening commonly includes a Pap smear test and an acetic acid test. Cells from the vagina and cervix are extracted and analyzed under a microscope for the occurrence of an abnormal cell in a pap test. An acetic acid test is employed to identify the existence of abnormal cells by comparing the differences in characteristics between samples before and after the application of acetic acid. According to the report, automated screening has become more common than manual screening, given that the latter is inaccurate [ 67 ]. This method of screening has been endorsed in a study conducted by Abdullah et al. (2019), where computer-based algorithms are broadly employed in cervical cancer screening. In this research, a better cellular neural network (CNN) algorithm has been set up as a potential means of detecting cancerous cells in Pap smear images in real-time. For automated detection of cancerous cervix cells, a CNN built-in in MATLAB using templates that segment cell nuclei has been established. The simulation findings demonstrate that our suggested CNN algorithm can automatically identify cervix cancer cells with over 88% accuracy [ 68 ].

Jaya and Latha (2019) introduced a technique for enhancing Pap smear images by comparing Power Law Transformation for Gamma Correction, Histogram Equalization in the Contrast Stretching algorithm, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Shading Correction. To determine the performance of upgraded Pap smear images, the quality measurement NAC, SC, PSNR, and MSE values were determined. As a programming tool, MATLAB R2016a and ANN classification were used to assess the accuracy level of each feature extraction of the algorithm. The study concluded that CLAHE produced a decent result for enhancement, and the SGLDM feature extraction algorithm achieved 93% accuracy while utilizing ANN [ 69 ]. A review of the literature undertaken found that accurate recognition of cervical cancer cells is crucial for clinical diagnosis. A better approach built around the residual neural network is presented to increase the accuracy of diagnosis. However, these current algorithms are only enhanced by the use of low-level manual features. The findings of the experiments demonstrate that the lightweight deep model performs better than the current comparative models and may obtain a model accuracy of 94.1% when applied to the cervical cell data set [ 70 ]. Hence, as recommended by William et al. (2019), it is advantageous to construct a computer-assisted diagnostic tool to increase the accuracy and reliability of the Pap smear test. In this research, Pap smear image analysis was utilized to construct a tool for the automated detection and classification of cervical cancer. Scene segmentation was accomplished using a trainable Weka segmentation classifier, while a sequential elimination strategy was employed for debris rejection. While classification was accomplished utilizing a fuzzy C-means technique, feature selection was accomplished employing simulated annealing combined with a wrapper filter [ 71 ]. The research found that three distinct datasets—single-cell images, multiple-cell images, and Pap smear slide images from a pathology lab—were utilized to evaluate the classifier. For each dataset, overall classification accuracy, sensitivity, and specificity results of “98.88%, 99.28% and 97.47%”, “97.64%, 98.08% and 97.16%”, and “95.00%, 100% and 90.00%”, accordingly, were attained. In comparison to the manual analysis, which takes between 5 and 10 min per slide, the suggested system can analyze a whole Pap smear slide in about 3 min.

Ref. [ 72 ] identified the relevant features in the cancer classification as well as optimized the model. The vital properties in the attribute list were explored using the binary cuckoo search optimization technique. The experimental findings demonstrate the greater performance of the Decision Tree (DT) classifier over all other classifiers, with accuracy increasing from 94.7% to 97% following cuckoo optimization. Another study conducted by Adem et al. (2019) discovered that softmax classification with a stacked autoencoder model, which was implemented for the first time in the cervical cancer dataset, performed better compared to other ML methods with an appropriate 97.8% classification rate. New techniques of diagnosis are described in this article in terms of patient diagnostic support systems, taking into account the interest in ML approaches in cancer research [ 73 ].

In the year 2020, a number of studies offered new screening methods, such as the Shot multiBox detector, which can accurately detect many items of multiple scales at the same time to solve the classic saliency cervical cancer diagnosis approach in ultrasound images. The study provides a new multi-saliency object detection model with an appended deconvolution module embedded within the residual attention module. Experiments demonstrate that the suggested diagnosis method beats comparable algorithms in terms of detection accuracy. It also improves the accuracy of cervical diagnosis by increasing detection performance for multi-saliency cervical cancer objects with small scales [ 74 ]. The Enhanced Johnson’s Algorithm (EJA) was proposed by Ali et al. (2019) as the new shortest path for detecting cervical cancer-associated genes in the Protein-to-Protein Interaction (PPI) network for early cervical cancer diagnosis in their study. EJA was also adopted to find the shortest path between invasive and pre-invasive genes. The Bellman-Ford approach was used in EJA to reconstruct the path with a new iterative matrix, which successfully reduced the elapsed time by omitting the negative cycles in the gene connection [ 75 ]. Huang et al. (2019) discovered that endogenous fluorophores in cells and tissues, such as diminished nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) as well as flavin adenine dinucleotide (FAD), may be imaged by FLIM to illustrate the tissue morphology features, including the biomolecular variations in the microenvironment. It was shown that by monitoring the fluorescence lifetime of NAD(P)H as well as FAD in nearby healthy cervical tissues, benign uterine tumors with abnormal cell development, which include leiomyomas and adenomyosis, may be identified [ 76 ]. According to [ 18 ], cervical cancer is caused by morphological alterations in cells or dead nuclei in the cervix. The detection of abnormalities in cells necessitated a high-level digital image processing technique that included an automated, complete ML skill set. To split the cytoplasm as well as the nucleus from the cell, an innovative fuzzy-based approach has been proposed. KNN is instructed with the color and form attributes of the segmented cell units, and then it is used to classify unknown cervix cell samples. The cytoplasm, as well as the nucleus of the cervix cell, are given shape and color using the proposed technique.

Several other methods have been introduced in detecting this disease, such as automatic feature extraction and classification for acetic acid and Lugol’s iodine cervigrams, as well as (2) methods for merging diagnosis/features of distinct contrasts in cervigrams for enhanced performance, which attained a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively [ 77 ]. A study reported that a novel immunosensor had been formed for quantitative detection with respect to the squamous cell carcinoma antigen (SCCA) in cervical cancer, built on surface-enhanced Raman scattering (SERS). The SCCA monoclonal antibody was combined with polydopamine resin microspheres covered with gold nanoparticles as capture substrates. Phosphate buffer (PBS) had a detection limit of 7.16 pg mL −1 and human peripheral blood had a detection limit of 8.03 pg LH −1 . The findings showed that the SERS immunoassay approach has a possibility for use in early cervical cancer screening and diagnosis [ 78 ]. Fuzzy Swallow Swarm Based Feature Selection (FSSBFS) has been introduced for the optimal selection of cervical cancer features. The proposed ISVM-FssBFS classifier is improved when compared to SVM and Multilayer Perception Classifier (MLP) classifiers. The cervical cancer samples are characterized by 32 risk factors and four target classes: Biopsy, Cytology, Schiller, and Hinselmann [ 79 ].

Early identification of CIN dramatically improved patient survival rates in the year 2020 [ 80 ]. Most cervical cancer detection algorithms rely on natural image object detection technologies, with only minor improvements made to account for the complex application scenario with respect to cervical lesion detection. The suggested method’s sensitivity at four false positives per image as well as average precision are enhanced by 2.79 and 7.2%, respectively, when compared to the baseline (Retinanet) [ 81 ]. Chen et al. (2020) first established the feasibility of using CT imaging and radiomics to create a low-cost image marker for detecting LN metastasis in cervical cancer patients. Here, the model was trained to utilize a leave-one-case-out (LOCO) cross-validation strategy with a total accuracy of 76.4%. Li et al. (2020) proposed a DL framework with regard to the accurate identification of LSIL+ (which includes CIN and cervical cancer) employing time-lapsed colposcopic images. All of the fusion methods that are compared perform better than the automated cervical cancer diagnosis systems that are currently in place and utilize a single time slot. The best fusion strategy was discovered to be a convolutional graph network with edge features (E-GCN). A novel framework built around a strong feature Convolutional Neural Networks (CNN)-Support Vector Machine (SVM) model was presented to properly categorize the cervical cells, according to research by Dongyao Jia et al. (2020). On two distinct datasets, the suggested technique was assessed using the metrics of accuracy (Acc), sensitivity (Sn), and specificity (Sp). The outcomes suggested that the CNN-SVM model with strong features might be utilized to classify cells for early cervical cancer screening [ 82 ].

A potential technique for the diagnosis of cervical cancer with parametrial infiltration is the combination of whole-tumor dynamic contrast-enhanced MRI and texture analysis [ 83 ]. Ktrans, energy, and entropy work more effectively together than separately, particularly when it comes to increasing diagnostic sensitivity. Fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) classification method-based cancer area detection and segmentation in cervical images were suggested by Ramasamy and Chinnasamy in 2020. Fuzzy logic is employed to identify the thick and thin edges, which are then combined using an image fusion approach at the pixel level. The suggested cervical cancer detection system has a classification rate average of 98.8%. In comparison to earlier suggested approaches for cervical cancer estimation, the CCPM result demonstrated more accuracy [ 84 ]. The sensitivity, specificity, and accuracy of the suggested cervical cancer segmentation methods presented in this paper are 98.1%, 99.4%, and 99.3%, respectively. A model for early cervical cancer prediction (CCPM) has been developed by researchers, utilizing risk indicators as inputs. In comparison to earlier suggested approaches for cervical cancer estimation, the CCPM results demonstrated more accuracy. For quick and effective action at the early stages of the disease, a mobile application that may gather information on cervical cancer risk factors and offer CCPM findings has been created [ 85 ].

Apart from that, [ 15 ] adopted a voting method that takes into account the issues with earlier research on cervical cancer. To assess the suggested procedure, several measures are implemented. According to the findings, the voting approach may be used to accurately forecast the chance of having cervical cancer. In comparison to previous techniques, the one that is being presented is more scalable and practical. The key finding by Singh and Goyal (2020) is the choice of the optimal ML algorithm with the maximum accuracy. Several algorithms were able to achieve up to 100%. Although a method such as LR with L1 regularization has a 100% accuracy rate, it consumes too much CPU time [ 16 ].

To effectively recognize the nucleus as well as the cytoplasm boundary of the Pap smear cell as a way to diagnose cervical cancer, an enhanced normalized graph cut with generalized data for enhanced segmentation (INGC-GDES) method was presented. In comparison to earlier methods, the suggested INGC-G DES mechanism leads to a 28% improvement in classification accuracy [ 13 ]. To the best of our knowledge, research has demonstrated the potential of Mueller matrix image processing as a unique strategy for the detection of cancer and precancer [ 86 ]. Sections of the human uterine cervix’s normal and precancerous tissue were utilized in the study. The research explained the creation of a DNA-based electrochemical biosensor that is sensitive and selective for the early detection of HPV-18. As a proprietary, accurate, sensitive, and quick diagnostic approach for HPV 18 in the polymerase chain reaction (PCR) of actual samples, the suggested biosensor can be presented. On a screen-printed carbon electrode (SPCE), a nanocomposite of reduced graphene oxide (rGO) as well as multiwalled carbon nanotubes (MWCNTs) was electrodeposited [ 87 ].

A study conducted by Rehman et al. (2020) reported that an auto-assisted cervical cancer screening system is suggested that utilizes a CNN trained on the Cervical Cells database. The system provides better performance than its previous counterparts under various testing conditions. For the 2-class problem, the classification accuracy of SR, SVM, and GEDT is determined to be 98.8%, 99.5%, and 99.6%, respectively [ 17 ]. Validation of Association Rule Mining using the Test Train Approach (VARMTTA), a data-driven methodology, was put out by Logeswaran et al. (2020). Employing the train-test validation approach lowers the number of rules that are generated from the dataset. This technique makes use of conventional measures, including sensitivity, precision, and total accuracy [ 14 ]. According to Sahoo et al. (2020), using a common path interferometric setup, low-coherence backscattered images of precancerous cervical tissue sections were recorded. These low-coherence images were subjected to a two-dimensional multifractal detrended fluctuation analysis (2D MFDFA) in order to examine the fluctuations in their fractal nature. The RI fluctuations showed long-range relationships, and multifractality was shown to be greater for cervical cancer with higher grades. It was discovered that normal and CIN-I, CIN-I and CIN-II, and normal and CIN-II had specificities and sensitivities of 94%, 88%, 93%, 96%, and 100%, respectively [ 88 ].

3.4. 2021–2022

B. Chitra and S. S. Kumar [ 89 ] reviewed the most recent soft computing techniques for detecting and classifying the most updated algorithms in current research. It is considered a literature review of the most common classification techniques for cervical cancer up to 2021. On top of that, Md. MamunAli et al. [ 90 ] employed clinical data for early cervical cancer detection. They applied a variety of data transformation techniques, such as Z-score, log, and sine functions, in addition to feature selection methods for specifying the most priority features for early detection of cervical cancer. Their results concluded that the logarithmic transformation feature is the best for biopsy data. On the other hand, sine is the best for cytology. However, the combination of sine as well as logarithmic is the best for the Hinselmann dataset, but for the Schiller dataset, the Z-score performance is the best. The classifiers utilized in this study are RF, Random Tree (RT), and instance-based nearest neighbor classifiers. For better performance, B. Chitra and S. S. Kumar [ 91 ] utilized the DL structure DesnNet 121 to classify Pap smear images. They apply various augmentation techniques to the dataset. The DL structure is optimized using the Mutation-based Atom Search Optimization (MASO) algorithm, which is employed to enhance the hyperparameters of DensNet121, for instance, the learning rate, the number of neurons in the dense layer, the number of epochs, patch size, and others. This approach obtains the best accuracy among existing techniques, which reaches 98.3%. Attempting other methods, such as recurrent neural networks, Zhang et al. [ 92 ] discussed the existing screening methods for cervical cancer that are based mainly on separated cells. Therefore, any misclassified cell causes poor accuracy. To overcome these limitations, they proposed a method that combines Long-Short Term Memory (LSTM) with a full CNN as well as fuzzy nonlinear regression. They exploited the time series method for improving cervical screening for cancer. Their procedure was accurate to 98.3%.

Sohely Jahan et al. [ 93 ] proposed an approach that is described in Figure 3 . As it is clear, the raw cervical dataset is cured by outlier removal, cleaning methods, and excluding the records that have missing values. Various feature selection principles are utilized, for instance, Chi-square and RF, to find the most significant features. The selected features are scaled and split into 70:30 to train and test various types of classifiers such as Random Forest (RF), Logistic Regression (LR), Support Vector (SV), Multi-Layer Perceptron (MLP), Decision Tree (DT), Gradient Boosting (GB), K-nearest neighbour (KNN), and AdaBoost (AB) classifiers. MLP performed the best among all with a variety of features. On the other hand, all classifiers have almost the same high performance on 25 selected features.

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Automated invasive cervical cancer disease detection at an early stage via an appropriate ML model.

The research aims to improve accuracy with a reliable system. Therefore, Lei Cao et al. [ 94 ] suggested a more accurate system for detecting cervical cancer. Their method is based on a feature pyramid network to automatically classify cytological images by detecting abnormal cells. Their distinguished model has two features: the first is the reading way of the cervical cytology images, which is the same as pathologists, and the second is detecting abnormal cells at different scales using a multi-scale region-based fusion network. Their designed approach builds on clinical knowledge about abnormal cervical cells based on their shapes and sizes. The performance of their approach is better than the DL approach. Their highest accuracy was 95.8% on the independent dataset. Their process is accurate and quick, and their diagnosis time is 0.04 s per image, which is faster than pathologists’ diagnoses. For dealing with big-size images such as 1000 × 1000 pixels, Antoine Pirovano et al. [ 95 ] proposed the classification under regression constraints. Their experiment enhanced the sensitivity by up to 80% for localizing malignancy in whole slide images. The proposed approach can be integrated with the pathology laboratory system to improve prediction. Figure 4 illustrates their approach.

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Graphical abstract of Antoine Pirovano et al.’s approach [ 95 ].

Some researchers used nanotechnology techniques, where Sakshi Pareek et al. [ 96 ] utilized nanotechnology to design an electrochemical biosensor that is sensitive and accurate for human papillomavirus infection (HPV-16) that causes cervical cancer. The designed biosensor is label-free for DNA. The proposed biosensor exhibits excellent sensitivity and stability. This is the core point in the HPV-16 analysis in medical diagnosis fields. On the other hand, Huiting Zhang et al. [ 97 ] employed Raman spectroscopy of pre-cancerous lesions for early cervical cancer detection. Their method depends on the Raman spectrum signal of the pre-cancerous cell, then utilizes partial least squares (PLS) with the Relife method for feature extraction from the signal. The selected features are passed to KNN and ELM classifiers. The novelty in their work is the feature fusion in the feature extraction phase. The classifier’s performance was enhanced using feature fusion, where KNN accuracy elevated from 88.17% to 93.55% using feature fusion and ELM from 90.81% to 93.51%.

AI is the challenge of many researchers, such as Sukumar Ponnusamy et al. [ 98 ], who combine the artificial neural network and fuzzy system interference (ANFIS) with a watershed algorithm to process, segment, and classify the Pap smear images. They exploited the fuzzy rules to classify abnormal images into their types. Their findings contrast with the existing approach, and it is feasible with high accuracy for classifying malignant cells into their corresponding classes. On top of that, Hongzhen Zhou et al. [ 99 ] analyzed the cervical tumor by automatic feature extraction using a deep belief network in contrast-enhanced ultrasonography images. Their goal postulated the effectiveness of intelligent cervical cancer diagnosis on chemotherapy. Their results are presented in terms of higher sensitivity and accuracy for the diagnosis system. Other researchers focused on the segmentation of affected parts of cervical cells using online machine learning (OLM), which was carried out by Asma Daly et al. [ 100 ], who segmented the cervical cells using the pelvic region in magnetic resonance imaging (MRI). They obtained high accuracy when they compared their results with existing segmentation techniques. Another type of ML is majority voting, which is based on utilizing a single classifier prediction and then an ensemble of them to vote the major, as proposed by Qazi Mudassar Ilyas et al. [ 101 ], who suggested using the ensemble classifier with majority voting of the output. Their ensemble consists of SVM, DT, RF, Naïve Bayes (NB), KNN, LR, J48 DT, and MLP. The best accuracy reached 94% when applied to different benchmark datasets. On the other hand, it utilized other types of classifiers, such as AB, XGBoost, and RF, with the Firefly algorithm as a feature reduction method in addition to SMOTE, which is utilized to deal with imbalance problems in the data. The four diagnostic data sets are exploited (Schiller, Hinselmann, Biopsy, and Cytology). The accuracy is enhanced in terms of reducing the number of selected features [ 102 ]. Due to state-of-the-art DL approaches, Khaled Mabrouk Amer Adweb et al. [ 103 ] discriminate between normal and pre-cancerous cervical cells using Leaky-RELU and PRELU in residual neural networks. The optimum accuracy reached 90.2% in Leaky-RELU and PRELU and 100% in colposcopy cervical images. On the other hand, Anant R. Bhatt et al. [ 104 ] discussed the shortcomings of all existing binary classification methods and conventional neural networks with respect to cervical cancer images. Therefore, they suggested a new approach to extracting features and classifying cervical cancer into multiclasses in a whole slide image (WSI) using ConvNet and a transfer learning strategy. They achieved 99.7% accuracy for multiclass classification in the SIPaKMed dataset. Other research focused on cervical cancer detection employing image processing methods such as Balaji, G. N., et al. [ 105 ], which utilized Boykov–Kolmogorov Graph Cuts as well as Cloud Model-based Synergy Integrated Segmentation algorithms for identifying the boundary for cytoplasm and nuclei in cervical Pap smear images. They approved that their methods enhanced the prognosis of cervical cancer by 14% over the traditional segmentation methods. Other studies employed template matching between the measured electrical impedance spectra of cervical cells and the spectra generated from a 3D model of finite elements for cancerous and non-cancerous cervical cells. The matching between spectra is expressed as a score to determine the high strength between the finite element model and the concourse and non-cancerous cells. This method can be effective for cervical cancer detection [ 106 ]. Some studies focused on the concomitant presence of miRNA-9-5p in cervical cancer, which was detected by RT-PCR. The experiment concluded that MiRNA-9-5p could be used as a biological marker for cervical cancer, which can be profitable in the inhibition track by inhibiting the CXCR4 gene and protein [ 107 ].

Some studies used the Lambert-Beer law to calculate the absorption peak. They found that the absorption is proportional to the cell concentration [ 108 ]. In contrast, other studies worked on both breast and cervical cancer together by employing DL [ 109 ]. Their work focused on utilizing the concepts of type of cancer, breast or cervical, whether it is located internally or externally, in addition to the imaging modality, whether it is mammography, ultrasound cytology, or colposcopy. Their results compared clinical diagnoses with DL. They conclude that DL can be an efficient tool for diagnosing cervical or breast cancer that can be replaced by clinician diagnosis. One Nobel and the most effective study depend on the fluorescence signal of urine samples [ 110 ]. They collected data using urine samples from 1500 patients and compared them with the healthy subjects, which formed control samples. They achieved a high true positive rate, reaching 74%. Their experiments can be conducted with simple requirements, such as fluorescence device analyses. With an amount of 200 μL, this process for diagnosis needs almost 40 min. On the other hand, the detection of affected papillomaviruses using photothermal-triggered multi-signal readout point-of-care testing (POCT). This bioassay method is realized and sensitive in linear ranges 10 −6 ng/mL to 1 ng/mL with detection constraints reaching 1.60 × 10 −6 ng/mL. This method is effective because it is fast, precise, and optimized for POCT. Therefore, it can be used in rural areas for the early detection of malignancy. Table 3 shows the reality of this method when it is compared with the available cervical cancer biomarker detection methods [ 111 ].

Comparison with multiple techniques with regard to cervical cancer biomarker detection.

Detection Methods	Targets	Liner Range	LOD (Limit of Detection)
Magnetic sensor	VCP	25–200 ng/mL	2.5 × 10 ng/mL
Colorimetric assay	HPV	20–2500 nM	1.03 nM
Electrochemical	pGEM-T/E6	40–5000 ng/mL	0.016 ng/mL
Electrochemical	GST-p16	15.6–250 ng/mL	1.3 ng/mL
Swab immunoassay	E6 protein	10 –1 ng/mL	1.60 × 10 ng/mL

Combining texture features of the nucleus and cytoplasm in Pap smear images is a prominent tool to diagnose cervical cells. This method comes from the reality that doctors diagnose cervical cancer based mainly on the structure as well as the size of the cervical cells. Therefore, the Pap smear images in the Herlev dataset are segmented, and then the texture features are extracted to pass through a multilayer feed-forward neural network. The optimum results show high performance compared with the existing method [ 112 ]. On the other side, some studies employed DL and endomicroscopic images to diagnose CIN grade 2. The segmented nucleus is exploited to obtain relevant information for diagnosis. The dataset consisted of 1600 patients, and 20% were used for validation and testing. This approach results in sensitivity reaching 94% and specificity reaching 58%. Therefore, HPV infection test results are considered added features. The sensitivity remains at 94%, and the specificity is enhanced to 71% [ 113 ]. Apart from that, Dongyao Jia et al. [ 114 ] employed the YOLO (You Only Look Once) algorithm to detect abnormal cervical cells to guarantee the accuracy and rapidity of the model. This novel method forms a milestone for future work in automatic cervical cancer diagnosis.

Among the most prominent studies employed dual-tree complex wavelet transform (DTCWT) with a DL approach to classify Pap smear images into four categories: carcinoma in situ, normal, dysplastic, and superficial. The database is augmented for DL requirements using shearing and flipping transformations. The pixel conductivity of the augmented images is manipulated using multimodal (DTCWT). The CNN that has been used in their experiment is ResNet18, and they obtained a high accuracy of about 99% [ 115 ]. On the contrary, Chenjie Li et al. [ 116 ] assessed the effectiveness of 3D ultrasound imaging (TUI) on the local staging diagnosis of cervical cancer. Their suggestion is compared with existing methods such as pelvic examination and MRI. Their experiment was conducted on 35 cervical cancer patients, and the back-propagation algorithm was exploited to segment the images. Their results conclude that there is a high correlation between tumor size in MRI and THI, reaching 0.842, and that the correlation between MR and clinical examination reaches 0.654. This reveals high consistency between MR and THI and can be used for evaluating the local staging for cervical cancer.

For the combination of image processing and AI, most recent studies, such as AbuKhalil, T., et al. [ 117 ], enhanced Pap smear images using median filters and then segmented them using Outs thresholding techniques. The deep descriptors are extracted using ResNet and Inception modules. The resultant descriptors are passed to the recurrent neural network (RNN) to classify Pap smear images as cancerous or non-cancerous. In another study, Mohamed Ibrahim Waly et al. [ 118 ] used the Harvel data set to classify Pap smear images after applying preprocessing techniques such as a Gaussian filter to remove noise. Then identify the illness portion by segmenting the cell with the Tsallis entropy method with dragonfly optimization (TE-DFO). The segmented region is passed through the SqueezeNet model to extract automated graphical features. Weighted Extreme Learning Machine (ELM) is employed for cervix cell classification. On top of that, R. Elakkiya et al. [ 119 ] discussed the shortcomings of the existing methods for classifying cervical cell cancers. Mainly, they are based on accurate spotting and segmentation, in addition to handcrafted feature extraction. Therefore, they proposed Small-Object Detection-Generative Adversarial Networks (SOD-GAN) with a Fine-tuned Stacked Autoencoder (F-SAE) to detect the lesion faster and classify it into premalignant and malignant without segmentation and preprocessing. At the same time, M. Anousouya Devi et al. [ 120 ] utilized Neutrosophic Graph Cut-based for segmenting preprocessed Pap smear images into non-overlapping regions, which will lead to enhanced classification accuracy. This algorithm depends mainly on transforming preprocessed Pap smear images into the neutrophilic set. Then, the indeterminacy filter played a main role in integrating the intensity, including the spatial information of preprocessed images based on the indeterminacy value. This value specifies the weights for each pixel to define the graph. Finally, the maximum graph is determined to obtain the optimal segmentation results. This approach is better than existing detection methods by over 13%.

4. Discussion

Cervical cancer is a prominent health problem globally, with high mortality as well as incidence rates, particularly in developing countries [ 121 , 122 ]. Early detection is critical for the successful treatment and management of cervical cancer. The traditional method for cervical cancer screening is the Pap smear test, which involves the examination of cervical cells under a microscope for abnormalities. HPV is a very common sexually transmitted infection, with estimates estimating that up to 80% of sexually active women will become infected with HPV at some point in their lives. However, the majority of these infections will clear up on their own without causing any long-term health problems. There are many different types of HPV, and some types are more likely to cause cancer than others. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, there has been further interest in establishing CAD methods to improve cervical cancer screening. CAD technology for cervical cancer detection has been extensively examined over the past few decades [ 123 , 124 ]. Between 1996 and 2022, significant advancements have been made in this field, leading to improved accuracy, sensitivity, and specificity of CAD methods. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells with the aim of identifying abnormal cells and lesions. However, these early systems had limited success due to low sensitivity and specificity.

In the early 2000s, ML algorithms were introduced to the field of CAD for cervical cancer detection. ML algorithms can analyze large datasets and learn from them to identify patterns and make predictions. This allowed for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods [ 125 , 126 , 127 ]. Among the most promising CAD systems for cervical cancer detection is the Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD), which was developed in 2012. HISCCD is a combination of ML algorithms and rule-based systems that analyze digital images of cervical cells to detect abnormal cells and lesions. Several studies have reported improved sensitivity and specificity of HISCCD compared to traditional screening methods. Another promising CAD system is the Automated Cervical Screening System (ACSS), which was introduced in 2016. ACSS uses an ML-based algorithm to analyze digital images of cervical cells and identify abnormal cells and lesions. In a study comparing ACSS to the Pap smear test, ACSS showed higher specificity and sensitivity for detecting high-grade cervical intraepithelial neoplasia. In addition to these systems, there have been several other CAD systems developed over the years, each with its own strengths and limitations. One of the major challenges with CAD systems for cervical cancer detection is the lack of standardized protocols and data sharing, which limits their widespread adoption and validation.

The previous studies describe the most updated state-of-the-art techniques that were suggested, validated, and evaluated for early cervical cancer detection. Most researchers conducted their experiments utilizing image processing in addition to ML and DL. The pre-processing techniques are employed to enhance the visualization of Pap smear images and make feature extraction an easy and more accurate task. Other researchers skipped this step by utilizing DL techniques to extract features automatically, which reduces time and gives accurate results because all of the features excreted in this step are relevant to the corresponding class. However, many researchers focused on HPV, which plays the main role in the infection of cervical cancer. They focused on the nanotechnology track by designing a biosensor that can detect the infection and is distinguished by its stability and linearity. Other researchers focus on building a finite element model for both cancerous and noncancerous cells to study the electrical impedance spectroscopy and compare it with the tested cell to find the matching score between them. They count it as an alternative method that is more accurate than using a Pap smear screening test. Chemical reactions are also considered by other researchers by studying the fluorescence signals from the urine of the infected women and comparing those signals with those of healthy women.

Various methods have been carried out in this area, either in biochemistry, image processing, DL, signals, or nanotechnology tracks, to enhance and reach a highly accurate approach to diagnosing cervical cancer in its early stages. This will reduce the mortality rate among women and increase the chance of survival. In conclusion, CAD technology for cervical cancer detection has come a long way since its introduction in the 1990s. ML-based algorithms have shown promise in improving the accuracy and sensitivity of CAD systems for cervical cancer detection. HISCCD and ACSS are two of the most promising CAD systems, but extensive research and validation are required before they can be broadly applied.

5. Conclusions

Cervical cancer is a substantial public health issue globally, with more than half a million new cases and a quarter of a million deaths each year. Early detection and treatment of cervical cancer can significantly improve outcomes and save lives. Fortunately, there are several different methods for cervical cancer detection, each with its own limitations and advantages. The Pap smear test is the most broadly employed and popular technique with respect to cervical cancer detection. It is a low-cost, simple, and efficient way to screen for precancerous or cancerous changes in the cervix. The Pap smear test has undergone several improvements over the years, including the use of liquid-based cytology, which has improved its accuracy and sensitivity. However, the Pap smear test is not foolproof and can miss some cases of cervical cancer, especially in its early stages.

The recommended screening guidelines may vary depending on age, risk factors, and previous screening results. In developed countries, the adoption of cervical cancer screening programs has led to a significant decrease in cervical cancer mortality rates. However, in low- and middle-income countries, the lack of access to screening programs and cost-effective screening methods and vaccines is a significant barrier to early detection and effective treatment. Therefore, the development of simple, low-cost, and accurate screening methods that can be implemented in low-resource settings is essential. In recent years, machine learning (ML) and deep learning (DL) algorithms have been deployed to aid in cervical cancer diagnosis and treatment by identifying abnormal and normal cells automatically, precisely, and quickly. These algorithms have demonstrated high sensitivity and specificity in detecting abnormal cervical cells, indicating their potential use as an adjunct to traditional screening methods. However, more research is needed to evaluate the feasibility and effectiveness of these algorithms in real-world clinical settings.

In the future, the identification of important risk factors as well as the utilization of various segmentation pre-processing techniques can enhance the effectiveness of cervical cancer diagnosis and treatment. Bigger and more balanced data can also improve the performance of future classification systems. In conclusion, cervical cancer detection has come a long way over the years, with several different methods available, each with its advantages and limitations. The Pap smear test remains the most frequently employed method, but newer methods, including HPV testing, VIA, and VILI, are becoming more widely used. A colposcopy is also an important tool for follow-up and diagnostic purposes. Regular cervical cancer screening is critical for early detection and successful treatment. Women should discuss their screening options with their healthcare provider and follow the recommended guidelines for cervical cancer screening. By working together, we can continue to improve cervical cancer detection and save lives. Nevertheless, continued innovation and collaboration in this field may facilitate the enhancement of cervical cancer detection and ultimately lower the disease’s burden on women worldwide.

Acknowledgments

Thank you to the Fundamental Research Grant Scheme (FRGS/1/2021/SKK0/UNIMAP/02/1) of the Ministry of Higher Education of Malaysia for supporting this project.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, W.A.M. and S.I.; methodology, W.A.M.; software, H.A.; validation, W.A.M. and H.A.; formal analysis, W.A.M., H.A. and Y.A.-I.; investigation, H.A. and F.S.M.; writing—original draft preparation, W.A.M., S.I., F.S.M., H.A. and Y.A.-I.; writing—review and editing, W.A.M., S.I., F.S.M., H.A. and Y.A.-I.; visualization, W.A.M. and S.I.; supervision, W.A.M. and H.A.; project administration, W.A.M.; funding acquisition, W.A.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The writers certify that they have no conflicting interests in relation to this research.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

DOI: 10.7759/cureus.66218
Corpus ID: 271731549

Cone-Beam Computed Tomography (CBCT)-Guided Adaptive Boost Radiotherapy for a Patient With Locally Advanced Cervical Cancer Ineligible for Brachytherapy

Alice E Silberstein , J. Schiff , +4 authors Jessika A Contreras
Published in Cureus 5 August 2024
Medicine, Engineering

36 References

Phase i/ii study of stereotactic body radiotherapy boost in patients with cervical cancer ineligible for intracavitary brachytherapy, less than whole uterus irradiation for patients with locally advanced cervical cancer., online adaptive magnetic resonance-guided radiation therapy for gynaecological cancers: preliminary results of feasibility and outcome., updated trends in the utilization of brachytherapy in cervical cancer in the u.s.: a surveillance, epidemiology, and end-results study., esgo/estro/esp guidelines for the management of patients with cervical cancer – update 2023*, simulated computed tomography-guided stereotactic adaptive radiotherapy (ct-star) for the treatment of locally advanced pancreatic cancer., adaptive magnetic resonance-guided external beam radiation therapy for consolidation in recurrent cervical cancer, mri-guided adaptive brachytherapy in locally advanced cervical cancer (embrace-i): a multicentre prospective cohort study., the astro clinical practice guidelines in cervical cancer: optimizing radiation therapy for improved outcomes., magnetic resonance–guided radiation therapy to boost cervical cancer when brachytherapy is not available: a case report, related papers.

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Cervical cancer articles within Nature Reviews Clinical Oncology

Research Highlight | 22 July 2024

Tisotumab vedotin effective in recurrent cervical cancer

Peter Sidaway

Review Article | 17 May 2024

Epidemiology of HPV-associated cancers past, present and future: towards prevention and elimination

Lessons from the prevention of cervical cancer, the first cancer type deemed amenable to elimination, can provide information on strategies to manage other cancers. Infection with human papillomavirus (HPV) causes virtually all cervical cancers and an important proportion of other cancer types. The authors of this Review discuss the epidemiology of HPV-associated cancers and the potential for their elimination, focusing on the cofactors that could have the greatest effect on prevention efforts and health equity.

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Non-inferiority of simple versus radical hysterectomy in low-risk cervical cancer

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Neoadjuvant chemoimmunotherapy is effective in locally advanced cervical cancer

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Benefit with cemiplimab in cervical cancer

Research Highlight | 06 October 2021

Pembrolizumab tunes up chemotherapy in cervical cancer

Research Highlight | 29 January 2021

SCRT for early stage cervical cancer

David Killock

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TIL infusions effective in HPV-associated cancers

Research Highlight | 21 November 2018

Less invasive is not always better

News & Views | 11 April 2017

Novel molecular subtypes of cervical cancer — potential clinical consequences

The Cancer Genome Atlas Research Network recently published the most comprehensive, multi-omic molecular characterization of cervical cancers performed to date. The data reveal novel disease subtypes, and provide new insights into the aetiology and pathogenesis of cervical cancer. Importantly, the information obtained has potentially major clinical implications.

Chris J. L. M. Meijer
& Renske D. M. Steenbergen

Research Highlight | 13 October 2015

Therapeutic HPV vaccine holds promise

Opinion | 01 September 2015

HPV-FASTER: broadening the scope for prevention of HPV-related cancer

Human papillomavirus (HPV)-screening technologies and HPV vaccination are revolutionizing the management of cancers related to this virus, in particular, cervical neoplasms. At present, however, the effectiveness of these modalities is not optimal, owing to the limited scope of HPV-vaccination and cervical screening programmes. In this Perspectives, an international panel of experts describes for the first time a new campaign, termed 'HPV-FASTER', which aims to broaden the use of HPV vaccination coupled with HPV testing to women aged up to 30 years, and in some settings up to 50 years, with the aim of accelerating the reduction in the incidence of HPV infections and cervical cancer. The authors describe the evidence supporting this approach and details on how it might be implemented, discuss the opportunities—particularly in low-resource settings—and challenges associated with the strategy, and highlight key research gaps that need to be addressed in future studies.

F. Xavier Bosch
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& Jack Cuzick

In Brief | 23 June 2015

Less than three doses of HPV-16/18 prevents HPV infection

News & Views | 02 June 2015

Squamocolumnar junction ablation—tying up loose ends?

Despite the commercialization of HPV vaccines, cervical cancer remains a major cause of death, especially in developing countries. Recent data implicate a discrete population of cells within the cervical squamocolumnar junction in the pathogenesis of cervical precancerous lesions, indicating that ablation of these cells might reduce the rate of cervical cancer in high-risk populations.

Michael Herfs
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News & Views | 24 February 2015

New standard of care—HPV testing for cervical cancer screening

High-risk human papillomavirus (hrHPV) types cause cervical cancer. Hence, a negative hrHPV test provides excellent reassurance against cervical precancer and cancer, superior to a negative cervical smear (Papanicolaou or Pap) test. Screening first for hrHPV might improve the accuracy and positive predictive value of secondary Pap testing in hrHPV-positive women, and thus guide decisions on what care is needed.

Philip E. Castle

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Survival benefit and quality of life

Lisa Hutchinson

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Further delineating bevacizumab's response spectrum

The use of antiangiogenic drugs, such as bevacizumab, represents an appealing intervention against cancer. However, not all malignancies are equally responsive to such treatment. Recent trials demonstrate the efficacy of this drug for advanced-stage cervical cancer and, despite limitations, bevacizumab provides an important clinical respite for most patients with progressive glioblastoma.

David A. Reardon
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Year in Review | 21 January 2014

Screening comes of age and treatment progress continues

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& Peter J. F. Snijders

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Declining use of brachytherapy for cervical cancer in USA

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Review Article | 04 June 2013

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Highly efficacious vaccines are available to protect against persistent human papillomavirus (HPV) infection and, therefore, the associated neoplasias (most notably cervical cancer). This Review article discusses the two approved vaccines in terms of their structure, mode of action, efficacy, cross-reactivity with non-vaccine HPV types, safety and use in vaccination programmes.

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Cervical cancer—should we abandon cytology for screening?

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& Johannes Berkhof

News & Views | 14 February 2012

More evidence supporting human papillomavirus testing

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News & Views | 01 July 2010

HPV testing for cervical cancer: the good, the bad, and the ugly

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News & Views | 01 February 2010

Cisplatin combinations in cervical cancer—which is best?

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IL-12 polymorphism linked to cervical cancer risk

Vessela Vassileva

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Published: 06 August 2024

Challenges associated with follow-up care after implementation of an HPV screen-and-treat program with ablative therapy for cervical cancer prevention

Rachel M. Morse 1 ,
Joanna Brown 2 ,
E. Jennifer Ríos López 2 ,
Bryn A. Prieto 1 ,
Anna Kohler-Smith 2 ,
Karina Gonzales Díaz 3 ,
Magaly Figueredo Escudero 3 ,
Daniel Lenin del Cuadro 3 ,
Giannina Vásquez del Aguila 3 ,
Henrry Daza Grandez 4 ,
Graciela Meza‑Sánchez 5 ,
J. Kathleen Tracy 6 ,
Patti E. Gravitt 7 na1 ,
Valerie A. Paz‑Soldan 1 , 2 na1 &

the Proyecto Precancer Study Group

BMC Public Health volume 24 , Article number: 2121 ( 2024 ) Cite this article

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Cervical cancer is a preventable cancer; however, decreasing its prevalence requires early detection and treatment strategies that reduce rates of loss to follow-up. This study explores factors associated with loss to follow-up among HPV-positive women after implementation of a new HPV-based screen-and-treat approach for cervical cancer prevention in Iquitos, Peru.

We conducted semi-structured interviews with “obstetras” (i.e., midwives) ( n = 15) working in cervical cancer prevention and women ( n = 24) who were recorded as lost to follow-up after positive HPV results. We used the Health Care Access Barriers Model to guide analyses. We utilized manifest content analysis to describe barriers to follow-up according to the obstetras and thematic analysis to report themes from the women’s perspectives. We also report the steps and time taken to contact women.

We found an incomplete and fragmented patient monitoring system. This incomplete system, in conjunction with challenges in contacting some of the women, led to structural barriers for the obstetras when attempting to deliver positive results. Women in this study expressed a desire to receive treatment, however, faced cognitive barriers including a lack of understanding about HPV results and treatment procedures, fear or anxiety about HPV or treatment, and confusion about the follow-up process. Women also reported having important work matters as a barrier and reported frequently using natural medicine. Reported financial barriers were minimal.

This study highlights the barriers to follow-up after implementation of a primary-level HPV-based screen-and-treat approach. While some barriers that have previously been associated with loss to follow-up were not as prominently observed in this study (e.g., financial), we emphasize the need for screen-and-treat programs to focus on strategies that can address incomplete registry systems, structural challenges in results delivery, cognitive barriers in understanding results and treatment, and work-related barriers.

Peer Review reports

Cervical cancer is the second most common cancer among women in South America [ 1 ]. In the Loreto district of Peru, cervical cancer is the primary contributor to cancer-related deaths among women, and the mortality rate from cervical cancer in this region is the highest in Peru at approximately 26.8 per 100,000 [ 2 ]. However, cervical cancer can be effectively prevented by utilizing vaccines for human papillomavirus (HPV) – the main cause of cervical cancer – and early detection and treatment (EDT) programs [ 3 , 4 ]. Successful implementation of vaccination and EDT programs requires adaptations to the complexities of the local healthcare system. These adaptations are needed to ensure access to effective screening, timely follow-up for abnormal screening results, and prompt treatment for those requiring it. Among the adaptations required to reduce cervical cancer mortality rates is addressing loss to follow-up (LTFU). Women who are LTFU are screen-positive; however, they do not reach an appropriate conclusion in their continuum of care by either receiving treatment or a negative confirmatory screening test [ 5 , 6 ].

To facilitate strengthening of the cervical cancer EDT program in the Loreto district of Peru, an implementation science project, Proyecto Precancer, worked with local health authorities to co-design and create a new EDT approach: a screen-and-treat program. This primary-level approach includes HPV testing as screening, and visual triage for those with a positive result. The visual triage determines eligibility for ablative therapy at select primary-level centers with trained personnel and equipment. Women ineligible for ablative therapy are referred for specialist hospital-level follow-up. Prior to implementation of this screen-and-treat approach, Proyecto Precancer collected monitoring and evaluation data on the number of women who tested positive following visual inspection with acetic acid (VIA) in the Micro Red Iquitos-Sur (MRIS) health network of Loreto and their subsequent hospital-level follow-up care or lack thereof. In the MRIS, before the new screen-and-treat approach (between January 2018 and June 2019), 69.8% (120/172) of these women were LTFU [ 7 ].

In parallel, also before implementation of the new screen-and-treat approach, the Proyecto Precancer team interviewed women who were LTFU at the hospital-level to help understand this high rate of LTFU [ 8 ]. These participants described a strong desire to complete the continuum of care but encountered a fragmented, burdensome system that continuously impeded their care. They faced cognitive barriers such as a lack of knowledge about cervical cancer, misunderstandings about screening results or treatment, lack of awareness of the follow-up process, unclear communication from staff, and preconceived notions about challenges at the hospital-level. They also encountered structural barriers including challenges receiving results or scheduling appointments, unavailability of providers, long wait times, complicated care processes, and broken equipment, and financial barriers including out-of-pocket payments and costs related to travel or missing days of work [ 8 ]. These hospital-level barriers are also commonly found in other low- and middle-income settings [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Barriers to care in a primary-level HPV based screen-and-treat program were studied in Kenya from the perspective of healthcare providers [ 17 , 18 ], which identified cognitive barriers among women including a lack of knowledge about HPV and cervical cancer, structural barriers such as a lack of supplies and lack of adequate staffing, and financial barriers including the cost of transport to health facilities.

Proyecto Precancer’s implementation of the screen-and-treat approach aimed to address many of the reasons women are LTFU at the hospital-level by task shifting the follow-up and management of those with abnormal screening results (HPV positive in the case of the new approach) from the hospital-level to the primary-level facilities. Following implementation of this new approach in 2019, in an additional study conducted by our team, we found that screening rates significantly increased, more than doubling from 83 to 176 screening tests per month between January 2018 and February 2020 [ 19 ]. Moreover, in this post-implementation study (between July 2019 and February 2020), we found a LTFU rate of 30.0% (174/580) among women with a positive HPV result in the MRIS [ 7 ]. While this LTFU rate is a considerable improvement over the LTFU rate of 69.8% before implementation, women are still being LTFU in the screen-and-treat approach.

Our study aimed to explore the factors associated with LTFU in the HPV-based screen-and-treat approach, with visual triage and ablative therapy at the primary-level for those with positive HPV tests. We considered the perspectives of women who are recorded as LTFU after receiving a positive HPV test, as well as obstetras working in cervical cancer prevention in Iquitos, Peru. Women can be LTFU at several points through the continuum of care including not receiving their HPV result, not wanting to attend ablative therapy treatment, and not attending ablative therapy treatment. While women can be LTFU in the new screen-and-treat approach at the hospital-level, we focus here on the primary-level as our previous paper outlines hospital-level barriers [ 8 ]. This study incorporates the perspectives of multiple stakeholders and provides insights into changes that are necessary in many low- and middle-income settings to further reduce rates of LTFU in screen-and-treat programs at the primary-level.

To ensure we obtained a detailed understanding of LTFU at the primary-level following implementation of the HPV screen-and-treat approach, we conducted two types of semi-structured interviews: 1. interviews recorded using a data collection spreadsheet with obstetras working in cervical cancer care and 2. Interviews recorded and transcribed verbatim with women who were documented as LTFU in the MRIS. All interviews focused on the same topic: reasons for LTFU following implementation of the screen-and-treat approach. Our team also recorded the steps and time required to contact each woman for the interview. Although we created a list of women who were documented as LTFU, we do not report a LTFU rate from this data because determining such a rate requires supplementary data collection (e.g., manual, hospital-level searches), which was beyond the scope of this study focused on exploring barriers to follow-up.

This study was conducted in the northern Peruvian Amazon rainforest, specifically in the MRIS health network (population 127,000) in Iquitos (population 400,000). Iquitos is the capital city of the Loreto district. It is the largest city in the world that can only be reached by plane or by boat; there are no roads to the city. Many of the MRIS communities outside of the city can only be reached by the one highway or by river. Additionally, within 15 min of leaving the city, there is limited to no cellphone coverage. Fishing, agriculture, logging, oil extraction, tourism, and small businesses are the main sources of income in Iquitos.

The public health facilities in this study are covered by the Seguro Integral de Salud (SIS) [Comprehensive Health Insurance]. SIS is a public healthcare insurance program that provides full or partially subsidized insurance to people in Peru living in poverty or extreme poverty. In Loreto, 67% of the population has SIS coverage [ 20 ]. All women in our study were covered by SIS, providing a general indication of socio-economic status of these women.

The MRIS is home to 20,000 women between 30 and 49 years old who are eligible for the new HPV-based screen-and-treat approach [ 21 ]. In this approach, women are first screened with an HPV test and if positive, are followed up with visual triage to determine eligibility for ablative therapy or referral to the hospital. Women in the MRIS can choose to either self-sample the HPV test at home (e.g., during a healthcare campaign) or at the health facility, or can choose to have the HPV sample collected by an obstetra at the health facility.

Within the MRIS, there are 17 SIS health facilities ranging in size and capacity. Some larger facilities are staffed with doctors, nurses, and obstetras and have laboratories, while other smaller facilities are staffed only by one obstetra and are open for limited hours. The obstetras provide preventative women’s reproductive and sexual health services. Specifically in relation to the new HPV-based screen-and-treat approach, they provide HPV counseling (i.e., what the test is for, how to do it, and what a positive or negative result means), HPV testing, results delivery at the health center or by home visits (or if necessary, by phone), and scheduling for triage and ablative therapy appointments. The obstetras do not provide ablative therapy treatment; instead, this treatment is done by trained doctors at one of two primary-level triage/ablative therapy facilities.

Ideally, when women in the MRIS test positive for HPV, they receive their results with counseling from their obstetra and, during that visit, are referred to one of the primary-level triage/ablative therapy facilities for triage. At these facilities, women receive counseling on ablative therapy (in this case, thermocoagulation) from the obstetra . In the case where a woman attends primary-level triage and the doctor deems that she is not eligible for ablative therapy treatment (i.e., acetowhite lesions over 75% of transformation zone, suspicious lesions, or transformation zone that is not visible), she is referred to one of two regional hospitals for specialist follow-up care. All MRIS obstetras received training on HPV counseling using flipcharts and health education materials, as well as instruction about referring HPV positive women. Obstetras working at the two triage/ablative therapy facilities received additional training for counseling on the procedure. Doctors at the triage/ablative therapy facilities were trained by specialists and were supervised by local gynecologists for their first 15 cases.

Participant selection, procedures, and data collection

Our sampling process is summarized in Fig. 1 . We began by generating a list of 630 women (ages 30–49) who had a positive HPV result between May 2019 and November 2020 (post-implementation of the HPV screen-and-treat approach) recorded in SIMOPP, a Proyecto Precancer monitoring and evaluation system. We then subset this list to include only women who had no recorded evidence of treatment within 10 months of their positive HPV result. Despite implementation of SIMOPP, when HPV testing began in the MRIS, we observed that some obstetras continued to use handwritten notebooks to record their patient data. In most cases, obstetras recorded this data in their notebooks and in SIMOPP; however, in some cases, this data was only recorded in their notebooks. As a result, we also cross-referenced the SIMOPP list with obstetras ’ notebooks to create a final list of 120 women who had not attended treatment.

PRISMA chart depicting the sample selection process

Interviews with obstetras

We then purposively selected a sample of obstetras from the 16 MRIS health facilities with women who were LTFU to complete semi-structured interviews regarding their perspectives on why the 120 women were LTFU. Most health facilities in the MRIS have only one obstetra per shift; however, at the larger health centers with more than one, we invited the obstetra who was most involved in providing HPV-related care. We interviewed 15 obstetras between July 2021 and August 2021. All obstetras provided informed consent prior to interviews with one of two Peruvian researchers (J.B., E.J.R.L.). Interviews were completed in Spanish over the phone or in person in a private area of the health facility. For each woman who was LTFU at the obstetra ’s health center, the obstetra was asked – to the best of their knowledge – to answer yes or no to each question and explain why or why not, as relevant: 1. Whether they were aware that the patient had a positive HPV test, 2. Whether they were able to contact the patient, 3. Whether the patient wanted to attend ablative therapy, 4. Whether ablative therapy treatment had been scheduled, 5. Whether the patient attended ablative therapy treatment, 6. Whether the patient received ablative therapy treatment, and 7. Whether the patient was referred for additional hospital-level follow-up. Lastly, the obstetras were asked what they believed the final resolution was for each patient (e.g., where they were LTFU) (see Supplemental Fig. 1). The researchers documented responses using a data collection spreadsheet. The spreadsheet included space for additional comments.

During the interviews, the obstetras reported that 18 women who were previously recorded as LTFU had attended triage and either received ablative therapy or were referred to the hospital and received hospital-level treatment, despite there being no record of this.

Interviews with women

We then selected 35 of the women who were still reported to be LTFU to participate in semi-structured interviews, approximately one-third of the women reported LTFU (see Fig. 1 ). Although we knew we would be unlikely to be able to reach all 35 women, we estimated and later ensured that the subset of women we were able to reach would be enough to have diversity (e.g., women who were LTFU at different points in the continuum of care) and reach saturation based on previous work [ 8 , 22 ]; however, we were prepared to add interviews as needed. For the interviews, we contacted women over the phone (if they had a phone and had service) or by a house visit to coordinate interviews. The women’s interviews were conducted in Spanish between August 2021 and February 2022 over the phone or in a private location in the participants’ homes, after they provided informed consent. We used a topic guide and focused the interviews on women’s understandings of and experiences with HPV and HPV screening, women’s desire to receive care, and women’s emotions about and experiences with the care process. The interviewer also asked women, as relevant, whether they received their HPV result, wanted to attend ablative therapy, had scheduled ablative therapy treatment, attended ablative therapy treatment, received ablative therapy treatment, and were referred for additional hospital-level follow-up (see Supplementary Fig. 1). If the woman being interviewed had not yet received her positive HPV result, the interviewer (E.J.R.L.) explained that the HPV test was positive, provided counseling, explained that the woman could attend treatment, if she would like, and provided help scheduling treatment, if requested. In the case where a woman had received her positive HPV result but did not know about available treatment, the interviewer described the treatment and provided help scheduling an appointment, if requested. All interviews were audio recorded and transcribed verbatim. We conducted a total of 24 interviews with women, at which point the researchers did observe saturation and no new findings emerging. The interviewer also took field notes which included information on the steps and time taken to contact and interview each participant.

Data analysis

We used the Health Care Access Barriers (HCAB) Model to guide the analysis. The HCAB is a framework developed to classify, analyze, and report measurable and modifiable health determinants categorized into three types of barriers: financial, structural, and cognitive [ 23 ].

Analysis of obstetras’ interviews

The researchers (R.M.M, J.B.) used manifest content analysis to analyze the obstetras’ interviews. We categorized the women discussed in these interviews into groups according to the barrier stated by the obstetra that resulted in their LTFU, if this barrier was known, and to count the number of women in each of the groups. Each of the barriers was then categorized according to the HCAB model, if applicable, or was categorized as other, if not applicable.

Analysis of women’s interviews

In Dedoose Version 8.0.35, the researchers (R.M.M, J.B.) analyzed the interviews with the women using thematic analysis and developed a codebook using the HCAB model. The codebook was adjusted as interview transcripts were reviewed. Ten transcripts were double coded, and any coding differences were discussed between the coders and resolved by consensus. Once all transcripts were coded, the coders reviewed the transcripts to ensure the coding was consistent with the final codebook.

Additional analyses

To consider challenges in contacting women, we report the steps the interviewer took and the time required to contact the women and conduct the interviews. Finally, to examine discrepancies and concordances between obstetras and women, we report whether the obstetras ’ reasons stated for why each woman was LTFU matched what each woman stated as her reason why she was LTFU.

Obstetra interviews

We interviewed 15 obstetras working at 16 health facilities. One of the 17 health facilities was excluded as they had no women who were LTFU, and one obstetra worked at two health facilities. We interviewed the obstetras about the 120 women with no documentation of attending triage for ablative therapy or ablative therapy, who we considered LTFU. Following these interviews, we were missing data on two of the women whose completion of follow-up care was not reported during the obstetra interviews. Of the remaining 118 women, obstetras reported that 18 women reached an endpoint of care despite there previously being no record of reaching an endpoint following their positive HPV test: 13 received ablative therapy, three received hospital-level treatment, and two received a negative confirmatory screening test through private follow-up care. Finally, of these 100 women reported by the obstetras as LTFU, one attended triage for ablative therapy and was referred to the hospital, and four were referred directly to the hospital. These four women completed Pap tests at the same time as their HPV tests and were referred to the hospital because of their positive Pap screening results. These five women were LTFU at the hospital-level, and we focus on the 95 women LTFU at the primary-level below. In summary, we arrived at 95 women LTFU at the primary-level out of 120 because two women were missing data, 18 received follow-up care according to the obstetras , and five had been LTFU at the hospital-level according to the obstetras .

Of the 95 women who were LTFU at the primary-level, the obstetras provided a reason for why the woman was LTFU in 70 cases; the reasons were unknown to the obstetra for the other 25. According to the obstetras , 47 of the 70 women were LTFU due to three main structural barriers: challenges in contacting the women, a lack of registry of the HPV results at the primary-level (e.g., a new obstetra without access to the former obstetra ’s notebook), or pending results delivery for women who had not yet been contacted. Eighteen of the 70 women were LTFU due to other reasons (e.g., vacation, being pregnant at the time of result delivery, preference for natural medicine). Five of the 70 women were LTFU due to two main cognitive barriers: fear of cancer or of treatment and aftereffects. No women were reportedly LTFU due to financial barriers (Fig. 2 ).

Summary of barriers to the completion of care according to the obstetras

Contacting women for interviews

Of the 35 women who were selected to take part in interviews, we were only able to contact 24 women. Nineteen of the 35 (54.3%) women provided a phone number they could be reached at; however, only eight (22.9%) women were able to be contacted through the phone number (e.g., some women did not answer, some changed their number). The 27 (77.1%) women who could not be contacted by phone needed to be contacted with a house visit. However, out of these 27 women, 22 (81.5%) did not provide a specific address (e.g., did not include a street name or house number), and in the end, we were only able to contact 16 of the 27 (59.3%) women who could not be reached by phone. These women needed to be searched for in a door-to-door search. It took the interviewer an average of 3.6 h and an average of 2.2 attempts searching in person to contact each of these 16 women, find where she lived, and arrive at the address. In summary, we were unable to contact 11 women by phone or house visit; these women were not interviewed. We were able to contact eight women by phone and 16 women in person for a total of 24 women interviewed.

Sample characteristics

We interviewed 24 women (age mean 39.6 years) identified as LTFU. Fifteen (62.5%) women were from urban health facilities, six (25.0%) from peri-urban health facilities, and three (12.5%) from rural health facilities. Of the 24 women, seven (29.2%) reported having their test done in the community (e.g., during a campaign where obstetras went door-to-door), 15 (62.5%) had their HPV test done at the health center, and two (8.3%) did not report where it was done but are still included in our sample. Of the 24 women, thirteen (54.2%) had not received their HPV result. Of those who had received their HPV result ( n = 11), seven (63.6%) received it at the facility, two (18.2%) during a house visit by an obstetra , and two (18.2%) over the phone.

Five of the 24 women reported that despite there being no record of reaching an endpoint of care, they did reach an endpoint: two reported receiving hospital-level treatment and three reported receiving ablative therapy at the primary-level. Two of the women who completed care received follow-up in a private facility:

Well , when I had the molecular test done , parallel to that , I had a biopsy done privately. With that biopsy , plus the molecular test , it was evident that I had cancer; so , I was referred to the Regional Hospital. (Participant 13, completed treatment)

The five women who completed the continuum of care are not excluded from the following discussion as they spoke about barriers to follow-up that we consider important for understanding system challenges. Fig. 3 summarizes where in the continuum of care each woman was LTFU or completed care according to the women.

Continuum of care model depicting where women were LTFU or completed care according to the women themselves

Main barriers to completing care

All 19 (out of 19) women who were LTFU expressed a desire to receive treatment. One woman described this as: “I am positive for this disease [HPV] , but I would like to be cured ” (Participant 18, LTFU). Another stated, “Well , it motivates me a lot because as women , we can’t have this disease… It’s better to go to our health center and have the doctor’s treatment ” (Participant 7, LTFU). However, despite showing a strong desire to receive treatment, the women were faced with cognitive, structural, financial, and other barriers throughout the continuum of care.

Five main cognitive barriers emerged: lack of understanding about the HPV result, fear or anxiety about HPV, lack of awareness of or confusion about the follow-up process, lack of understanding of treatment procedures, and fear or anxiety about treatment.

Nine women showed a lack of understanding of their HPV result. For example, one woman stated after she received her HPV result, “ The lady told me that I had infections only ,” (Participant 9, LTFU). Another woman expressed confusion about the meaning of the result by stating that she was told her HPV result was negative: “ She [the obstetra] told me , ‘I don’t think it came back positive , it came back good ’ ” (Participant 14, LTFU).

In some of these cases, the lack of understanding was due to a lack of time spent on the explanation by the obstetra . One woman described this as, “ Sometimes you ask the obstetras and sometimes they don’t give you much attention because they have a lot of patients. Sometimes they don’t have a moment to tell you , to help you understand , and sometimes you leave with doubts ” (Participant 7, LTFU). In other cases, the lack of understanding was due to forgetting much of the obstetra ’s explanation. One woman stated, “ Yes , they explained [the HPV test] to me , but I forgot ” (Participant 9, LTFU) while another stated: “To be honest with you , I don’t remember it so well , but I was told that it was to rule out some diseases like cancer or venereal diseases ” (Participant 3, LTFU).

Seven women were anxious or scared about their result or specifically feared cancer. One woman described her fear, “I felt bad , and I was afraid , and I knew I was going to have cancer. It was very hard … The first thing that came to my mind was to think that I was going to die ” (Participant 13, completed treatment). Another woman described how her friends told her that if she went for treatment, she would find out she has cancer:

“Don’t go , you will really get cancer. They are going to put an ugly thing in you , like this. They are going to take out your uterus , oh , no , no , no , don’t go”. Yeah , I also cowardly said , “I’m not going to go.” I was afraid. (Participant 11, LTFU)

During a discussion of the process to receive HPV results, six women mentioned confusion about how to receive results. In some cases, women stated that they expected a house visit or phone call to receive their results and did not get one: “ Because the lady told me that if I have something , she will come and look for me. But I , well , I said to myself that I didn’t have anything. Why? Because she didn’t come looking for me ” (Participant 9, LTFU). In other cases, women were unsure how to receive their results:

At the health post , when I did it [the HPV test] , they didn’t tell me to come back , and I thought that they would tell me something … because the lady didn’t tell me , “You are going to come on such and such a day to find out about your test.” (Participant 20, LTFU).

When discussing treatment, 10 women showed a lack of understanding of treatment and its possible side effects. One spoke about concerns of sterilization with treatment: “ That has been my doubt and when they say ‘sterilization’ , ‘cauterization’ and all that ” (Participant 4, LTFU). Two of these women expressed confusion about whether a treatment was available, with one woman asking the interviewer: “ I would like to ask you a question , does this disease have a cure? ” (Participant 7, LTFU).

Eight women discussed fear or anxiety about treatment. One woman stated, “ I am so afraid of the little machine [thermocoagulator] ” (Participant 9, LTFU), while another stated, “ I’m a little scared , I am. I’ve never done this , and it scares me a little bit ” (Participant 23, LTFU).

The main structural barrier was long wait times for receiving HPV results or follow-up care. Six women reported challenges with completing the continuum of care due to long wait times. Four of these women spoke about delays in receiving their HPV result. One woman stated, “ They told me to go to the health post , and when I went to ask , they told me that the results were not available ” (Participant 20, LTFU), while another stated, “ I went twice to ask the lady if my result had arrived. She told me it hadn’t ” (Participant 9, LTFU).

The main other barrier, reported by five women, was needing to prioritize their more urgent work matters. One woman described her priority of work as: “ I never went , because of work I have not gone” (Participant 1, LTFU). Another stated: “I work , Miss. I sell. At the end of the day , I sell. I go to sell on the street. That’s why I haven’t gone ” (Participant 15, LTFU).

A minority of women (two of the 24 women) specifically mentioned financial barriers. One woman spoke about not having money to travel to the health center, “ I didn’t have the money to go. That’s why I haven’t gone ” (Participant 12, LTFU). Another spoke about the opportunity cost as a result of missing work: “ If I don’t sell , my children don’t eat. If I don’t wash other people’s clothes , they don’t eat either , so how could I go? ” (Participant 11, LTFU).

Facilitators of follow-up care

A few of the themes that were barriers to some participants (e.g., inadequate counseling, not understanding processes), were described as facilitators by those who did receive appropriate information. Specifically, women discussed two main facilitators to completing the continuum of care: good knowledge of or a desire to better understand HPV and its treatment.

Eight women showed a good understanding of HPV and its treatment, often due to good counseling from the obstetras . One woman demonstrated her understanding of HPV: “He told us that this requires a treatment because if we don’t have a treatment , it can advance. If you don’t realize it , as cancer is silent , it can arrive even when you are in the last stage ” (Participant 6, completed care). One woman described a helpful explanation from the obstetra: “She took a good look at my face , she told me that I do have the beginnings of cancer , ‘pre-cancer’ she said , ‘No , the cancer is not there yet. You have pre-cancer. You still have time to get it fixed because you are young. You are strong’” (Participant 11, LTFU).

Additionally, five women showed a desire to learn more about HPV and its treatment. One woman asked the interviewer for more information about HPV: “ Can my partner also have that [HPV]? ” (Participant 3, LTFU). Another woman described looking for information on the internet: “I went and checked on the Internet: what is it , why and how come , and all those things ” (Participant 4, LTFU).

Natural medicine

Ten women spoke about taking natural medicine as a supplement to the care provided in the public healthcare system. Seven of these women had not yet received treatment despite stating they would like to receive treatment during their interview. These women often reported taking natural medicine to address symptoms they were experiencing. One woman stated, “ I took natural medicine for the pain ” (Participant 9, LTFU). Three of these women had already received treatment and took natural medicine to improve their post-treatment healing: “ That is why I continue with natural medicine and with my treatment ” (Participant 22, completed care).

Obstetras’ and women’s outcomes

When comparing data from obstetras ’ interviews with women’s interviews, we found agreement in the reason why women were LTFU in 13 out of 24 cases, non-agreement in 10 cases, and encountered missing data from the obstetra interview in one case.

An important finding in this study was the impact of the absence of a complete registry for managing appropriate follow-up care for HPV positive women. Despite efforts to develop and utilize a hybrid paper/electronic monitoring and evaluation registry system (SIMOPP), as well as manual searches for data at healthcare facilities, there were no records of women in the study completing care prior to the interviews. The obstetras , who coordinate much of the follow-up care, also often had incomplete or inaccurate data on women’s follow-up, including instances where they had no registration of women’s HPV results and instances of mistakenly recording women as having received results when the women stated they had not. The fact that some women complete their care in private settings makes registration of follow-up even more complicated. Additionally, databases for monitoring screening and treatment data were fragmented between primary and hospital-level care, making it challenging to determine if patients referred to the hospital received follow-up care, including women in our study who received undocumented hospital care. While this fragmentation has been seen previously in the MRIS and in other LMICs [ 8 , 24 ], this study also revealed instances where registration of treatment was missing at the primary-level. Successful EDT programs need integrated data registries that are consistently used by all relevant health professionals at the primary- and hospital-levels with accurate documentation of follow-up care linked across levels of care. Implementation science frameworks can be used, including Participatory Action Research, to improve the use of registry systems by allowing stakeholders to internally derive registry systems and feel ownership over the new system [ 25 , 26 ].

Women who were LTFU expressed a desire for treatment but faced various barriers throughout the continuum of care, starting with receiving their results. Obstetras reported, and our team experienced, challenges in contacting these women due to invalid phone numbers or an inability to locate them at their registered address. Conducting house visits was time-consuming, taking almost half a day per woman. This was further complicated by the possibility of women being away during the visit or having moved address. To note, if obstetras were expected to find all their HPV positive women who could not be contacted by phone and it took them almost half a day on average per woman, it would be unfeasible; moreover, the public health system needs to consider that the more time that passes between the HPV screening and the results delivery, the more LTFU should be expected in this mobile community. Relatedly, some women assumed that if they were not visited by an obstetra , everything was fine, while others did not know when or how to pick up their results. For women who went in person to pick up their results, some women described long wait times. Far too often, these factors culminate in women being unable to receive their results in a timely manner or altogether. Long wait times and challenges in delivering results are barriers seen in LMICs [ 9 , 24 , 27 ]. The challenge of timely results delivery or delivery of results at all can be addressed through greater emphasis on information collection from women, including accurately recording full addresses or asking women to provide a second phone number (e.g., a landline). Alternatively, at the time of screening, women could be provided with a phone number to call to receive their results and speak to a trained professional, ideally available 24 h per day, 7 days a week. The system could consider hiring a ‘patient navigator’ who can help guide women through the follow-up care process, particularly if the navigators can access the data registry that allows them to visualize patient data [ 28 ]. Patient navigators have been shown to increase care completion rates following positive cancer screenings [ 29 , 30 ]. Importantly, the patient navigators do not need to be clinical staff but instead can be trained to coordinate care, provide health education and information, and offer counseling and psychosocial support [ 29 ].

The women and obstetras also outlined cognitive barriers to completing the continuum of care including a lack of understanding and fear or anxiety about HPV results and treatment. In some cases, cognitive barriers arose due to obstetras being too busy to provide detailed counseling. In other cases, women forgot information shared during counseling. Importantly, during implementation of the screen-and-treat program in the MRIS, Proyecto Precancer provided counseling training to obstetras that aimed to address many of these cognitive barriers, which were previously identified in the MRIS and other LMICs [ 8 , 9 , 12 , 13 , 18 ]. While this counseling training may have addressed some cognitive barriers – as seen by women in this study who discussed facilitators for care (e.g., a good understanding of HPV) – these cognitive facilitators were not sufficient on their own to overcome all of the barriers that resulted in some women being LTFU. The presence of one facilitator (e.g., a desire to learn more about HPV) is likely inadequate for ensuring care completion; there are multiple steps in the continuum of care, each with its own set of barriers, and to reduce LTFU, facilitators must be present throughout the entire system and corresponding barriers must be addressed. That said, this study highlights the importance of further improving counseling before and after HPV testing, including addressing obstetras ’ time constraints, reducing fear and anxiety, and addressing women forgetting information. The patient navigators could be trained to provide counseling that specifically addresses fear and anxiety around HPV, alleviating the time constraints faced by obstetras . Guidelines and tools can also be developed for patient navigators to promote consistency in key messages and reduce the risk of confusion [ 31 ]. The tools can include take-home health education materials, which can be adapted to the local and cultural context and provide information on HPV, its treatment, and the process of seeking follow-up care. Traditional health education methods, such as take-home counseling materials, have been shown to improve health literacy in LMICs [ 32 ], decrease anxiety, and increase knowledge following abnormal cervical cancer screenings [ 33 ].

Financial barriers in this study were minimal; obstetras reported that no women were LTFU due to financial barriers, while two (out of 24) women specifically reported financial barriers. Importantly, this is a substantial shift in barriers from our previous work in the MRIS at the hospital-level which found that 14 (out of 20) women faced financial barriers [ 8 ]. Financial barriers are commonly found in cervical cancer care in Latin America [ 10 , 11 , 12 , 13 , 15 ], and the shift seen in this study underscores the possibility of reducing financial barriers through task shifting cervical cancer care to the primary-level.

Women in this study also commonly mentioned a lack of time due to more urgent work matters as a barrier. This has been found in other LMICs [ 9 , 27 , 34 ], and previous research in Latin America suggests that informal workers have fewer social protections to allow them to leave work to attend follow-up cervical cancer preventative care [ 14 ]. In Iquitos, much of the economy relies on informal work, and further research can explore support options for women unable to attend follow-up care due to work obligations, such as including a phone service for results or patient navigators.

Approximately half of the women in this study reported using natural medicine. These women also stated that they would like to receive follow-up care in the healthcare system; however, nearly all of them were LTFU, and the obstetras also reported cases where women used natural medicine instead of care in the healthcare system. This suggests two possibilities. First, some women may rely solely on natural medicine (as the obstetras reported), despite expressing a desire for follow-up care in the healthcare system, which may have been reported by the women due to social desirability bias. Alternatively, natural medicine may be used as a complementary approach alongside follow-up care in the healthcare system. Although further research is needed to better differentiate and assess the presence and impact of these two possibilities, in Peru, natural medicine has been found to be used in conjunction with care in the healthcare system [ 8 , 35 ]. For the moment, improved counseling, including take-home materials, may help ensure that obstetras provide consistent and complete information about women being able to use natural medicine in conjunction with the healthcare system and fully inform women about treatment availability [ 32 , 33 ].

While our previous research indicates that implementation of the primary-level screen-and-treat approach with HPV testing and ablative therapy reduced the LTFU rate from 69.8% to 30.0% in the MRIS [ 7 ], task shifting cervical cancer care to the primary-level did not entirely eliminate LTFU. Instead, this shift reduced barriers seen in the previous system, including women’s anticipation of challenges with seeking follow-up care, burdensome multi-step care processes, and out-of-pocket payments [ 8 ]. A holistic, systems thinking approach that considers multiple stakeholders’ perspectives - from women to obstetras to specialists - is necessary for countries to meet cervical cancer elimination goals.

Limitations

Some of the women who were interviewed in this study were not LTFU, despite our inclusion of women who were recorded as LTFU. We decided to include the interviews from these women in the study as they added valuable information about the challenges in the current system. Moreover, during data collection, we triangulated data from a variety of different sources that often relied on recollection, rather than documentation, to try to obtain a complete picture of follow-up care. We recognize that there are likely recall errors. The interviews with the obstetras focused on whether their patients had been LTFU and why; it is possible that some obstetras may have felt pressured to say that they had, for example, delivered results to women when they had not yet. However, our team worked closely and was in regular communication with these obstetras for years. We had focused on building a relationship of collaboration and trust where the obstetras became empowered to discuss improvements needed for the cervical cancer EDT system without judgment and with recognition that they all are part of a larger system that needed collaboration for success. Additionally, when identifying potential participants, if the obstetras reported a woman had completed care, we chose at that time to not investigate further. Outside of the scope of this study, we did verify that the obstetras in this study were mistaken in some of these cases. It is possible that including these women in follow-up interviews would have elucidated additional themes not obtained with our sample; however, we reached saturation in this study. In the discussion, we consider the possibility that financial barriers were decreased in this study following implementation of the screen-and-treat approach. However, this is a qualitative study limited by its sample size. To draw any conclusion about the relationship between task shifting cervical cancer care to the primary-level and financial barriers, further studies with larger sample sizes are required. Lastly, the findings of this study may not be generalizable to other regions; however, they provide information on barriers faced in resource-limited, primary-level screen-and-treat systems.

This study highlights the need for cervical cancer EDT programs to address multifaceted barriers hindering access to follow-up care. By including multiple perspectives – obstetras and women – numerous barriers emerged. We highlighted the need for successful EDT programs to have complete registry systems with patient-level data linked across levels of care. Obstetras in this study encountered structural barriers in contacting women, compounded by a lack of clarity in how HPV results should be delivered. Despite expressing a strong desire for treatment, women in this study encountered additional challenges including cognitive barriers, such as a lack of knowledge about HPV and treatment procedures, fear, anxiety, and confusion about follow-up processes. Additionally, women discussed work commitments as a barrier and spoke about using natural medicine. A complete registry system, patient navigators, strong counseling and take-home materials, and support structures to accommodate work-related time constraints may help address these barriers.

Availability of data and materials

Data and materials are available on request to the corresponding author.

Abbreviations

Human papillomavirus

Early detection and treatment

Lost to follow-up

Visual inspection with acetic acid

Micro-Red Iquitos Sur

Seguro Integral de Salud [Comprehensive Health Insurance]

Health Care Access Barriers

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Acknowledgements

We would like to thank the women and obstetras who generously shared their experiences during this research. Additionally, we would like to thank the stakeholders within the Ministry of Health, DIRESA Loreto, and the Micro Red Iquitos-Sur health network for their collaboration.

The Proyecto Precancer Study Group:

Joanna Brown, Iris Carhuaza, Lita E. Carrillo Jara, María del Carmen Caruhapoma, Meda Del Carpio-Morgan, Henrry Daza Grandez, Magaly Figueredo Escudero, Esther Y. Garcia Satalay, Sarah D. Gilman, Karina Gonzales Díaz, Patti E. Gravitt, José Jerónimo, Alcedo Jorges, Magdalena Jurczuk, Anna Kohler-Smith, Margaret Kosek, Gabriela Ladrón de Guevarra, Daniel Lenin del Cuadro, Renso Lopez Liñán, Andrea Matos Orbegozo, Jaime Marín, Graciela Meza‑Sánchez, Rachel M. Morse, Helen E. Noble, Victor A. Palacios, Valerie A. Paz-Soldan, Reyles Ríos Reátegui, E. Jennifer Ríos López, Patricia Rivas, Karina Román, Anne F. Rositch, Carlos Santos-Ortiz, Hermann F. Silva Delgado, Sandra Soto, Nolberto Tangoa, J. Kathleen Tracy, Javier Vásquez Vásquez, Giannina Vásquez del Aguila, and Karen Zevallos.

Funding for this work was received through the National Institute of Health/National Cancer Institute (grant ID: R01-CA190366, mPI to P.E. Gravitt/V.A. Paz-Soldan; U01-CA190366, mPI to JK Tracy/VA Paz-Soldan). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Patti E. Gravitt and Valerie A. Paz‑Soldan contributed equally to this work.

Authors and Affiliations

Department of Tropical Medicine and Infectious Disease, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA

Rachel M. Morse, Bryn A. Prieto & Valerie A. Paz‑Soldan

Asociación Benéfica PRISMA, Lima, Peru

Joanna Brown, E. Jennifer Ríos López, Anna Kohler-Smith & Valerie A. Paz‑Soldan

Department of Cancer Control and Prevention, Gerencia Regional de Salud de Loreto, Iquitos, Loreto, Peru

Karina Gonzales Díaz, Magaly Figueredo Escudero, Daniel Lenin del Cuadro & Giannina Vásquez del Aguila

Oficina de Servicios de Salud, Gerencia Regional de Salud, Iquitos, Loreto, Peru

Henrry Daza Grandez

Facultad de Medicina Humana, Universidad Nacional de la Amazonía Peruana, Iquitos, Peru

Graciela Meza‑Sánchez

Department of Medicine, University of Vermont College of Medicine, Burlington, VT, USA

J. Kathleen Tracy

Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

Patti E. Gravitt

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Joanna Brown
, Iris Carhuaza
, Lita E. Carrillo Jara
, María Carmen del Caruhapoma
, Meda Del Carpio-Morgan
, Henrry Daza Grandez
, Magaly Figueredo Escudero
, Esther Y. Garcia Satalay
, Sarah D. Gilman
, Karina Gonzales Díaz
, Patti E. Gravitt
, José Jerónimo
, Alcedo Jorges
, Magdalena Jurczuk
, Anna Kohler-Smith
, Margaret Kosek
, Gabriela Ladrón de Guevarra
, Daniel Lenin del Cuadro
, Renso Lopez Liñán
, Andrea Matos Orbegozo
, Jaime Marín
, Graciela Meza‑Sánchez
, Rachel M. Morse
, Helen E. Noble
, Victor A. Palacios
, Valerie A. Paz‑Soldan
, Reyles Ríos Reátegui
, E. Jennifer Ríos López
, Patricia Rivas
, Karina Román
, Anne F. Rositch
, Carlos Santos-Ortiz
, Hermann F. Silva Delgado
, Sandra Soto
, Nolberto Tangoa
, J. Kathleen Tracy
, Javier Vásquez Vásquez
, Giannina Vásquez del Aguila
& Karen Zevallos

Contributions

All authors contributed to the conceptualization and design of the study. R.M.M, J.B., E.J.R.L, A.K.S., P.E.G., and V.A.P.S. contributed to the data curation. R.M.M, J.B., B.A.P., and J.K.T. contributed to data analysis. R.M.M. and J.B. prepared a first draft of the manuscript. All authors contributed substantially to subsequent revisions and approved the final manuscript.

Corresponding author

Correspondence to Valerie A. Paz‑Soldan .

Ethics declarations

Ethics approval and consent to participate.

The study was reviewed and approved by all participating ethical institutional review boards at Asociación Benéfica PRISMA (CE0251.09), Tulane University School of Public Health and Tropical Medicine (reference number 891039), the University of Maryland School of Medicine (IRB#061614), Hospital Regional Loreto (ID-002-CIEI-2017), and Hospital Apoyo Iquitos (065-ID-ETHICS COMMITTEE HICGG- 2018). Written informed consent was obtained from all study participants prior to the interviews, and the study was performed in accordance with the Declaration of Helsinki.

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Competing interests

Patti E. Gravitt reports receiving other commercial research support from Cepheid. No potential conflicts of interest were disclosed by the other authors.

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Morse, R.M., Brown, J., Ríos López, E.J. et al. Challenges associated with follow-up care after implementation of an HPV screen-and-treat program with ablative therapy for cervical cancer prevention. BMC Public Health 24 , 2121 (2024). https://doi.org/10.1186/s12889-024-19436-3

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DOI : https://doi.org/10.1186/s12889-024-19436-3

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In both time periods, Asian and Hispanic women were underrepresented in clinical trials for all 3 cancer sites. Black women with an endometrial or cervical cancer diagnosis were either adequately represented or overrepresented in both time periods, but Black women with ovarian cancer were underrepresented. White women were adequately represented or overrepresented in clinical trials for all 3 cancer sites. Numbers were too low to generate meaningful estimates for American Indian/Alaska Native, Native Hawaiian/Pacific Islander, or other race women.

eTable 1. International Classification of Diseases for Oncology–3 Codes Including Gynecologic Cancer Histology Types

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eTable 2. Participation-to-Prevalence Ratios for Women With a Gynecologic Cancer Diagnosis According to Cancer Site and Stratified by Year of Diagnosis

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Khadraoui W , Meade CE , Backes FJ , Felix AS. Racial and Ethnic Disparities in Clinical Trial Enrollment Among Women With Gynecologic Cancer. JAMA Netw Open. 2023;6(12):e2346494. doi:10.1001/jamanetworkopen.2023.46494

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Racial and Ethnic Disparities in Clinical Trial Enrollment Among Women With Gynecologic Cancer

1 Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The Ohio State University Comprehensive Cancer Center, James Cancer Hospital and Solove Research Institute, Columbus
2 Division of Epidemiology, College of Public Health, The Ohio State University, Columbus

Question Are there racial and ethnic disparities in clinical trial enrollment among women with gynecologic cancer?

Findings In this cohort study of 562 592 women with endometrial, ovarian, or cervical cancer, the odds of clinical trial enrollment were lower among Asian, Black, and Hispanic women compared with White women. Comparisons with the US population demonstrated overrepresentation among White women for all cancer sites, underrepresentation among Asian and Hispanic women for all cancer sites, and varied patterns for Black women depending on cancer site.

Meaning These findings suggest that efforts to engage women with gynecologic cancer who are from minoritized racial and ethnic groups are needed to increase their representation in clinical trials.

Importance Racial and ethnic disparities in clinical trial enrollment are unjust and hinder development of new cancer treatments.

Objective To examine the association of race and ethnicity with clinical trial enrollment among women with endometrial, ovarian, or cervical cancer.

Design, Setting, and Participants This retrospective cohort study used data from the National Cancer Database, a hospital-based cancer registry, and the Surveillance, Epidemiology, and End Results Program (SEER), a population-based cancer registry. Population-based race and ethnicity–specific proportions for each cancer site were derived from SEER. Participants included women with an endometrial, ovarian, or cervical cancer diagnosed from 2004 to 2019. Analyses were performed from February 2 to June 14, 2023.

Exposure Race and ethnicity were categorized as American Indian/Alaska Native, Asian, Black, Hispanic (any race), Native Hawaiian/Pacific Islander, White, and other (not defined in the National Cancer Database).

Main Outcomes and Measures The primary outcomes were the odds of clinical trial enrollment and representation in clinical trials compared with the US population. Multivariable-adjusted logistic regression was used to estimate odds ratios (ORs) and 95% CIs for associations of race and ethnicity with clinical trial enrollment within the National Cancer Database sample. Participation-to-prevalence ratios (PPRs) according to diagnosis period (2004-2011 vs 2012-2019) were calculated by dividing the race and ethnicity–specific percentage of clinical trial participants in the study sample by the percentage of racial and ethnic groups in SEER.

Results Among 562 592 patients with gynecologic cancer (mean [SD] age at diagnosis, 62.9 [11.3] years), 1903 were American Indian/Alaska Native, 18 680 were Asian, 56 421 were Black, 38 145 were Hispanic, 1453 were Native Hawaiian/Pacific Islander, 442 869 were White, and 3121 were other race and ethnicity. Only 548 (<1%) were enrolled in clinical trials. Compared with White women, clinical trial enrollment was lower for Asian (OR, 0.44; 95% CI, 0.25-0.78), Black (OR, 0.70; 95% CI, 0.50-0.99), and Hispanic (OR, 0.53; 95% CI, 0.33-0.83) women. Compared with the US population, White women were adequately or overrepresented for all cancer types (PPRs ≥1.1), Black women were adequately or overrepresented for endometrial and cervical cancers (PPRs ≥1.1) but underrepresented for ovarian cancer (PPR ≤0.6), and Asian and Hispanic women were underrepresented among all 3 cancer types (PPRs ≤0.6).

Conclusions and Relevance In this cohort of patients with gynecologic cancer, clinical trial enrollment was lower among certain minoritized racial and ethnic groups. Continued efforts are needed to address disparate clinical trial enrollment among underrepresented groups.

Health care disparities exist within all scopes of medicine and occur along various dimensions, including race and ethnicity, socioeconomic status, geography, and language. Racial and ethnic inequities in gynecologic oncology treatment and outcomes are well-established and deeply entrenched in the social determinants of health, 1 prompting calls to address these gaps in care. 2 Clinical trials, defined as research in which humans are prospectively assigned to 1 or more interventions for the evaluation of health-related effects, 3 are essential for ensuring validity, generalizability, and equity of care, as well as advancing medical knowledge. Recent reports 4 , 5 suggest that between 6% and 8% of the US adult population with cancer participates in clinical trials, with lower representation of patients from minoritized racial and ethnic groups. Structural barriers (eg, lack of clinical trials in regions with a higher density of minoritized patients) and clinical factors (eg, narrow eligibility criteria that disproportionately affect underrepresented populations) have resulted in lower clinical trial enrollment of racial and ethnic minoritized groups, 4 with evidence that this contributes to poorer survival. 6 - 9

In a recent review, Barry and colleagues 10 outlined the extent of racial disparities in clinical trial enrollment of patients with gynecologic cancer. Most of the reviewed studies compared observed enrollment in clinical trials identified through ClinicalTrials.gov with expected enrollment derived from population-based, age-adjusted incidence rates. Collectively, women from minoritized racial and ethnic groups were underrepresented in these trials, whereas White women were more likely to be overrepresented across gynecologic cancer types. This work is an important starting point for describing racial and ethnic disparities in clinical trial enrollment of patients with gynecologic cancer; however, studies with an internal comparison group with adjustment for potential confounders are needed to fully understand the complex picture of clinical trial enrollment. Moreover, these studies highlight the need for data sets that include large numbers of women from underrepresented groups. As such, we examined associations of race and ethnicity with clinical trial enrollment among women with gynecologic cancer using the National Cancer Database (NCDB). In addition, we present participation-to-prevalence ratios (PPRs) according to period of diagnosis to evaluate trends in the representation status of underrepresented groups in gynecologic cancer trials.

The 2020 Participant User File was obtained from the hospital-based NCDB, 11 a cancer registry capturing 70% of cancers diagnosed in the US. Data include sociodemographic characteristics, tumor characteristics, treatment facility attributes, treatment, and survival outcomes abstracted from patient medical records by Certified Tumor Registrars. 12 Data submitted to NCDB undergo rigorous quality checks according to American College of Surgeons standards. This study was exempt from the Ohio State University institutional review board and the need for informed consent because the data were anonymous and publicly available, in accordance with 45 CFR §46. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines for cohort studies. 13

We used International Classification of Disease for Oncology, Third Edition primary site codes to identify women (aged ≥18 years) with 1 of the following gynecologic cancers diagnosed between 2004 and 2019: endometrial (C54.0-C54.9 and C55.9), cervical (C53.0, C53.1, C53.8, and C53.9), ovarian (C56.9), fallopian tube (C57.0), peritoneal (C48.1 and C48.2), and retroperitoneal (C48.0). 11 , 12 We restricted the sample to common histologic types (eTable 1 in Supplement 1 ), resulting in an initial sample size of 1 018 044 women. We excluded women for the following reasons: unknown race (51 384 women), unknown facility location or type (60 441 women), unknown income or education level (105 963 women), unknown clinical trial enrollment (71 women), noninvasive or unknown cancer stage (111 827 women), unknown radiation treatment (17 330 women), unknown chemotherapy (8019 women), and unknown follow-up time (341 women). Additional cancer site-specific exclusions are detailed in the eAppendix in Supplement 1 . Following exclusions, ovarian, peritoneum, retroperitoneum, and fallopian tube cancers were grouped together for analysis.

We categorized women as enrolled in a clinical trial when response observations were enrolled in an institutional (code 2) or double-blind clinical trial (code 3). Categories of no trial (code 0), other (code 1), other–unproven (code 6), or refused trial (code 7) were categorized as no clinical trial enrollment. 14

Race and ethnicity were available as self-reported variables coded by the NCDB. We cross-classified race (American Indian/Alaska Native, Asian, Black, Native Hawaiian/Pacific Islander, White, and other) and ethnicity (Hispanic vs non-Hispanic) to produce the following categories: non-Hispanic Asian (hereafter referred to as Asian), non-Hispanic American Indian/Alaska Native (hereafter referred to as American Indian/Alaska Native), non-Hispanic Black (hereafter referred to as Black), Hispanic ethnicity of any race, non-Hispanic Native Hawaiian/Pacific Islander (hereafter referred to as Native Hawaiian/Pacific Islander), non-Hispanic White (hereafter referred to as White), and non-Hispanic other (hereafter referred to as other ). The NCDB does not specify what groups are included in “other race.” Detailed information on the categories of race that compose the 6 overarching groups is provided in the eAppendix in Supplement 1 .

Additional covariates included age at diagnosis (continuous), Charlson-Deyo comorbidity score (0, 1, or ≥2), health insurance (none, private, Medicaid, Medicare, other government), area-level annual income (<$46 277, $46 277-$57 856, $57 857-$74 062, and ≥$74,063), area-level educational attainment (measure of the percentage of adults who did not graduate from high school; ≥15.3%, 9.1%-15.2%, 5.0%-9.0%, and <5.0%), metropolitan status (large metropolitan county [population >1 million], medium metropolitan county [population 250 000-1 million], small metropolitan county [population <250,000], urban, and rural), facility location (Northeast, Midwest, Mountain, Pacific, and South), facility type (community cancer, comprehensive community cancer, academic or research, and integrated network cancer), surgery (yes or no), chemotherapy (yes or no), radiation (yes or no), cancer stage (I, II, III, and IV), and tumor grade (1, 2, or 3; applicable for uterine and ovarian endometrioid and ovarian serous only). Additional details regarding area-level income, area-level education, metropolitan status, and cancer stage are provided in the eAppendix in Supplement 1 .

In the NCDB sample, we used multivariable logistic regression to estimate adjusted odds ratios (ORs) and 95% CIs for associations of race and ethnicity with clinical trial enrollment. Factors included as covariates comprised patient, facility, tumor, and treatment characteristics that have been identified as factors related to clinical trial enrollment among patients with cancer 4 and were available in NCDB.

To evaluate the racial and ethnic composition of patients with gynecologic cancer enrolled in clinical trials (in the NCDB) relative to the racial distribution in the overall cancer-specific population, we calculated the PPR according to period of diagnosis (2004-2011 vs 2012-2019). 15 The PPR was calculated by dividing the race-specific percentage of clinical trial participants in the study sample (eg, percentage of NCDB patients with endometrial cancer enrolled in clinical trials who are Black) by the percentage of racial and ethnic groups in the US patient population (eg, percentage of US patients with endometrial cancer who are Black) according to cancer site. We used the Surveillance, Epidemiology, and End Results *Stat program to derive population-based race and ethnicity frequencies for each cancer site. We omitted calculations for American Indian/Alaska Native, Native Hawaiian/Pacific Islander, and other women owing to low numbers. Stratification of the PPR by diagnosis period (2004-2011 vs 2012-2019) was done to qualitatively assess clinical trial enrollment over time. We evaluated only 2 time periods to reduce the potential for small numbers. PPRs less than 0.8 can be interpreted as underrepresentation in clinical trials, PPRs of 0.8 to 1.2 indicate adequate representation in clinical trials, and PPRs greater than 1.2 indicate overrepresentation. 15 Additional methodological details are presented in the eAppendix in Supplement 1 .

Statistical analyses were performed using Surveillance, Epidemiology, and End Results *Stat software version 8.4.1.1 (National Cancer Institute) and SAS statistical software version 9.4 (SAS Institute). All P values were 2 sided, with statistical significance set at P < .05. Analyses were performed from February 2 to June 14, 2023.

Among 562 592 women included (mean [SD] age at diagnosis, 62.9 [11.3] years), 1903 were American Indian/Alaska Native, 18 680 were Asian, 56 421 were Black, 38 145 were Hispanic, 1453 were Native Hawaiian/Pacific Islander, 442 869 were White, and 3121 were other race and ethnicity. Only 548 women (<1%) were enrolled in a clinical trial. In a multivariable-adjusted model, compared with White women, clinical trial enrollment was lower among Asian (OR, 0.44; 95% CI, 0.25-0.78), Black (OR, 0.70; 95% CI, 0.50-0.99), and Hispanic (OR, 0.53; 95% CI, 0.33-0.83) women but not significantly different for American Indian/Alaska Native (OR, 1.37; 95% CI, 0.43-4.36), Native Hawaiian/Pacific Islander (OR, 0.86; 95% CI, 0.12-6.16), or other race (OR, 0.48; 95% CI, 0.12-1.92) women ( Table ).

We also observed that older age at diagnosis (OR per 5-year increment, 0.89; 95% CI, 0.85-0.94) and having 2 or more comorbidities (OR, 0.56; 95% CI, 0.34-0.95) were associated with lower clinical trial enrollment odds. Area-level characteristics were related to clinical trial enrollment. Women living in zip codes with higher area-level income (quartile 4 vs quartile 1, OR, 0.65; 95% CI, 0.45-0.94) or living in zip codes with lower area-level educational attainment (quartile 4 vs quartile 1, OR, 0.41; 95% CI, 0.28-0.59) had lower odds of clinical trial enrollment. Clinical trial enrollment was lower among women living in small metropolitan (OR, 0.70; 95% CI, 0.49-0.99), medium metropolitan (OR, 0.73; 95% CI, 0.58-0.93), or urban (OR, 0.70; 95% CI, 0.51-0.96) counties but not different for women in rural counties (OR, 1.10; 95% CI, 0.55-2.16) compared with women residing in large metropolitan counties. Facility characteristics were related to clinical trial enrollment. Compared with treatment in the Northeast, those treated in the South (OR, 0.72; 95% CI, 0.57-0.90), Midwest (OR, 0.68; 95% CI, 0.53-0.87), or Pacific (OR, 0.71; 95% CI, 0.52-0.97) had lower clinical trial enrollment odds. Treatment at an academic or research program (OR, 6.26; 95% CI, 2.33-16.84) or an integrated network cancer program (OR, 2.93; 95% CI, 1.07-8.05) was associated with higher clinical trial enrollment odds compared with treatment at community cancer programs.

Over the study period, we observed higher clinical trial enrollment, with women who received a diagnosis between 2016 and 2019 being approximately 10 times more likely to be enrolled compared with those who received a diagnosis between 2004 and 2006 (OR, 10.18; 95% CI, 6.32-16.39). Patients with ovarian (OR, 3.70; 95% CI, 2.69-5.08) or cervical (OR, 4.30; 95% CI, 2.76-6.70) cancer were more likely to be enrolled in clinical trials than patients with endometrial cancer. Treatment with surgery (OR, 2.77; 95% CI, 1.94-3.96) or chemotherapy (OR, 2.78; 95% CI, 1.93-4.02) was associated with increased clinical trial enrollment odds, whereas women treated with radiation were less likely to be enrolled (OR, 0.63; 95% CI, 0.43-0.92).

PPRs according to race and ethnicity and diagnosis period and stratified by cancer site are shown in the Figure . Among patients with endometrial cancer, White and Black women were adequately represented or overrepresented (PPRs ≥1.1) in clinical trials in both time periods, with a slight decline in representation among Black women between the 2 time periods (2004-2011, PPR = 1.4; 2012-2019, PPR = 1.1). Asian and Hispanic women were inadequately represented during both time periods (PPRs ≤ 0.5). Among patients with ovarian cancer, White women were overrepresented during both time periods, whereas Asian, Black, and Hispanic women were underrepresented during both periods (PPRs ≤ 0.6). For cervical cancer, Black and White women were either overrepresented or adequately represented during both time periods, whereas Asian and Hispanic women were underrepresented. Further details of the PPRs are shown in eTable 2 in Supplement 1 .

In this retrospective cohort study of women with gynecologic cancer, we used 2 complementary approaches to evaluate racial and ethnic disparities in clinical trial enrollment. First, we examined clinical trial enrollment odds comparing participation among minoritized women with that of White women, with covariate adjustment. These analyses demonstrated lower clinical trial enrollment odds among Asian, Black, and Hispanic women compared with White women, but no difference in enrollment among American Indian/Alaska Native, Native Hawaiian/Pacific Islander, or other race women. In addition, social determinants of health, including area-level income and education, geographic region, and metropolitan status, along with certain facility characteristics, were associated with clinical trial enrollment. In the second analytic approach, analyses comparing the race-specific prevalence of clinical trial enrollment in the NCDB sample with the race-specific cancer prevalence in the US population with gynecologic cancer revealed interesting patterns. First, regardless of diagnosis period, Asian and Hispanic women with an endometrial, ovarian, or cervical cancer were underrepresented in clinical trials compared with the proportion expected on the basis of US cancer incidence. White women were either adequately represented or overrepresented for all 3 cancer sites, whereas patterns diverged for Black women: among those with endometrial or cervical cancer, adequate representation or overrepresentation was noted but among those with ovarian cancer, underrepresentation was evident. Together, these analyses provide novel information on the landscape of racial and ethnic disparities in gynecologic cancer treatment.

Prior studies 7 , 8 examining clinical trial representation among patients with gynecologic cancer have compared observed case counts of racial and ethnic groups from published trials (including trials registered through ClinicalTrials.gov, Gynecologic Oncology Group–based trials, 9 or National Cancer Institute–sponsored gynecologic cancer treatment trials 16 ) to the expected racial and ethnic count obtained from population-based age-adjusted incidence rates. In support of this body of work, we identified adequate representation or overrepresentation in clinical trials among White women with endometrial, ovarian, or cervical cancers along with underrepresentation of Black patients with ovarian cancers. Our findings that Black women with endometrial or cervical cancers were adequately or overrepresented in clinical trials are in line with the findings of 2 prior studies. 8 , 16 For example, Mattei and colleagues 8 reported that Black women with either a uterine or cervical cancer were proportionately enrolled in precision medicine trials, whereas Mishkin and colleagues 16 similarly noted no enrollment disparities for Black women with uterine or cervical cancer in National Cancer Institute–sponsored treatment trials. However, an evaluation of racial representation in Gynecologic Oncology Group–sponsored clinical trials revealed that enrollment of Black women was 9.8-fold lower than expected for endometrial cancer trials and 4.5-fold lower for cervical cancer trials. 9 Overall, although our PPR findings indicate that Black women are being enrolled in endometrial and cervical cancer clinical trials at levels proportionate to their distribution in the population, this practice of striving for proportional enrollment is unlikely to culminate in the sample sizes needed to make well-powered conclusions about treatment efficacy within minoritized groups. 17 Indeed, recent calls for equitable clinical trial inclusion suggest the need to recruit equal numbers of racial and ethnic groups, such that minoritized groups are overenrolled with respect to their size in the general population. A shift in this direction would allow ideally powered analyses of treatment effects within racial and ethnic groups. 18

Disparities between Black and White populations in gynecologic oncology have been frequently investigated; however, reports focused on other racial and ethnic groups are less common. Our PPR and logistic regression analyses showing underrepresentation and lower clinical trial enrollment odds of Asian and Hispanic women agree with data published by Mattei and colleagues, 8 where women in these groups were less commonly enrolled to precision oncology trials for ovarian and uterine cancer, with Hispanic women also less likely to be enrolled in cervical cancer clinical trials. Furthermore, in a review of National Cancer Institute–sponsored gynecologic oncology trials, Hispanic, but not Asian, women were less likely to be enrolled in ovarian, uterine, or cervical cancer clinical trials. 16 Because of the low numbers of Native Hawaiian/Pacific Islander, American Indian/Alaska Native, and other race patients, we were unable to provide meaningful estimates of clinical trial enrollment odds or PPRs for these groups.

Apart from race and ethnicity, other factors associated with clinical trial enrollment included the presence of comorbidities, which was related to lower odds of clinical trial enrollment, in line with prior work. 19 Although clinical trials traditionally exclude patients with medical comorbidities under the auspice of patient safety, in 2017 and 2021, the American Society for Clinical Oncology recommended broadening clinical trial eligibility to maximize generalizability. 20 , 21 In addition, older age; living in zip codes with higher income; living in zip codes with lower educational attainment; living in urban, small, or medium sized counties; and treatment in the South, Midwest, and Pacific (compared with the Northeast) were associated with lower clinical trial enrollment. Treatment at an academic or research program or an integrated network cancer program was associated with higher odds of clinical trial enrollment. Most of these associations were expected on the basis of prior literature 22 , 23 ; however, our finding that women living in areas with higher area-level income were less likely to participate in clinical trials was surprising. It is likely that area-level income also captures unmeasured neighborhood effects underlying this unexpected association. Future studies that also include individual-level income measures will be useful in contextualizing this association.

Our analyses are limited by the available data within the NCDB, because we lack information on important patient and oncologic characteristics. Certain data that can affect clinical trial enrollment, including trial phase sponsor or funding source, availability of clinical trials, trial treatments (and whether they ultimately ended up becoming standard of care), and physician characteristics, are unavailable. As such, unmeasured confounding is possible in this observational study. In addition, the NCDB does not provide contextual information on the specific therapeutic area of the clinical trial (eg, cardiovascular, endocrine, or oncology); however, we assumed that an indication of clinical trial enrollment pertained to patients’ gynecologic cancer diagnosis. Moreover, we lacked details that would allow us to assess racial and ethnic differences in the pathway to clinical trial enrollment, which is important for clarifying the required intervention. For example, if racial and ethnic differences in clinical trial recommendations are apparent, technology-based interventions that screen patients and automate trial matching might be warranted. 24 Alternatively, if we were to observe that women from minoritized racial and ethnic groups are more likely to reject clinical trials when offered, this might suggest a need for interventions aimed at the patient and physician levels to increase participation. Furthermore, because of missing values of key variables (eg, race and stage) within the NCDB data set, we excluded approximately 45% of the original sample to conduct a complete case analysis. This approach restricted the sample size, likely leading to imprecise estimates for American Indian/Alaska Native and Native Hawaiian/Pacific Islander women. It is imperative that future studies include women from these underrepresented groups to better define clinical trial disparities.

Despite these limitations, several important strengths warrant mention. First, we used a large cancer database to examine clinical trial enrollment, which is an infrequent event. Second, our analysis allowed for an internal comparison of women from different racial and ethnic groups with control for important covariates. Third, we relied on the PPR to frame representation, as opposed to age-adjusted incidence rates, which typically do not adjust for hysterectomy status, thus allowing for a potentially more accurate estimation.

In this cohort study of women with gynecologic cancer, we observed that Asian, Black, and Hispanic women had lower odds of being enrolled in clinical trials, whereas women from other minoritized groups did not experience differences in clinical trial enrollment when compared with White women. Comparisons of clinical trial enrollment in this study sample with the US population revealed underrepresentation of Asian and Hispanic women with all 3 types of gynecologic cancers, underrepresentation of Black women with ovarian cancer, adequate representation of Black women with endometrial and cervical cancers, and overrepresentation of White women with all 3 gynecologic cancer types. Further work aimed at understanding the race-specific barriers and facilitators that impact enrollment of gynecologic oncology patients in clinical trials is imperative. Although we noted lower clinical trial enrollment in multiple minoritized groups, the pathways leading to these outcomes are likely diverse and will require targeted interventions.

Accepted for Publication: October 24, 2023.

Published: December 7, 2023. doi:10.1001/jamanetworkopen.2023.46494

Corresponding Author: Ashley S. Felix, PhD, Division of Epidemiology, College of Public Health, The Ohio State University, 1841 Neil Ave, Cunz Hall 304, Columbus, OH 43210 ( [email protected] ).

Author Contributions: Dr Felix had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Khadraoui, Backes, Felix.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Khadraoui, Meade, Felix.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Meade, Felix.

Supervision: Backes, Felix.

Conflict of Interest Disclosures: Dr Backes reported receiving grants and personal fees from Merck, ImmunoGen, Clovis, and Eisai; grants from Beigene and Natera; and personal fees from CEC Oncology, AstraZeneca, GlaxoSmithKline, Agenus, UpToDate , Genentech, and Myriad outside the submitted work. Dr Felix reported receiving grants from the National Cancer Institute during the conduct of the study that were unrelated to the scope of work in this study. No other disclosures were reported.

Data Sharing Statement: See Supplement 2 .

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August 14, 2024

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New vaccine against cervical cancer combines prophylactic and therapeutic activity

by German Cancer Research Center

A new vaccine against cancer-causing human papillomaviruses (HPV) is intended to help increase the rate of HPV vaccinations, particularly in developing countries. Scientists at the German Cancer Research Center (DKFZ) have developed a completely new vaccination concept for this purpose.

The paper is published in the journal npj Vaccines .

The vaccine is inexpensive and protects mice against almost all cancer-causing HPV types. In addition to preventing new infections, the vaccine also triggers cellular immune responses against HPV-infected cells and may therefore also have a therapeutic effect against existing infections.

Cervical cancer caused by certain types of human papillomavirus (HPV) is the fourth most common cancer in women worldwide. The majority of cases are diagnosed in less developed countries, particularly in South East Asia, Africa and Latin America. The carcinogenic so-called risk HPVs are mainly transmitted during sexual contact. The infections are very common.

It is assumed that up to 80% of the population will come into contact with these viruses in their lifetime. In addition to cervical cancer , infections with high-risk HPV are also associated with oral cancer, anal cancer and other cancers of the genital organs.

The vaccines currently available against cancer-causing HPV are effective, but have limitations. They are temperature-sensitive and therefore require continuous refrigerated transportation, which poses a logistical problem in some countries. Their production is complex and expensive. In addition, they are only effective against certain cancer-causing HPV types. Above all, however, the established HPV vaccines show no therapeutic effects on existing infections.

In developing their new HPV vaccine, Müller and his colleagues took a systematic approach to solving all these problems. The basis for this was the "predecessor model" PANHPVAX, which was also developed in Müller's laboratory: this exclusively prophylactic vaccine has already proven to be safe in phase I clinical trials and induces protective antibodies against all cancer-causing HPV as well as against some cutaneous papillomaviruses.

For PANHPVAX, the researchers used small fragments of the L2 protein from eight different HPV types. These fragments differ only slightly between different HPV types and can therefore trigger a very broad immune response. To make these protein snippets immunogenic, they were inserted into a suitable scaffold protein derived from a heat-loving microorganism (Pyrococcus furiosus).

"In our current work, we have added a therapeutic component to PANHPVAX, i.e. an antigen that stimulates the cellular immune response," explains Müller. The DKFZ virologists chose the protein E7 of the two high-risk types HVP16 and 18. It is formed very early in the course of an HPV infection in the infected cells and is therefore an ideal target for a cellular immune response to eliminate these cells. However, E7 is also responsible for the malignant transformation of HPV-infected cells. The researchers therefore first had to modify the vaccine antigen so that it no longer posed a threat.

In preclinical studies , the new vaccine cPANHPVAX was able to trigger neutralizing antibodies against all carcinogenic HPV in mice and simultaneously activate cytotoxic T cells against the HPV16 protein E7.

These positive results encouraged the researchers to now produce cPANHPVAX under conditions that comply with Good Manufacturing Practice (GMP) guidelines for pharmaceuticals. The vaccine produced in this way can be used in clinical trials .

"Our major goal is to increase vaccination rates against HPV worldwide, especially in countries with limited resources. Our new, heat-stable vaccine is inexpensive to produce, protects against all cancer-causing HPV types and can potentially neutralize existing infections by combining it with E7," the researcher says.

In order to further investigate the promising properties of cPANHPVAX, the researchers are currently developing a concept for clinical testing of the vaccine.

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Advancing towards a novel, highly accurate method for cervical cancer screening

The method allowed researchers to identify novel biomarkers in cervical mucus samples with high diagnostic power

Fujita Health University

Identifying new biomarkers for cervical cancer

After a preliminary screening round, researchers identified a set of microRNAs (miRNAs) and cytokines that are abnormally expressed in serum and mucus samples in cases of cervical cancer. These biomarkers could soon be the basis of novel screening protocols for early detection.

Credit: Takuma Fujii from Fujita Health University

Cervical cancer is a highly prevalent cancer, with approximately 500,000 new cases diagnosed each year. Shockingly, the number of individuals diagnosed with precursor lesions in the cervix—also known as cervical intraepithelial neoplasia (CIN)—is 20 times higher. As with many potentially malignant conditions, early diagnosis of cervical cancer can make all the difference in a patient’s life in terms of treatment outcomes. For this, developing effective, convenient, and easily available screening protocols for CIN and cervical cancer is of paramount importance.

Currently, the two most widely used screening procedures for these conditions are human papillomavirus (HPV) test and cytology examination. While cytology is well established as a screening method in many countries, it has rather low sensitivity for detecting CIN. On the other hand, HPV tests are highly sensitive, but HPV infections do not always lead to cervical lesions, resulting in poor specificity. Given these drawbacks, the need for improved diagnostic methods is all the more necessary.

Against this backdrop, a research team led by Professor Takuma Fujii from Fujita Health University, Japan, aimed to identify biomarkers that could assist in the early detection of cervical cancer. In their latest paper published in Cancer Science on May 15, 2024, they report on a series of compounds that show abnormal expression in serum and cervical mucus samples of cervical cancer patients. These findings could potentially revolutionize disease prevention strategies.

Interestingly, the use of cervical mucus samples as part of a potential diagnostic tool was not initially planned. “ We wanted to investigate how changes in local immunity are related to cervical cancer, and so, we aimed to study all the currently known microRNAs (miRNAs) associated with the development and progression of cervical tumors, ” explains Fujii. Adding further, Fujii says, “ Initially, we focused on developing a serum-based diagnostic method for clinical use. However, we realized it would be better to first verify if molecular expression levels in the local tissue correlated with serum, assessing the feasibility of a serum diagnostic method. ”

To achieve these goals, the research team compared the miRNA and cytokine profiles from serum and mucus samples. These were collected from patients with cervical cancer or CIN who underwent routine gynecological examinations at Fujita Health University Hospital, over approximately eight years. Through initial screening, the researchers identified three candidate miRNAs and five candidate cytokines in serum, and five candidate miRNAs and seven candidate cytokines in mucus.

With the help of miRNA real-time PCR tests and cytokine immunoassay experiments on a larger sample size, the team verified the abnormal expression of these biomarkers on patients with cervical cancer at different stages of the disease. They subsequently evaluated the diagnostic potential of these compounds. Surprisingly, while miRNAs and cytokines in serum showed limited diagnostic accuracy, a specific combination of miRNAs and cytokines in mucus samples proved much more promising. This suggests that focusing on changes in local expression levels, rather than serum levels, may offer a superior diagnostic strategy.

“ Our study, for the first time, demonstrates that analyzing mucus samples can distinguish cervical tumors from normal tissues more accurately than serum samples. Using such a method as an additional option to traditional screening techniques could help discover cancer and precancerous conditions at an earlier stage, ” remarks Fujii.

Going ahead, however, further validation on larger populations is necessary to solidify these findings and pave the way for improved cervical cancer screening and diagnostic procedures. These advancements could reduce the need for invasive procedures such as colposcopy, which, in turn, would reduce the burden on patients and minimize healthcare costs.

Here’s hoping that with continued progress, early detection of cervical cancer becomes feasible, sparing women from the burden of this devastating disease.

Title of original paper: Performance of an ancillary test for cervical cancer that measures miRNAs and cytokines in serum and cervical mucus

Journal: Cancer Science

DOI: https://doi.org/10.1111/cas.16214

About Fujita Health University

Fujita Health University is a private university situated in Toyoake, Aichi, Japan. It was founded in 1964 and houses one of the largest teaching university hospitals in Japan in terms of the number of beds. With over 900 faculty members, the university is committed to providing various academic opportunities to students internationally. Fujita Health University has been ranked eighth among all universities and second among all private universities in Japan in the 2020 Times Higher Education (THE) World University Rankings. THE University Impact Rankings 2019 visualized university initiatives for sustainable development goals (SDGs). For the “good health and well-being” SDG, Fujita Health University was ranked second among all universities and number one among private universities in Japan. The university became the first Japanese university to host the "THE Asia Universities Summit" in June 2021. The university’s founding philosophy is “Our creativity for the people (DOKUSOU-ICHIRI),” which reflects the belief that, as with the university’s alumni and alumnae, current students also unlock their future by leveraging their creativity.

Website: https://www.fujita-hu.ac.jp/en/index.html

About Professor Takuma Fujii from Fujita Health University

Takuma Fujii is a Professor in the Department of Gynecology at the School of Medicine, Fujita Health University. He specializes in obstetrics and gynecology and has published more than 100 articles that collectively accumulated over 1,800 citations.

Funding information

This work was partly supported by KAKENHI from the Ministry of Education, Culture, Sports, Science and Technology, Japan (Grant No. 23K08812) and a Fujita Health University research Grant-in-Aid.

Cancer Science

10.1111/cas.16214

Method of Research

Observational study

Subject of Research

Article title.

Performance of an ancillary test for cervical cancer that measures miRNAs and cytokines in serum and cervical mucus

Article Publication Date

15-May-2024

COI Statement

The authors declare no conflict of interest.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Original Source

IMAGES

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(PDF) Prevention and early detection of cervical cancer in the UK
😂 Literature review on cervical cancer pdf. Perception of Cervical

COMMENTS

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Prevention Strategies and Early Diagnosis of Cervical Cancer: Current
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1. Introduction. Cervical cancer is a highly prevalent disease amongst females, associated with significant morbidity and mortality worldwide [].It is the fourth most common malignancy to affect women globally and responsible for approximately 850 deaths per annum in the United Kingdom (UK) [2,3].Cancer Research UK reported 3200 new cervical cancer cases in the UK annually between 2016 and ...
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Find research articles on cervical cancer, which may include news stories, clinical trials, blog posts, and descriptions of active studies. ... The rates of timely cervical cancer screening fell between 2005 and 2019, researchers found, and disparities existed among groups of women. The most common reason for not receiving timely screening was ...
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Research using Behavioral Risk Factor Surveillance System data found that rates of past-year cervical cancer screenings decreased from 58% in 2018 (prepandemic) to 52% in 2020. 1 Data from an electronic health records system showed that after the national COVID-19 public health emergency was declared, cervical cancer screenings decreased 94% ...
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Globally, 570 000 cases of Cervical Cancer and 311000 deaths from the disease occurred in 2018. Cervical Cancer is the fourth most common cancer in women, ranking after breast cancer (2.1 million cases), colorectal cancer (0.8 million) and lung cancer (0.7 million). 1 It is the 2nd most leading cause of female cancer among women aged 15-44 years in India.
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Effectiveness of cervical screening with age: population based case-control study of prospectively recorded data

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Introduction

Participants

Statistical methods

Association between screening and cervical cancer at different ages

Association between screening at different ages and cervical cancer by stage and histological type of cancer

Benefit associated with being screened twice by age 26

Screening classification of diagnoses in women before age 25

Strengths and weaknesses

Comparison with other studies

Interpretation of the results

Unanswered questions and future research

What is already known on this topic

What this study adds

Cervical Cancer Research

MINI REVIEW article

Introduction

Immunity and cervical cancer

Immune checkpoint inhibitors

First-line treatment and recent advances

Second-line treatment options

Conclusions

Recommendations

Author contributions

Conflict of interest

Publisher’s note

Tertiary lymphoid structures are associated with enhanced macrophage activation, immune checkpoint expression and predict outcome in cervical cancer.

Competing Interest Statement

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Cervical Cancer Detection Techniques: A Chronological Review

Shahrina Ismail

Fahirah Syaliza Mokhtar

Hiam Alquran

Yazan Al-Issa

Associated Data

1. Introduction

2. Materials and Methods

3. Review of the Study

3.2. 2016–2018

3.3. 2019–2020

3.4. 2021–2022

4. Discussion

5. Conclusions

Acknowledgments

Funding Statement

Author Contributions

Institutional Review Board Statement

Cone-Beam Computed Tomography (CBCT)-Guided Adaptive Boost Radiotherapy for a Patient With Locally Advanced Cervical Cancer Ineligible for Brachytherapy

36 References

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Epidemiology of HPV-associated cancers past, present and future: towards prevention and elimination

Pembrolizumab plus chemoradiotherapy effective in locally advanced cervical cancer

Non-inferiority of simple versus radical hysterectomy in low-risk cervical cancer

Neoadjuvant chemoimmunotherapy is effective in locally advanced cervical cancer

Benefit with cemiplimab in cervical cancer

Pembrolizumab tunes up chemotherapy in cervical cancer

SCRT for early stage cervical cancer

TIL infusions effective in HPV-associated cancers

Less invasive is not always better

Novel molecular subtypes of cervical cancer — potential clinical consequences

Therapeutic HPV vaccine holds promise

HPV-FASTER: broadening the scope for prevention of HPV-related cancer

Less than three doses of HPV-16/18 prevents HPV infection

Squamocolumnar junction ablation—tying up loose ends?

New standard of care—HPV testing for cervical cancer screening

Survival benefit and quality of life

From ENA 2014

Further delineating bevacizumab's response spectrum

Screening comes of age and treatment progress continues