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• Clinical Journal of Oncology Nursing
• Number 6 / December 2021
• Supplement, December 2021, Survivorship Care

Prostate Cancer: Survivorship Care Case Study, Care Plan, and Commentaries

Michelle Delcioppo

Mary L. Schmitt

Joanna Bodmann

This case study highlights the patient’s status in care plan format and is followed by commentaries from expert nurse clinicians about their approach to manage the patient’s long-term or chronic cancer care symptoms. Finally, an additional expert nurse clinician summarizes the care plan and commentaries, emphasizing takeaways about the patient, the commentaries, and additional recommendations to manage the patient. As can happen in clinical practice, the patient’s care plan is intentionally incomplete and does not include all pertinent information. Responding to an incomplete care plan, the nurse clinicians offer comprehensive strategies to manage the patient’s status and symptoms. For all commentaries, each clinician reviewed the care plan and did not review each other’s commentary. The summary commentary speaks to the patient’s status, care plan, and nurse commentaries.

Jump to a section

When delivering survivorship care, a nurse may start with incomplete information about the cancer survivor’s treatment and follow-up. Therefore, this prostate cancer package includes an incomplete case study and care plan, and then follows with commentaries from expert nurses about their approach to comprehensive survivorship care.

J.M. is a 70-year-old Korean American man with a history of prostate cancer who has been treated with radiation therapy and is undergoing androgen receptor inhibitor therapy. Electronic health record notes also suggest that J.M. may be a poor historian (see Figure 1).

•  Electronic health record notes state that J.M. says he has had multiple aunts who he believes were diagnosed with breast cancer. He also believes his father had pancreatic cancer (died at age 57 years).

•  J.M. has had careful monitoring because he has been diagnosed with diabetes. His hemoglobin A1c has been greater than 6.5, but he has made significant progress in lowering it. He started an exercise program and reduced his hemoglobin A1c from 6.8 to 6.1 as of last month.

•  He is retired and lives with his wife of 40 years, who is showing signs of early-stage dementia.

•  Blood pressure: 160/51

•  Pulse: 57 beats per minute

•  Temperature: 97.8°F (36.6°C)

•  Weight: 115.2 kg (254 lbs)

•  Oxygen saturation: 96%

•  Body mass index: 38.62 kg/m²

•  Other medical issues

•  Coronary arteriosclerosis in native artery; essential hypertension

•  Benign hypertensive heart disease without congestive heart failure

•  Tobacco history: 1.5 packs per day for 15 years (22 pack years; 1964–1979)

•  Medication list

•  Aspirin: 81 mg by mouth two times daily

•  Atorvastatin: 40 mg by mouth at bedtime

•  Fluticasone propionate: 50 mcg per nasal spray at bedtime

•  Hydrochlorothiazide: 12.5 mg by mouth every morning

•  Losartan: 50 mg by mouth two times daily

•  Vitamin D 3 : 25 mcg (1,000 U) by mouth daily

Challenges and Questions

•  A focus of care is the patient’s understanding of his goals of care and quality of life. He reports fatigue, difficulty sleeping, incontinence, bone pain, sexual dysfunction, weight changes, and hot flashes.

•  Assess the patient’s tolerance to the prescribed treatment with androgen receptor inhibitors and his understanding of the side effects of treatment.

•  An updated prostate-specific antigen (PSA) test is needed to determine the status of his disease.

•  His body mass index of 38.62 kg/m 2 and obesity-related health conditions (i.e., diabetes and hypertension) place him in the morbid obese category.

•  His chief complaints and ongoing concerns include bone pain, fatigue, difficulty sleeping, alteration in genitourinary and sexual function, hot flashes, and weight changes.

•  Pay attention to bone pain as a new symptom or if localized or as a result of an injury. Patients undergoing androgen-deprivation therapy (ADT) require close monitoring for osteoporosis and the development of fractures. If pain is associated with point tenderness on physical examination or in the context of a rising PSA level, further bone imaging and possibly a metastatic workup should be ordered. Risk of pathologic and nonpathologic fracture is associated with traditional and second-generation androgen receptor inhibitors.

•  Other ongoing symptoms associated with ADT and/or radiation therapy include fatigue, alteration in sexual function, alteration in urinary function, increased risk for diabetes and heart disease, psychological distress, sleep disturbance, and hot flashes.

Commentary 1

Michelle delcioppo, msn, aprn, agpcnp-c.

Perform a complete physical assessment, including palpating nodal chains of the head and neck for adenopathy and the thyroid for nodules. A fundoscopic examination is recommended because eye metastasis, although rare, is possible. I recommend that the patient see an ophthalmologist annually for a complete examination because diabetes increases his risk of diabetic retinopathy.

Laboratory studies include a complete blood cell count with differential to evaluate possible anemia, complete metabolic panel, lipid profile, and fasting blood glucose levels. ADT increases the patient’s risk of developing metabolic syndrome, which J.M. has developed. Monitor testosterone levels and vitamin D levels because of their relation to the patient’s bone health. A computed tomography scan can be considered to assess lymph nodes and viscera, as dictated by clinical presentation.

J.M. reports fatigue, which could be a result of low testosterone levels. An evaluation of his thyroid function with laboratory tests, including thyroid-stimulating hormone, free T3, and free T4, may yield helpful information to evaluate his fatigue. Other causes of fatigue, such as anemia and adrenal insufficiency, can be evaluated with a complete blood cell count with differential and cortisol level.

He reports difficulty sleeping. I would recommend a sleep study to evaluate for possible sleep apnea for which he is at an increased risk of developing because of his metabolic syndrome and high body mass index. Undiagnosed and unmanaged sleep apnea has long-term implications on his diabetes and coronary artery disease.

ADT increases his risk of osteopenia, osteoporosis, and bone fracture because of the loss of bone mineral density. A dual-energy x-ray absorptiometry (DEXA) scan should be performed every two years for surveillance purposes. Bisphosphonates are indicated with a diagnosis of osteopenia or osteoporosis. Obtaining a thorough history of his bone pain can determine if this is acute pain or chronic pain, predating his cancer diagnosis. Depending on the patient’s description of his bone pain, an x-ray can be ordered for further evaluation.

He reports starting an exercise program and has reduced his hemoglobin A1c from 6.8 to 6.1 in the past month. Assessing his level of physical activity can allow the advanced practice nurse to offer recommendations regarding exercise guidelines to include weight-bearing exercises. A referral to a dietitian is indicated because of his history of diabetes. I would ensure that the patient is being followed by an endocrinologist and primary care provider for his diabetes and hypertension.

Initial Patient Teaching

It is important that patient teaching include signs and symptoms of disease progression, such as pain, fatigue, poor appetite, hematuria, and weight loss. Time should be spent talking through the side effects of treatment so that the patient has a clear understanding of normal side effects of treatment and what symptoms would be cause for further workup.

I would also include education about the importance of genetic testing because this has not been completed. His familial history, including breast and pancreatic cancer, is concerning for possible BRCA genetic pathogenic variants. In fact, evidence shows that about 90% of patients with metastatic castration-resistant prostate cancer have clinically actionable germline and somatic alterations. Inherited BRCA genetic pathogenic variants are associated with an increased risk of developing breast, ovarian, prostate, and other cancers. By confirming actionable pathogenic variants, clinicians may be able to suggest treatment options. Discovering a germline pathogenic variant also has implications for other family members who should be tested.

Follow-Up Assessment and Patient Teaching

Depending on J.M.’s disease stability, his follow-up schedule is every month to every three months. Assessment focuses on a comprehensive physical examination and report of any side effects and any new symptoms of concern. His PSA level is monitored at each visit. The velocity and doubling time of his PSA level are critical to understand his cancer prognosis. Colorectal cancer screenings are indicated, and lung cancer screening is appropriate because of his history of tobacco use.

Additional Information

Consistent follow-up allows providers to manage J.M.’s disease and symptoms. I would encourage a family member to participate in office visits for additional support. Using a palliative care nurse practitioner to assist in managing long-term symptoms has provided our patient population with an additional level of care that supports their quality of life and understanding of the disease.

Commentary 2

Mary l. schmitt, ms, fnp-bc, aprn, aocnp ®  , patient education following initial assessment.

Included in the education of patients, families, and other caregivers is the importance and management of bone health in prostate cancer. Bone health may be affected by ADT during and following treatment, increasing the risk of osteoporosis and subsequent bone fracture. For all men on ADT, clinicians provide education about the importance of calcium and vitamin D supplements, diet, avoidance of alcohol and tobacco, and exercise. For all men on ADT, dietary calcium intake (food and supplements) of 1,000–1,200 mg daily and supplemental vitamin D of 800–1,000 U daily are indicated (National Osteoporosis Foundation, 2014).

Exercise is a key component of this patient’s survivorship plan. For optimal bone health, patients are provided with a safe and structured exercise routine that focuses on safety, weight bearing, and/or resistance. Benefits include reduction of hemoglobin A1C and improvement in management of cardiovascular health. It may also improve fatigue, hot flashes, sleep disturbance, and bone health.

During all survivorship appointments scheduled at least every six months with PSA level monitoring, patients should be educated about the potential urinary and sexual side effects of radiation therapy and ADT and encouraged to speak about concerns and symptoms. As part of adult cancer survivor assessment, clinicians should inquire at regular intervals about the patient’s sexual function and any concerns or distress. Unless directly asked, survivors may not volunteer this information. When survivors report distress, education includes the causes of these symptoms and treatment modalities available.

Identification of a hereditary cancer has important implications for treatment decisions, early detection and risk-reduction measures for secondary cancer, and identification of relatives who could potentially benefit from counseling and testing.

Follow-Up Assessment and Patient Education

With each follow-up appointment, assessment for side effects of ADT is indicated. Side effects include fatigue, hot flashes, weight gain, sexual and urinary dysfunction, and depression. Education covers the causes of these symptoms and treatment options, such as pharmacologic and nonpharmacologic management. Clinicians can refer patients to cancer rehabilitation programs, psychotherapy, sexual or couples counseling, urology, or a sexual health specialist.

This patient is at risk for psychological distress and depression, particularly with growing concerns about his wife’s health. It may be helpful to encourage him to speak about his worries and fears. Cancer often changes roles and responsibilities within a family dynamic. To support this patient and family, the clinician should refer him to an oncology social worker, support group, or mental health counseling. Support groups (in person or online) are valuable for peer support. Local chapters of the American Cancer Society provide information and referral resources.

Data, Screening, and Laboratory Results to Establish as Baseline

Prior to initiating ADT therapy, assess vitamin D levels, and order supplementation to keep levels between 30 ng/ml and the upper limit of normal. For men undergoing long-term ADT, order a baseline bone mineral density imaging study (DEXA scan) and the Fracture Risk Assessment (FRAX ® ) score calculation (Centre for Metabolic Bone Diseases, n.d.). This tool provides an estimate of the 10-year probability of hip and other fractures. Using FRAX with a baseline DEXA scan would have been optimal for this patient prior to initiating ADT to assess risk and implement an appropriate exercise program.

Obtain and document a full family history of cancer. A referral to a genetics professional may assist patients with obtaining important personal and family medical history and could provide necessary education prior to germline testing to include the most appropriate tests.

Strategies for Success

To best manage bone health for this patient, order a DEXA scan and vitamin D level (if needed), and refer to physical therapy. A referral to a physical therapist will provide recommendations for a safe and structured exercise program, with a focus on safety, weight bearing, and/or resistance. Schedule a follow-up appointment with the patient, either in person or via telephone, to review test results and physical therapy recommendations. This should be documented in the health record and may be added to the treatment summary.

Management of Patients With Long-Term Symptoms

Patients often focus on the disease surveillance portion of their plan, such as tumor markers and imaging, as the primary reason for survivorship appointments. It is up to the clinician to ask the right questions to recognize long-term symptoms and to teach patients that there may be evidence-based treatments available that improve quality of life. Support groups may be extremely beneficial to survivors; often, they are an important first step if patients are feeling overwhelmed or anxious because patients may be more willing to share feelings with a peer survivor. Cancer rehabilitation programs are becoming more readily available in local communities, with evidence-based treatment for many late effects of cancer treatment.

Summary Commentary

Joanna bodmann, msn, aprn-cnp, aocnp ® .

The commentaries provide similar, comprehensive perspectives on the management of J.M.’s survivorship care. As Commentary 2 points out, survivorship care is much more than a checklist. Oncology advanced practice nurses who provide survivorship care must be well versed in their specialty, but they must also understand how that care affects the patient’s other medical conditions. As Commentary 2 points out, clinicians who ask the right questions can appropriately assess the effects of treatment. Repetition and experience can help advanced practice nurses hone those skills. There can be variations in appropriate care provided, which may change based on the responses given by the patient. Those variations do not necessarily mean there is a right or wrong approach.

Appropriate assessment, management, and education are all critical components of survivorship care. Both commentaries stress the importance of first addressing any symptoms or complaints that may be related to possible disease progression or recurrence. In this case, evaluation of J.M.’s bone pain was recognized as a priority. A secondary focus is late and long-term side effects of treatment. J.M. has several concerns, including fatigue, difficulty sleeping, incontinence, hot flashes, sexual dysfunction, and weight gain, all of which may affect his overall quality of life and are a result of his treatment and current therapy. Commentary 2 notes that each visit requires reassessment of treatment-related side effects. Then, to address them, the clinician provides patient education, pharmacologic and nonpharmacologic management, and appropriate referrals.

Neither commentary included specific pharmacologic interventions to address any of these symptoms. If initial symptom management strategies are ineffective, specialist referrals to urology, endocrinology, or palliative medicine may reveal better management strategies. Both commentaries address concerns related to ongoing bone health, including calcium and vitamin D supplementation, exercise, and DEXA scans.

When the patient has multiple concerns, the clinician may have difficulty prioritizing issues that require immediate attention. In this case, an advanced practice nurse might choose to prescribe a medication that may address hot flashes and improve sleep quality and fatigue simultaneously. Arguably, a nonpharmacologic intervention, such as acupuncture or weight loss, may provide the same solution. Depending on the patient, a particular strategy may be perceived as unreasonable to the patient. Therefore, in discussion with the patient, focus on the patient’s most bothersome side effects.

Oncology care has boundaries, and those boundaries can be difficult to establish. For example, although J.M.’s treatment may predispose him to weight gain, he may have been overweight prior to his cancer diagnosis. The patient’s primary care provider may be following his hemoglobin A1C, lipids, or cortisol levels; in collaboration with primary care, the advanced practice nurse keeps to their scope of practice. Patient education is critical; the advanced practice nurse can empower patients to ask appropriate questions of their other providers. For example, referrals to a dietitian or physical therapist may be indicated. The advanced practice nurse can also provide education and encourage the patient to follow up with their primary care provider for some of J.M.’s other concerns.

Preventive care is a focus of survivorship care. Both commentaries recognized the importance of consultation with a genetic counselor. Because of J.M.’s personal history and possible family history of breast and pancreatic cancer, J.M. and his first-degree relatives may carry a germline pathogenic variant; this has implications for treatment and long-term surveillance. As Commentary 1 notes, because of J.M.’s comorbidities, including obesity, diabetes, and hypertension, and his ongoing treatment, J.M. is at an increased risk of developing cardiovascular disease or diabetes. Whether through the care of specialists or his primary care provider, the clinician makes referrals so that J.M. is followed for his comorbid conditions. The clinician also ensures that J.M. is screened for other cancers, including colorectal cancer and other malignancies for which he has a higher risk because of pathogenic genetic variants or other associated predispositions.

Priority of care is focused on physical concerns, but psychosocial concerns deserve attention as well. Commentary 1 suggests the involvement of a social worker to help with J.M.’s increased stress related to his wife’s declining health. J.M. may also benefit from a support group to connect with patients undergoing similar treatments and side effects. As noted in Commentary 1, J.M. may benefit from the support of different or additional family members at his office visits.

Another important aspect of survivorship care is education. When educating the patient, clinicians can establish follow-up care guidelines and set realistic expectations. Commentary 1 suggests scheduling laboratory tests and follow-up visits at an interval different than the survivorship care plan provided. It can be confusing when the care plan and verbal expectations do not match. Many patients benefit from hearing the same recommendations reinforced at multiple visits, whether about the management of a side effect or their treatment plan or prognosis. The care of patients with cancer, including J.M., can be incredibly complex, even when ongoing care seems uncomplicated.

Clinicians can be challenged in their attempt to manage too many problems. Oncology can be a specialty where the lines may be blurred between management of the side effects of treatment and the impact on a patient’s comorbid conditions. J.M.’s difficulty sleeping could be because of undiagnosed sleep apnea. Therefore, first address treatment-related side effects; if persistent, refer back to primary care for ongoing evaluations and management. Similarly, although the patient has a history of prediabetes, the clinician can refer management and treatment to primary care. During a follow-up visit, many issues require attention; simple follow-up visits can easily turn into lengthy, comprehensive visits.

As demonstrated by the unique perspectives in the survivorship care of J.M., there can be multiple ways to appropriately manage the same patient. Providers may have specific areas of focus and side effects that they are more comfortable managing. Oncology advanced practice nurses can take their cues from patients to address new and ongoing concerns. Some problems may not have solutions, or some problems that affect oncology care are best managed by other medical providers. To provide good survivorship care, clinicians provide ongoing patient education and communicate well with other medical providers.

Additional Considerations

Suzanne m. mahon, dns, rn, aocn(r), agn-bc, faan.

•  The patient’s electronic health record documents a complete treatment summary, with staging, histology, Gleason score, PSA test results, and prior imaging, as well as radiation dose and start date for each ADT medication. Comprehensive care is challenging without this information, so it should be obtained prior to the visit. It also enables the clinician to focus on the most pressing issues first and determine whether the current therapy and follow-up plan is appropriate. A more complete plan can also be shared with the primary care provider.

•  Guidelines for follow-up change over time. It is important to review this information with patients. There are algorithm-based guidelines for providers (NCCN, 2021b) that are provided in bullet form for patients (NCCN, 2020). Sharing this information with patients and primary providers helps ensure that recommendations for care are current and evidence-based.

•  It is important to establish who is ordering tests, such as PSA, and managing issues. In addition, primary care requires an awareness of multiple medications and their contradictions. Polypharmacy is typically defined as taking five or more medications on a regular basis. J.M. has issues that are likely associated with his treatment, as well as medical issues that may be related to lifestyle, aging, or other risk factors. This requires careful communication between the oncology team and primary care. An oncology pharmacist may be helpful. Instruct the patient about who will be managing which issues.

•  Cancer survivors present with multiple concerns that need to be addressed, and determining the priority can be challenging. Sometimes there is not enough time to address all issues in a single visit. Telehealth provides a means to address issues in more depth, particularly when an intensive physical examination is not required. It also provides a way to check progress on previous issues and an opportunity to discuss wellness and health promotion, which often take lower priority. Dedicating a telehealth visit to something like the importance and benefits of diet and exercise sends a powerful message about its importance. Telehealth can also make survivor services more accessible for patients that live in areas with more limited healthcare resources.

•  NCCN (2021a) guidelines now recommend genetic testing for men with high-risk prostate cancer, including metastatic disease or a Gleason score of 7 or greater.

•  Urinary incontinence can be a short- and long-term consequence of treatment for prostate cancer. This requires regular assessment and intervention whenever possible to improve quality of life.

•  Screening guidelines change for the general population. This case exemplifies why keeping up with these guidelines is important. Along with the general public, cancer survivors follow cancer screening guidelines and comprehensive risk assessment; for cancer survivors, screening can detect second cancers early when treatment is most likely to be effective.

•  For patients on long-term endocrine-blocking therapy, clinicians assess side effects. If the side effects are substantial, it can affect adherence and long-term survival.

•  Treatment for prostate cancer can affect sexuality, which can be directly assessed. The patient can then be offered intervention, if appropriate.

About the Author(s)

Michelle Delcioppo, MSN, APRN, AGPCNP-C, is a nurse practitioner at Charleston Oncology in South Carolina. The author takes full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Delcioppo can be reached at [email protected] , with copy to [email protected] . (Submitted June 2021. Accepted August 26, 2021.)

Mary L. Schmitt, MS, FNP-BC, APRN, AOCNP ® , is an oncology nurse practitioner at St. Joseph Hospital in Nashua, NH. The author takes full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Schmitt can be reached at [email protected] , with copy to [email protected] . (Submitted June 2021. Accepted August 26, 2021.)

Joanna Bodmann, MSN, APRN-CNP, AOCNP ® , is a nurse practitioner at the Cleveland Clinic Taussig Cancer Institute in Ohio. The author takes full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Bodmann can be reached at [email protected] , with copy to [email protected] . (Submitted June 2021. Accepted August 26, 2021.)

Suzanne M. Mahon, DNS, RN, AOCN ® , AGN-BC, FAAN, is a professor in the Department of Internal Medicine in the Division of Hematology/Oncology and in the Trudy Busch Valentine School of Nursing at Saint Louis University in Missouri. The authors take full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Mahon can be reached at [email protected] , with copy to [email protected] . (Submitted June 2021. Accepted August 26, 2021.)

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Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study

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• Published: 30 July 2024

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• Rossano Girometti   ORCID: orcid.org/0000-0002-0904-5147 1 ,
• Valeria Peruzzi 1 ,
• Paolo Polizzi 1   nAff5 ,
• Maria De Martino 2 ,
• Lorenzo Cereser 1 ,
• Letizia Casarotto 3 ,
• Stefano Pizzolitto 3 ,
• Miriam Isola 2 ,
• Alessandro Crestani 4 ,
• Gianluca Giannarini 4   na1 &
• Chiara Zuiani 1   na1  

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To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives.

One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions.

csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9–77.6/54.5–70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5–84.5/59.4–77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only.

Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

Graphical Abstract

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Introduction

Since its introduction in 2012 [ 1 ] and revision as version 2 in 2015 [ 2 ], the Prostate imaging reporting and data system (PI-RADS) has become the most widely accepted standard for interpreting multiparametric magnetic resonance imaging (mpMRI) of the prostate. Current version 2.1 [ 3 ], released in 2019, has been validated by several studies [ 4 , 5 , 6 , 7 , 8 ] and, according to a recent metanalysis, shows pooled positive predictive value (PPV) for clinically significant prostate cancer (csPCa) of 16, 59 and 85% for PI-RADS 3, 4 and 5 category, respectively [ 9 ]. Though the PI-RADS promotes a standardized lesion-based scoring approach, interpretation remains subjective in several instances, thus explaining its moderate inter-reader agreement only with version 2 and 2.1 [ 10 , 11 ]. Current limitations of version 2.1 also include the need to clarify some interpretation criteria, lack of definite criteria for scoring the central zone, lack of assessment of the prostate background potentially affecting cancer detection [ 12 ] and, importantly, still limited specificity translating in too many unnecessary biopsies [ 7 ].

Not surprisingly, the PI-RADS is not of universal use in the setting of initial diagnosis of csPCa. While the joint societies' European guidelines endorse it with a "strong" strength rating [ 13 ], other recommendations favor the Likert score as the preferred alternative for reporting prostate MRI [ 14 , 15 ]. Comparably to the PI-RADS, the Likert score expresses the risk that a mpMRI observation is a csPCa on an ascending 1–5 scale, though this system works as a gestalt subjective assessment not relying on a dominant sequence or specific criteria to define each risk category [ 16 ]. This allows for much flexibility when interpreting findings that are difficult-to-categorize with the PI-RADS, and the possibility to take clinical information into account, e.g., age, prostate-specific antigen (PSA) level, PSA density (PSAD), family history, and so on [ 16 , 17 ].

A few studies comparing both systems on an intra-patient basis found that the Likert score has the potential for greater diagnostic accuracy [ 18 ] and improved specificity compared to PI-RADS version 2 [ 19 ]. This suggests the potential for maximizing cancer detection while avoiding unnecessary biopsies, which still represent the Achilles's heel of prostate mpMRI [ 20 ]. On the other hand, the absence of standardized rules of image interpretation translates into its dependence on the radiologist's experience [ 16 , 18 ] and limited potential for reproducibility across different institutions and practice settings compared to the relatively objective PI-RADS [ 16 ]. A recent British audit of cancer yields after prostate MRI found PI-RADS version 2 and the Likert score clinically equivalent, with most discrepancies confined to the PI-RADS 4 category [ 21 ]. Given the difficulty in establishing the superiority of one system over the other, we hypothesized that a two-step combined use of both systems could maximize the related advantages while minimizing disadvantages, thus potentially improving the diagnostic performance, especially in terms of reducing false-positive cases. We assumed that while the PI-RADS version 2.1 can represent the basis for reporting (first step), the radiologist could refine lesion categorization with the Likert score in all those selected cases in which the PI-RADS is perceived as not fully catching the complexity of risk assessment (second step).

This study aimed to assess whether the above-mentioned case-by-case strategy of combining the PI-RADS version 2.1 categories with the Likert score reduces the number of false-positive cases for csPCa and the appropriateness of mpMRI-informed biopsy decisions.

Material and methods

Study population and standard of reference.

The Institutional Review Board approved this monocentric study. The acquisition of informed consent was waived because of the retrospective design.

We searched the institutional database for all consecutive ≥ 18-year-old men who underwent prostate mpMRI followed by prostate biopsy between January 2020 and April 2021. Indications to mpMRI were clinical suspicion of csPCa (PSA value ≥ 3 ng/ml and/or positive digital rectal examination) in biopsy-naïve men or persistent clinical suspicion of csPCa despite one or more prior negative prostate biopsies. We identified144 eligible subjects who received prostate through the transperineal route by one of three urologists using software-assisted mpMRI-ultrasound guidance (Applio 300, Toshiba/Canon). The biopsy included 4 target cores (2 in-target and 2 peri-target) on PI-RADS ≥ 3 lesions, followed by 12 systematic cores. Per internal policy, patients with PI-RADS 1–2 examinations and high clinical risk received only systematic biopsy. After excluding 10 men because of the exclusion criteria shown in Fig.  1 , we used freely available software ( https://www.randomizer.org/ ) to randomly select 100 over the remaining 134 men as the final study population (Fig.  1 ). This number of patients was defined in advance when planning the study as a balance between the available time for performing the readings and the study duration. All included men were Caucasian.

Study flowchart. BCG = bacillus Calmette-Guérin; mpMRI = multiparametric magnetic resonance imaging; TURP = transurethral resection of the prostate

The standard of reference was represented by ISUP-compliant histological examination performed on biopsy cores [ 22 ] by one of three genitourinary pathologists (5–30 years of experience). csPCa was defined as a lesion showing the highest ISUP grading group ≥ 2 on systematic or targeted biopsy.

Imaging protocol

Examinations were acquired on a 1.5 T (MAGNETOM Aera, Siemens Healthineers) or a 3.0 T MRI equipment (Achieva, Philips Medical Systems) in 13/100 and 87/100 cases, respectively. A 32-channel surface coil was used. All patients received preliminary cleansing enema and i.m. administration of 20 mg hyoscine butylbromide (Buscopan, Boehringer Ingelheim) as an antiperistaltic agent.

Acquisition parameters are detailed in Supplementary Tables 1 and 2. On the 1.5 T magnet, the maximum b-value in the second diffusion-weighted sequences was interpolated up to 1400 s/mm 2 . The apparent diffusion coefficient (ADC) map was built upon a monoexponential fitting of signal decay versus b-values of the first diffusion-weighted sequence (maximum b = 1000 s/mm 2 ). Dynamic contrast-enhanced imaging (DCE) was acquired intravenously after administering 0.2 mL/Kg of gadoteridol (Prohance, Bracco) at an injection rate of 3 ml/s using a remote-controlled power injector (Medrad Spectris Solaris EP). DCE series was presented as native images and subtracted ones.

Image analysis

Two readers independently analyzed images on a Picture Archiving and Communication System console (Suite Estensa, Ebit). Readers included one radiologist (R1) with an experience of > 3000 examinations (R.G.) and a non-experienced radiologist resident (R2) mentored by R1 during clinical activity (< 200 readings) (V.P.). A study coordinator (P.P.) showed them mpMRI examinations using a two-phase strategy.

In the first phase, blinded to clinical information, readers were allowed to report up to four lesions to be scored with the PI-RADS version 2.1 (“PI-RADS” from here on out) [ 3 ] and asked to clearly identify the index lesion as the one showing the highest PI-RADS category or the largest size in the case of more lesions with the same PI-RADS category. When readers found no lesions, the examination was assumed to include a PI-RADS 1 "index lesion" for analysis. In the second phase, the coordinator disclosed clinical data, including age, results from prior biopsy, if any, last PSA value, results of the digital rectal examination, prostate volume calculated in the original mpMRI report, PSAD, ongoing therapy with alpha-blockers if any, family history of csPCa, and symptoms if any. Based on those clinical features and depending on the mpMRI appearance, readers were then allowed to make case-by-case additional use of the Likert score according to the following rules: (i) combining the PI-RADS category of the lesions with a Likert score, e.g., to reinforce a level of suspicion (e.g., PI-RADS 3 combined with Likert 3 score) or instead upgrading or downgrading it (e.g., PI-RADS 3 combined with Likert 2 or PI-RADS 3 combined with Likert 4); (ii) identifying and categorize a new lesion and assign it a Likert score only. Using the Likert score was not mandatory, so readers were asked to explain reasons for doing so on a case-by-case basis, detailing the number and type of clinical variables and imaging findings that triggered combined scoring. Imaging features supporting Likert scoring were those summarized by Latifoltojar et al. in Supplementary Tables 5, 6 and 7 of their paper [ 17 ], as well as the PI-RADS descriptors for T2-weighted imaging, DWI and DCE [ 3 ]. Differently from the PI-RADS, we did not establish in advance which imaging or clinical feature should have been selected or privileged for image analysis, nor defined exact combinatory rules to achieve a certain Likert score. Readers were also free to integrate clinical information with no predefinite rules, except establishing that the PSAD value to be considered “suspicious” was 0.15 ng/mL/mL (not a standalone criterion for malignancy). Our strategy aimed at: (a) reflecting the subjective nature of the Likert system and facilitate the comparison with previous works on the same topic; (b) to prevent the risk of testing a set of combinatory rules rather than the properly said Likert score; (c) to prevent the risk that a set of definite combinatory rules could overinflate the performance of the less experienced reader.

The Likert score was assumed to express the risk that a mpMRI finding was a csPCa as follows: 1 = highly unlikely; 2 = unlikely; 3 = equivocal; 4 = likely; 5 = highly likely [ 21 ].

Statistical analysis

After observing non-normal data distribution with the Shapiro–Wilk test, we used the median and the interquartile range (IQR) to report continuous variables. Relevant proportions were coupled with 95% confidence intervals (95% CI). Descriptive statistics was also used to report how many lesions were found by R1 and R2 and how they were categorized with the PI-RADS and Likert scores.

Concerning PI-RADS categorization, we decided not to run an inter-reader agreement analysis because readers could detect different lesions. We then calculated the per-category rate of concordant categorizations, i.e., how many times R1 and R2 assessed the same index lesion as PI-RADS 1–2, PI-RADS 3, or PI-RADS 4–5 over the total number of index lesions scored with the same PI-RADS category.

Based on the rules of comparison between mpMRI results and prostate biopsy shown in Table  1 , we calculated the per-index lesion sensitivity, specificity, PPV and negative predictive value (NPV) for csPCa of two different biopsy strategies, as follows: (i) strategy 1 (PI-RADS categorization only), i.e., biopsying any index lesion categorized PI-RADS ≥ 3; (ii) strategy 2 (PI-RADS categorization combined with the Likert score), i.e., biopsying any index lesion categorized as PI-RADS ≥ 3 (in cases receiving PI-RADS categorization only) or Likert ≥ 3 (in cases receiving combined scoring). In strategy 2, the Likert categorization, when attributed, was considered dominant compared to the PI-RADS. E.g., a PI-RADS 2 lesion upgraded to Likert 4 was assumed to be biopsied, while a PI-RADS 3 lesion downgraded to Likert 2 was assumed to avoid biopsy. For analysis, newly identified lesions in reading phase 2 showing a Likert score greater than the PI-RADS category of the index lesion established in reading phase 1 were assumed to represent the index lesion for biopsy strategy 2.

The clinical impact of both biopsy strategies was assessed with the decision curve analysis [ 23 ], assuming that the reference "treat all" and "treat none" strategies meant to biopsy all men and biopsy none, respectively. Net benefit, i.e., the balance between the advantage of diagnosing true positives weighted for the harm of biopsying false positives, was calculated at disease threshold probabilities of 10, 15, 20, 25, and 30%, respectively.

Calculations were performed using commercially available software (MedCalc software bv, version 18.11.16), except for decision analysis, which was run on Stata using source codes freely available at https://www.mskcc.org/departments/epidemiology-biostatistics/biostatistics/decision-curve-analysis .

Study population

The median age of the men included was 66.0 years (IQR 61.0–72.0). The median serum PSA and PSAD were 6.44 ng/mL (IQR 4.85–8.94) and 0.11 ng/mL/mL (IQR 0.07–0.17), respectively. Seventy-nine/100 men were biopsy-naïve, while the remaining 21/100 showed previous negative biopsy. csPCa was found in 38/100 men (38%; 95% CI 29.59–46.41). Lesions included 17/38 ISUP 2 cancers (44.73%), 12/38 ISUP 3 cancers (31.57%), 7/38 ISUP 4 cancers (18.42%), and 2/38 ISUP 5 cancers (5.26%). Clinically insignificant cancer (ISUP 1) was found in 12/100 men (12%).

PI-RADS categorization

R1 and R2 reported 119 and 131 mpMRI findings on one hundred men, respectively. Table 2 summarizes the distribution of their PI-RADS categories. Index lesions were found in the peripheral zone and transition zone in 55/100 and 20/100 cases by R1 and 68/100 and 18/100 cases by R2, respectively. The remaining 25/100 cases (R1) and 14/100 cases (R2) were PI-RADS 1 "index lesions" not corresponding to definite mpMRI observations.

Readers identified the same index lesion in 66/100 cases (66%; 95% CI 50.40–69.60), providing the same PI-RADS categorization in 55/66 cases (83.3%; 95% CI 74.34–92.32). In particular, the rate of concordant categorizations was 15/55 (27.3%; 95% CI 15.50–39.04) for PI-RADS 1–2 assignments, 1/55 (1.8%; 95% CI 00.05–05.35) for PI-RADS 3 assignments, and 39/55 (70.9%; 95% CI 58.91–82.91) for PI-RADS 4–5 assignments. The eleven cases of discordant categorizations are detailed in Supplementary Table 3.

Combined PI-RADS-Likert score categorization

R1 and R2 provided combined PI-RADS-Likert categorization of the index lesion in 28/100 (28%; 95% CI 19.20–36.80) and 18/100 (18%; 95% CI 10.47–25.53) cases, respectively, as summarized in Fig.  2 and in Supplementary Table 4. The latter shows that, for both readers, the use of the Likert score was mostly supported by PSAD values and the results of DRE.

Distribution of cases in which reader 1 ( a ) and reader 2 ( b ) used the Likert score to complement the PI-RADS categorization of index lesions. FN = false-negative; FP = false-positive; PI-RADS = Prostate Imaging Reporting and data System version 2.1; TN = true negative; TP = true-positive

R1 assigned a Likert ≤ 2 score to PI-RADS ≤ 2 findings in 10/28 cases (35.7%) and a Likert ≥ 3 score to PI-RADS ≥ 3 findings in 9/28 cases (32.1%), suggesting that Likert scoring was used to reinforce the lesion risk in around two-thirds of cases (Fig.  3 ). The same trend was observed for R2, who assigned a Likert ≥ 3 score to PI-RADS ≥ 3 findings in 11/18 cases (61.1%). Most reinforcements of suspicious cases regarded PI-RADS 4 assignments (7/9 for R1 and 9/11 for R2). R1 observed no PI-RADS 5 or Likert 5 cases, while R2 upgraded 3 PI-RADS 4 lesions to Likert 5.

Case of Likert scoring by reader 1 reinforcing the level of suspicion of PI-RADS categorization in a 53-year-old man with a prostate-specific antigen level density of 0.08 ng/mL/mL and negative digital rectal examination. The index lesion in the right anterior peripheral zone of the midgland showed wedge-shaped mild hypointensity on the apparent diffusion coefficient map (arrow in a ) and wedge-shaped mild hyperintensity on b = 2000s/mm 2 image ( b ), slight hypointensity on T2-weighted imaging (arrow in c ) and early focal enhancement after contrast administration (arrow in d ). The lesion was assessed as PI-RADS 2 and Likert 2. Transperineal systematic biopsy cores in the same quadrant and adjacent quadrant showed gland atrophy/subatrophy and chronic prostatitis

The secondary main trend consisted in assigning a Likert ≤ 2 score to PI-RADS ≥ 3 findings, i.e., 7/28 (25.0%) cases by R1 and 6/18 (33.3%) cases by R2, respectively. Reclassification beneath the threshold for biopsy translated into a switch from false-positives to true-negatives in all cases (Fig.  4 ). In a minority of cases, R1 and R2 assigned a Likert ≥ 3 score to PI-RADS ≤ 2 lesions (2/28 and 1/18 cases, respectively), all of which were found to be false-positives at systematic biopsy. Reclassifications are shown in Fig.  2 . As an overall balance between the false-positive cases saved or induced by the use of the Likert score, strategy 2 could have avoided 5 and 4 unnecessary biopsies for R1 and R2, respectively.

Case of Likert-induced downgranding of lesion suspicion by reader 1 in a 62-year-old man. A mildly-hypointense atypical nodule in the left anterior transition zone of the midgland (arrow in a and b ) showed restricted diffusion with marked hyperintensity on b = 2000s/mm 2 image ( c ) and marked hypointensity on the apparent diffusion coefficient map ( d ), and was categorized as a PI-RADS 2 upgraded to 3. Based on prostate-specific antigen level density of 0.07 ng/mL/mL and negative digital rectal examination, reader 1 downgraded the level of suspicion to Likert 2. A targeted prostate biopsy showed chronic prostatitis

Diagnostic performance

The diagnostic performance of biopsy strategies 1 and 2 is shown in Table  3 . For both readers, strategy 2 translated into increased specificity and PPV while maintaining comparable sensitivity and NPV.

Concerning the clinical impact for R1, decision curve analysis (Fig.  5 ) showed greater net benefit of strategy 2 compared to strategy 1 over the whole range of disease probability, with net benefit values at 10, 15, 20, 25 and 30% of csPCa likelihood of 0.34 versus 0.33, 0.33 versus 0.32, 0.32 versus 0.30, 0.30 versus 0.29 and 0.29 versus 0.27, respectively. In the case of R2, the curves of strategy 2 and strategy 1 largely overlapped up to around 25% threshold probability, with comparable net benefit values at 10% (0.29), 15% (0.27), and 20% of csPCa likelihood, and greater net benefit values at 25% (0.23 versus 0.22) and 30% (0.20 vs. 0.19) disease probability.

Decision curve analysis for reader 1 ( a ) and reader 2 ( b ) (see the main text for details)

In this study, we observed that, when combining the PI-RADS version 2.1 categorization of prostate index lesions with a case-by-case use of the Likert score, R1 and R2 downgraded the risk of csPCa beneath the threshold actioning prostate biopsy in 25 and 33.3% of the reclassified cases, respectively. This translated into increased mpMRI specificity and PPV in diagnosing ISUP ≥ 2 prostate cancer, with no detrimental effect on sensitivity and NPV. While this trend was observed in both readers, the net benefit on decision curve analysis improved for R1 only, supporting previous observations that adequate reader experience is the prerequisite for using the Likert scale [ 24 , 25 ]. This is related to the fact that most experienced readers are able to account for additional clinical and imaging factors when interpreting mpMRI and, in turn, make image interpretation more flexible and nuanced. Of note, greater net benefit was observed across the whole range of csPCa probability in our population (79% biopsy-naïve men and 21% re-biopsy patients).

As far as we know, previous studies did not investigate a similar strategy but rather compared the PI-RADS (version 1 or 2) versus the Likert score as alternative systems for diagnosis [ 18 , 19 , 26 , 27 , 28 , 29 ]. In line with our results, one of those works by Zawaideh et al. on 199 men [ 19 ] found that, being equal the sensitivity (94%) and NPV (96%), the use of the Likert score translated into lower positive call rate, and in turn greater per-lesion specificity and PPV for ISUP ≥ 2 cancers than the PI-RADS version 2 (77 versus 66% and 66 versus 58%, respectively). Differently from Khoo et al. [ 18 ], we did not observe an increase in cancer detection rate since sensitivity remained stable for R1 (94.7%) or minimally dropped for R2 (from 86.8 to 84.2%). This is in line with the fact that the Likert score upgraded the PI-RADS risk in a very minority of cases, and most times inappropriately, e.g., 2/2 cases upgraded from PI-RADS ≤ 2 to Likert 3 and 1/2 cases upgraded from PI-RADS 3 to Likert ≥ 4 were false-positive for the most experienced reader. Our results suggest that the Likert-induced upgrading is expectedly rare and should be regarded with caution as a trigger for biopsy, though further studies should confirm this issue and assess how to overcome it.

Our findings are more directly comparable with those by Stevens et al. [ 30 ], who performed logistic regression to identify Likert findings predicting csPCa and, in turn, built a model to automatically adjust indeterminate PI-RADS 3 cases (version 2) with the Likert score. In the testing cohort, the adjustment translated into an increase in specificity from 30.3 to 74.9%, comparable to the one we observed for our most experienced reader when using strategy 2 (74.2%). However, we did not focus analysis on indeterminate cases only and showed lower PI-RADS 3 call rates (10.1% for R1 and 16.8% for R2) than those Authors (135/411 men, i.e., 32.8% in the building cohort, and 159/380 men, i.e., 41.8% in the testing cohort). One can assume that, even though the Authors' model determined a comparable increase in specificity and PPV in a validation study [ 26 ], our results are at lower risk of overestimation in favor of Likert-induced effects over the entire spectrum of PI-RADS categories.

A strength of our study is that we blinded readers to clinical information when reporting with the PI-RADS, thus eliminating those confounders that could have translated into using "modified PI-RADS categories" close to Likert ones in clinical practice and previous trials [ 26 ]. Using this strategy translated into accurate lesion risk reduction, and, in turn, false-positives, e.g., as occurred for R1 in seven PI-RADS ≥ 3 cases reclassified as Likert ≤ 2 which were found to be inflammation on prostate biopsy (Suppl. Table 4). Though in a different setting, our results compare to those by Zawaideh et al. [ 19 ], who observed significantly more Likert negative/PI-RADS positive than Likert positive/PI-RADS negative cases. Notably, both readers used the Likert score mainly to reinforce the level of suspicion already expressed by the PI-RADS category, in line with the fact that this system expands image analysis and risk stratification from the lesion-based level of the PI-RADS to a more comprehensive patient-level. In the case of R1, this occurred mostly to reinforce a PI-RADS ≤ 2 category as a Likert ≤ 2 score (10/28 reclassified cases), in accordance with a recent audit of cancer yield showing that negative mpMRIs are a major source of agreement between PI-RADS version 2 and Likert scoring [ 21 ]. One can hypothesize that selective reporting of both the PI-RADS and Likert score could help identifying those cases in clinical practice and research in which clinical information was determinant in shaping the above-mentioned “modified PI-RADS” categories. A more systematic assessment and quantification could be helpful to further refine PI-RADS categories and provide more nuanced risk stratification.

As this was a proof-of-concept study, we did not run a systematic analysis of how much reproducible a selective use of additional Likert scoring can be, and whether it can depend on lesion location (i.e., peripheral zone versus transition zone findings) or other factors, e.g., how much complete the available clinical information is at the time of mpMRI reporting. While the Likert score is not-standardized by definition, it was found to compare the diagnostic performance of the PI-RADS [ 21 ], effectively impact biopsy decisions in reference studies such as the MRI-FIRST [ 31 ], and potentially prompt which imaging features can be helpful in future revisions of the PI-RADS [ 21 ]. In our study, both readers relied more on clinical variables than imaging findings when using the Likert score (Supplementary Table 4), especially PSAD and DRE compared to the remaining clinical information available during reading phase 2 (age, prior biopsy, PSA, prostate volume, ongoing therapy with alpha-blockers, family history of csPCa, and symptoms). This result is in line with the role that PSAD and DRE have in shaping biopsy decisions and defining patient risk categories, respectively [ 13 ]. Our results support the concept that, while the most reproducible and impacting features supporting a selective use of the Likert score should be further elucidated, the strategy we investigated is of clinical added value in reducing the false positives in the real world. Further studies should also assess whether Likert-adjusted PI-RADS categories compare with risk calculators in assessing the pre-biopsy risk of harboring csPCa [ 32 ] or can represent additional variables to be included in clinical-imaging-based risk models, assumed that the same expert readers who provided reliable PI-RADS categorization can properly refine them as we showed.

We must acknowledge several study limitations. Given the retrospective design, we could not perform a targeted biopsy of index lesions found by Likert scoring only, so a systematic biopsy was used as a surrogate standard of reference. Second, we did not compare our approach to strategies proven to reduce the false-positive rate (e.g., adjusting the PI-RADS with PSAD [ 13 ]) or multivariable models stratifying patients’ risk by combining clinical features with mpMRI findings [ 32 ]. However, in the absence of a definite strategy on how to refine PI-RADS categories, the Likert adjustment strategy could be used in high-volume centers and multidisciplinary contexts where the urologist and other professionals can become familiar with the increased complexity of the mpMRI report, and more likely trust the Likert score as the "dominant" category for shaping biopsy decision (e.g., when a PI-RADS 4 lesion is downgraded to Likert 2). At the same time, this strategy could help less experienced readers to capitalize on the more standardized approach of the PI-RADS during the learning curve phase while having the capability, under supervision, to face more difficult cases with the flexibility inherent to the Likert score. Third, we only included biopsied patients, thus making difficult understanding how the combined PI-RADS-Likert categorization can work in low risk patients with negative mpMRI. Finally, R2 was a resident mentored by R1 in clinical practice, suggesting that her criteria for using the Likert score can largely reflect those those of R1, and, in turn, limit the generalizability of our inter-reader comparison outside the monocentric setting of this study. This could be further emphasized by the fact that the use of the PI-RADS version 2.1 translated into a diagnostic performance of R2 close to that of the experienced radiologist in our study, suggesting that the effect of combining the Likert score with a standardized system should be tested on a larger scale in less experienced readers.

In conclusion, our proof-of-concept study supports the hypothesis that combining the PI-RADS version 2.1 categories with the Likert score can improve the specificity and PPV of prostate mpMRI with no detrimental effect on sensitivity and NPV. Regardless of readers’ experience, clinical features (especially PSAD and DRE) were the most impactful ones in determining combined PI-RADS and Likert scoring. However, the reproducibility of the factors triggering the selective use of the Likert score should be tested on a larger scale. While overall mild, the improvement translated into greater net benefit in shaping biopsy decisions in the case of R1, suggesting that this strategy can be easily and effectively used by more experienced readers in clinical practice.

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Acknowledgements

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of the Department of Medicine (DMED), University of Udine ([email protected]), with the approval number RIF. Prot IRB: 75/2023. The acquisition of informed consent was waived because of the retrospective design.

Open access funding provided by Università degli Studi di Udine within the CRUI-CARE Agreement. No funds, grants, or other support was received.

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Paolo Polizzi

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Gianluca Giannarini and Chiara Zuiani share the senior co-authorship.

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Rossano Girometti, Valeria Peruzzi, Paolo Polizzi, Lorenzo Cereser & Chiara Zuiani

Division of Medical Statistics, Department of Medicine (DMED), University of Udine, pl.le Kolbe, 4 – 33100, Udine, Italy

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Rossano Girometti, Valeria Peruzzi, Paolo Polizzi, Letizia Casarotto, Miriam Isola, and Alessandro Crestani. The first draft of the manuscript was written by Rossano Girometti, Maria De Martino, Lorenzo Cereser, and Gianluca Giannarini, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Girometti, R., Peruzzi, V., Polizzi, P. et al. Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04506-2

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From foes to friends: rethinking the role of lymph nodes in prostate cancer.

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First-in-human validation of a drop-in β-probe for robotic radioguided surgery: defining optimal signal-to-background discrimination algorithm., cancer statistics, 2024, efficacy and safety of immune checkpoint inhibitors for patients with prostate cancer: a systematic review and meta-analysis, genitourinary cancer neoadjuvant therapies: current and future approaches., prostate-specific membrane antigen (psma) radioguided surgery in prostate cancer: an overview of current application and future perspectives, different lymph node dissection ranges during radical prostatectomy for patients with prostate cancer: a systematic review and network meta-analysis, pelvic lymphadenectomy may not improve biochemical recurrence-free survival in patients with prostate cancer treated with robot-assisted radical prostatectomy in japan (the msug94 group), lba7 neoadjuvant immune checkpoint inhibition in locally advanced mmr-deficient colon cancer: the niche-2 study, safety of neoadjuvant immunotherapy in resectable cancers: a meta-analysis, tumor-draining lymph nodes: at the crossroads of metastasis and immunity, related papers.

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Lord J, Willis S, Eatock J, et al. Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project. Southampton (UK): NIHR Journals Library; 2013 Dec. (Health Technology Assessment, No. 17.58.)

Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project.

Chapter 4 case study 1: full guideline model for prostate cancer.

This chapter presents a case study application of the development of a full guideline model to evaluate multiple decision problems across the prostate cancer pathway.

• Introduction

Introduction to the context of the case study

Prostate cancer is the most common cancer in men in the UK. 67 Every year over 40,000 new cases are diagnosed and just over 10,500 men die of prostate cancer. It is largely a disease that affects older men and is rare below the age of 50 years. More than 75% of cases occur in men aged > 65 years, with the largest number in men aged between 70 and 75 years old. 68 The symptoms of prostate cancer can be easy to misinterpret as they are not specific to the disease. They include urgency, difficulty and pain on passing urine. Men with early stages of the disease are likely to have no symptoms at all.

There is no routine screening of men in the UK for prostate cancer; 69 however, men are encouraged to seek a consultation with their general practitioner (GP) for testing if they are concerned about or are at higher risk of developing the disease. Risk factors for prostate cancer include age, family history (the risk of developing prostate cancer doubles or triples for men with a family history of prostate cancer in a first-degree relative), ethnicity (the incidence of prostate cancer in the UK is highest in black Caribbean and black African men and lowest in Asian men) and diet (diets high in calcium may increase the risk of developing prostate cancer). 68

Prostate cancer is not always life-threatening. Over the past 10–15 years there have been a number of significant advances in prostate cancer management but also a number of major controversies, particularly about the clinical management of men with early, non-metastatic disease. 54 Radical treatment can result in nerve damage and cause urinary dysfunction, sexual dysfunction and bowel problems which have a significant and lasting impact on quality of life.

Variation in practice across the UK, the significant uncertainties faced by men in making treatment decisions and the considerable impact of prostate cancer on quality of life as well as mortality led to the commissioning of the first CG on prostate cancer by NICE in 2005 (CG58). The guideline covered the key aspects of prostate cancer management from the point of referral into secondary care: diagnosis and staging, observation, radical treatment, salvage treatment, follow-up, hormone treatment and best supportive care (BSC). 54

Aims of the case study

The aim of this case study was to develop a health economic model to cover the scope of the prostate cancer guideline in sufficient depth that it could be used to evaluate various options for service change.

The modelling approach was broadly based on the methodological framework for developing Whole Disease Models set out by Tappenden and colleagues, 49 albeit using a more restrictive model scope which includes only a partial representation of disease natural history.

This case study includes economic analysis of a number of potential topics to update within the guideline, selected using methods discussed in Chapter 3 . Our aim was to investigate the ability of the full guideline model to address such questions. The results of these analyses are not intended to provide suggestions for new guideline recommendations, as they are not based on up-to-date systematic reviews of clinical effectiveness and they have not been informed by an expert CG group. Instead the aim of the economic analyses is to indicate topic areas where further investigation is likely to be of value.

The typical starting point for creating a health economic model involves developing an understanding of the decision problem and setting out the basis for the comparison between a full set of relevant alternatives. Given that one of the key objectives of the case study was to assess the flexibility of having a full guideline model for prostate cancer, the questions that the model would need to be able to evaluate were not known at the outset.

We developed a detailed individual-level DES to evaluate the expected cost-effectiveness of options for the diagnosis, treatment and follow-up of prostate cancer. The model was developed using SIMUL8 software. In line with the current NICE reference case, 36 the model considers health outcomes and costs from the perspective of the NHS and PSS and simulates key clinical and subclinical events, and the costs and consequences of these, over the remaining lifetime of patients. Costs were valued at 2010–11 prices. All costs and health outcomes were discounted at a rate of 3.5%. The headline model results are presented in terms of the incremental cost per QALY gained within each guideline topic.

The model development process had four main stages. First, we developed a detailed understanding of the clinical area and represented this using conceptual service pathway models. These conceptual models were intended to be recognisable to men with prostate cancer in the UK and to clinicians working within the NHS. This aspect of conceptual model development was based on a preliminary review of the literature and the existing NICE guideline. 54 We also developed an understanding of the key clinical events, and later represented these within a model of the disease process. The second stage involved converting our understanding into a model constructed to retain the key events in the clinical pathway, while taking into account the availability of evidence and the need for simplifications and assumptions. This took the form of a design-oriented conceptual model which set out the main interactions between the disease and treatment pathways. This latter conceptual model was developed iteratively and was formalised only at a late stage during model development. The third stage involved programming the simulation model. Although it has been argued that conceptual model development and implementation should remain largely discrete, 57 the processes of designing and implementing the model overlapped considerably. The final stage involved using the model to assess the potential cost-effectiveness of a variety of options for service change across the prostate cancer pathway.

Preliminary literature review

We conducted a literature review of published economic models of prostate cancer from NICE (TAs) and other HTA bodies and guideline developers. Searches were undertaken across a number of electronic databases [Centre for Reviews and Dissemination (CRD) NHS Economic Evaluation Database, CRD HTA Database, NHS Evidence, The Cochrane Library and G-I-N database] using general disease and patient group search terms. This search was undertaken as a rapid means of identifying potentially appropriate structures for certain elements of the model and to identify potentially relevant sources of evidence to inform the model parameters. We did not conduct a formal critical appraisal of the identified economic evaluations nor did we summarise their findings, as we were not specifically interested in the credibility of the results of existing models. Documentation for the current NICE guideline was reviewed (comprising the full NICE guideline, accompanying evidence review, the QRG and the implementation tools 70 – 73 ) in detail to ensure that we had a coherent understanding of existing recommendations and the rationale underpinning these, the recommended care pathway and the clinical and economic evidence available at the time the recommendations were made.

• The conceptual model

Boundary and scope of the model

The scope of the NICE prostate cancer guideline 74 was used to define the boundary of the health economic model. Entry and exit rules were defined, based on all current recommendations from NICE including recommendations for men with hormone-refractory disease from the NICE TA101 (NICE 2006). 75 Patients enter the model after having been referred to secondary care by their GP, either due to the presence of symptoms or due to an elevated prostate-specific antigen (PSA) test. Patients exit the model when they die or when they have an event which would fall under the remit of another guideline. For example, although the NICE prostate cancer guideline 54 refers to the referral of patients with suspected prostate cancer from primary care, this is covered in another guideline 76 and was thus deemed to be beyond the boundary of this evaluation. A proportion of men who present with elevated PSA will not have prostate cancer but may still undergo further tests and monitoring for prostate cancer, so these patients were necessarily retained within the model boundary.

Conceptual service pathways

A conceptual representation of the clinical service pathways for prostate cancer services in England and Wales was constructed based on the recommendations contained within the NICE 2008 prostate cancer guideline. 54 This was intended to represent clinical practice if the recommendations within CG58 had been fully implemented. It is important to note that the pathway does not necessarily reflect actual practice in the NHS, as the extent of implementation and compliance with guideline recommendations is likely to be variable.

The NICE prostate cancer guideline has a relatively clear structure in terms of the key disease management areas: diagnosis and staging of disease; monitoring and management options; potentially curative treatment; and palliative treatment. However, like most CGs, it was not designed to cover every aspect of clinical care, hence a number of assumptions were required to link individual recommendations into a single ‘joined-up’ pathway. We sought advice from a consultant clinical oncologist who was a member of the 2008 Prostate Cancer NICE GDG and an additional urological registrar to ensure the accuracy and representativeness of the conceptual service pathway.

Figure 1 summarises the conceptual service pathway model; a more detailed version of this conceptual model is presented in Appendix 4 . Briefly, patients enter the pathway on referral into secondary care. Patients may have been referred by their GP or by another secondary care physician. Repeat tests are carried out during the initial consultation and a decision is made whether the patient should undergo a transrectal ultrasound (TRUS)-guided biopsy. If men opt-out, or if a TRUS-guided biopsy is not considered necessary, they have regular PSA tests carried out by their GP. Note that although men on GP monitoring will not have a diagnosis of prostate cancer at this point, some may have the disease. For men who do undergo TRUS-guided biopsy, the result (which generates a Gleason score – a marker of cell differentiation or ‘aggressiveness’ of the cancer) is used together with PSA score and clinical disease stage to define a patient’s prostate cancer risk ( Table 5 ).

Summary of the service pathway based on guideline recommendations. DRE, digital rectal examination; TRUS, transrectal ultrasound.

D’Amico risk classification

The ‘preferred treatment option’ for men with low-risk disease who are suitable candidates for radical treatment is active surveillance (AS). 54 We interpreted this preference as a strict recommendation, ignoring other treatments recommended as possible alternatives.

All men with intermediate- or high-risk disease who are suitable for radical treatment are assumed to receive imaging (MRI or CT scan) to stage the disease and plan treatment. Radical treatment options include prostatectomy, brachytherapy (for patients with high-risk disease only), radical radiotherapy with adjuvant hormone treatment or hormone treatment (in which case men follow the same pathway as for, what we term, ‘palliative treatment’).

Men who are considered unsuitable for radical treatment or who have a life expectancy of 10 years or less are assumed to receive watchful waiting. This involves regular PSA tests and contact with a urologist in a secondary care setting. If symptoms of advanced prostate cancer develop over this time, individuals are assumed to receive palliative treatment. First-line palliative treatment was taken to mean either medical or surgical castration (intermittent or continuous hormone treatment or bilateral orchidectomy) or bicalutamide monotherapy (which may be chosen to retain sexual function at the expense of overall survival). When first-line treatment fails, bicalutamide is added to the treatment regimen (unless the patient has received bicalutamide previously, in which case continuous hormone treatment is offered) and the addition of dexamethasone is given as third-line palliative treatment. When dexamethasone fails, the patient is considered castration refractory.

If the patient is considered well enough, chemotherapy is offered as fourth-line palliative treatment, using either docetaxel or mitoxantrone in combination with prednisone or prednisolone. 75 When chemotherapy fails, patients receive corticosteroids, such as diethylstilbestrol for pain relief. No further active treatments are offered after this time, patients will receive BSC.

• The disease process model

In addition to the service pathway model, we also developed a conceptual model of the disease process to characterise the key clinical events, risks and subsequent prognosis ( Figure 2 ). We assumed that prior to diagnosis the underlying progression of prostate cancer follows a consecutive sequence of disease events, depicted on the left-hand side of Figure 2 . Men without prostate cancer are only at risk of death from other causes. Men with localised prostate cancer are assumed to only develop metastases if they first have local progression. The NICE prostate cancer guideline 54 recommends that a clinically meaningful relapse should be established before starting palliative treatment. Owing to the absence of reported evidence on documented relapse, we assumed that biochemical relapse after radical treatment is a proxy for local progression. Similarly, we assumed that a patient cannot die of prostate cancer without first developing metastases.

Disease process model.

The central distinction in the clinical management of the disease (depicted on the right-hand side of Figure 2 ) is between patients with disease that is potentially curable and those with disease that is not. The distinction between localised disease and locally advanced disease is assumed to be less significant since treatment options for patients with locally advanced disease mirror those offered to patients with high-risk localised disease. The aim of treating patients with incurable disease is to slow the progression of the disease and to prevent it becoming castration refractory. Patients with castration-refractory disease may be treated with chemotherapy, which is also intended to slow the progression of the disease. Again, owing to limitations in the available evidence on documented relapse, we assume equivalence between biochemical relapse and local progression.

• Final model design

The final model structure did not fully mimic the conceptual service pathways model described in Appendix 4 . The main reason for this was that the 2008 NICE guideline 54 relies heavily on PSA score as an indicator of underlying disease progression and as a trigger for events such as follow-up tests and changes in treatment. Some evidence was available on initial PSA and PSA changes over time according to initial diagnosis (which we used in the GP monitoring section of the model); however, we did not find evidence to link these changes in PSA to changes in treatment or risk of progression over time. As a consequence, we were unable to use PSA to fully drive changes in patients’ underlying disease and the treatment pathways that patients would follow. Instead, we assumed that the natural history of the disease follows a linear series of conditional transitions from local progression to metastases to death from prostate cancer (see Figure 2 ). We also assumed that patients would begin palliative treatment as soon as radical treatment was considered to have failed. In this sense, the lack of evidence restricted the level of depth with which the progression of the disease could be represented within the model. These decisions were taken iteratively as we understood what evidence was available and were only formalised after we had begun to implement the model.

The model was implemented as a next-event DES model. An individual-level simulation approach was taken as this allows for a more complex representation of model events conditional on patient characteristics and provides a greater level of flexibility in implementing and adapting the model as compared with a cohort approach (e.g. a Markov model). The model was developed by considering the relevant competing events at each point in the clinical pathway (see Figures 3 and 4 ). The time to each event was sampled for each patient, with the next event determined by whichever of these occurred first. After each event, an individual’s prostate cancer risk profile was updated (e.g. age and disease status) and the times to the next set of relevant events were recalculated. Other-cause mortality was sampled differently in that this was defined on model entry and the remaining time to this event was recalculated on the occurrence of any other non-fatal event. Costs and effects were recorded as the patient progressed through the model, conditional on the events that they experienced. A continuous discounting approach was adopted to account for health outcomes and costs which accrue over a particular time period. One-off costs (e.g. surgery) were discounted using a standard periodic discounting approach. The programming approach implemented within the final model followed the method suggested by Tappenden and colleagues. 49

Design-oriented model: pre-clinical and diagnostic sections. BPH, benign prostatic hyperplasia; PC, prostate cancer; RTT, remaining time to; TTNE, time to next event.

Design-oriented model: treatment pathways for diagnosed disease. AE, adverse event; OS, overall survival; PC, prostate cancer; PFS, progression-free survival; RTT, remaining time to; TTNE, time to next event.

• Detailed model description and programming logic

Patients enter the simulation model having been referred to secondary care by their GP, either due to the presence of symptoms or due to an elevated PSA test, or by a secondary care physician who suspects the individual might have prostate cancer. The model design is summarised in Figures 3 and 4 and the underlying logic of each section is described below. Each box within the diagram represents a SIMUL8 ‘workcentre’ in which events, costs and consequences are sampled and applied to individual patients. With the exception of hormone and palliative treatments, all events which are modelled according to multiple competing risks are implemented using two related workcentres; one dummy workcentre that determines which event occurs next and another that represents the actual interaction of the patient with the prostate cancer service.

Workcentre 1: initial characteristics

On entry into the model, patients are assigned initial characteristics. These characteristics include: the presence or absence of prostate cancer; age; initial stage (using standard tumour node metastasis classification); and Gleason score. Patients are assigned a risk category based on the D’Amico classification using clinical stage and Gleason score (see Table 5 ). CG58 classified T2c disease as intermediate risk prostate cancer, where the original D'Amico criteria 81 classified this as high-risk disease. The assigned risk category later dictates which treatment options are available to the patient. PSA score is then sampled conditional on stage; this was necessary as the national registry data used to assign patients’ characteristics did not include data on initial PSA score (see Evidence used to inform model parameters ).

Published results from the observation arm of the Bill-Axelson and colleagues trial 78 were used to provide information on the natural history of prostate cancer for each disease event (local progression, metastases and prostate cancer death). Patients included in this arm of the trial were from an unscreened (Scandinavian) population and most did not receive any curative treatment.

The incidence of prostate cancer is not captured in the model (whether a man has prostate cancer or not is defined on entry to the model). If a simulated individual does not have prostate cancer on entry into the model, it is assumed that he cannot go on to develop prostate cancer. A proportion of these patients are assumed to have benign prostatic hyperplasia (BPH).

Workcentre 2: secondary care attendance

Following referral to secondary care, all patients are assumed to have a repeat PSA test (from a blood test) and digital rectal examination (DRE). Patients with a very high PSA score (> 75 ng/ml), which is taken to indicate obvious symptoms of advanced prostate cancer, are offered a bone scan without prior biopsy and hormone treatment with palliative intent. All other patients are considered for a TRUS-guided biopsy if they meet the Prostate Cancer Risk Management Programme (PCRMP) primary care referral guidelines, 79 which are dependent on age and PSA score. Patients who do not meet the referral criteria, who have already had three prior biopsies or who opt-out of biopsy, are sent for GP monitoring with a PSA test every 6 months. These patients do not have a diagnosis of prostate cancer, although some may have underlying disease which may or may not be diagnosed if they re-enter secondary care. If a patient has undiagnosed prostate cancer the disease will progress untreated. Patients who do not have prostate cancer are assumed not to develop prostate cancer within their lifetime. For the sake of simplicity, it is assumed that no time elapses between the secondary care visit and the primary care attendance (either at model entry or when the patient attends GP monitoring). The cost of the PSA test is added, but contact between the patient and his GP is not included.

Workcentre 3: transrectal ultrasound-guided prostate needle biopsy

On entry to the biopsy workcentre, the number of biopsies is recorded as we assumed that patients could undergo a maximum of three biopsies in their lifetime, unless they are on AS. The probability that a patient receives a positive biopsy result is based on the sensitivity of the test given the individual’s true underlying histology. TRUS is assumed to be perfectly specific, meaning that all men who do not have prostate cancer will be correctly identified as not having the disease. The results of a TRUS-guided biopsy given the presence/absence of underlying cancer are sampled and patients with a true-positive result are sent to the ‘determine appropriate treatment’ workcentre. A proportion of patients who test negative are assumed to be invited to attend a repeat biopsy in 6 months’ time, whereas the remainder are assumed to return to GP monitoring and undergo a PSA test in 6 months’ time. Those patients who have undiagnosed BPH are assumed to have this pathology detected at this point and remain in the BPH workcentre until they die of other causes. Those patients who test negative, do not have BPH and were not referred for a repeat biopsy, undergo GP monitoring (these patients may have prostate cancer, but this is clinically unknown at this stage). The model assumes that patients do not attend every GP visit to which they are invited. 92 Where applicable, the cost of TRUS-guided biopsy is added to the running total cost. A probability of experiencing infection due to TRUS is also sampled and the cost of treating the infection, if it occurs, is added to the running total.

Workcentre 4: undiagnosed (dummy workcentre)

Patients who enter the GP monitoring workcentre do not yet have, and may never receive, a diagnosis of prostate cancer; these patients may or may not have underlying cancer. These patients are assumed to undergo PSA tests every 6 months indefinitely. For these patients, the time to the next event (TTNE) is then determined. Competing events are (1) other-cause mortality; (2) prostate cancer-specific death; (3) local progression (unless this has already occurred); (4) metastases (unless this has already occurred); (5) next scheduled PSA test; and (6) time to next biopsy (for those with a scheduled repeat biopsy only). If cancer-specific or other-cause death occurs during GP monitoring, patients exit the model at this point. The remaining time to each competing event is then recalculated based on the time interval TTNE. If the next event is local progression or metastases, this is assumed to manifest symptomatically and triggers a GP visit and PSA test at the time of the clinical event. Most patients return to the GP for their scheduled 6-monthly PSA test (a proportion are assumed to not attend). If the patient was due to undergo a repeat biopsy but some other event occurs first, this is assumed to result in earlier biopsy (at age + TTNE). Age is then updated by the time to next event for all patients.

Workcentre 5: primary care appointment for prostate-specific antigen test

In the primary care workcentre, patients who meet the PCRMP referral guidelines (dependent on age and current PSA score) are assumed to be sent for a biopsy. Those patients who do not meet the referral criteria, who have already had three prior biopsies or who opt-out of biopsy, return to GP monitoring with the next PSA test scheduled 6 months later. The cost of a primary care visit plus the cost of the PSA test is added to the running total. The time of the PSA test is recorded.

Workcentre 6: bone scan

Bone scans are assumed to be perfectly sensitive and specific within the model; this is a simplifying assumption due to the lack of evidence. Patients who have metastases are assumed to be identified by the scan; these patients are diagnosed at this point and go on for treatment planning. Patients who do not have metastases are correctly identified and, if eligible, will have a biopsy immediately or will otherwise have GP monitoring. The cost of the bone scan is added to the running total cost.

Workcentre 7: determine appropriate treatment

At the point of diagnosis all patients enter this workcentre to determine appropriate treatment given their age, stage of disease and suitability for radical treatment. The patient’s prostate cancer risk, according to the CG58 classification, 70 is updated at this point based on the patient’s underlying cancer stage, Gleason score and PSA score. Disease stage is updated over time in line with the disease logic model detailed in Figure 2 . PSA score is updated only when patients receive GP monitoring. Gleason score is not updated over time (note this assumption has been made elsewhere). 80

If the patient has metastases they are assumed to receive palliative hormone treatment. If the patient is aged < 80 years, is suitable for radical treatment and has low-risk disease, he is assumed to go to AS with the intention of later receiving radical treatment, either at the onset of symptoms or when he chooses to undergo treatment. If the patient is aged < 80 years, is suitable for radical treatment and has intermediate-risk disease he is assumed to either transit immediately to radical treatment or to enter into AS. Patients with high-risk disease are assumed to transit immediately to radical treatment. Patients who are unsuitable for radical treatment and are symptomatic are assumed to transit immediately to palliative hormone treatment. Patients who are unsuitable for radical treatment and are not symptomatic are assumed to receive watchful waiting. If the patient has metastases but has not previously had a bone scan since developing metastases, he receives a bone scan at this point. All patients undergo a MRI scan or CT scan prior to receiving radical treatment.

Workcentre 8: active surveillance (dummy workcentre)

This is a ‘dummy’ workcentre which determines the next relevant event for a given patient. As noted above, only patients with low- or intermediate-risk disease enter AS. On entry into AS, patients are assumed to undergo a PSA test every 3 months for the first year after their initial diagnosis of prostate cancer and every 6 months thereafter until they leave surveillance or die. TRUS-guided biopsy is assumed to take place 1 year following initial diagnosis and then every 3 years thereafter until the patient leaves surveillance or dies. Patients who experience local progression or those who opt for treatment over surveillance go on to receive radical treatment. Although the model assumes that it is impossible for patients to develop metastatic disease on AS, we do not believe that this is a strong assumption as in reality metastasis is very unlikely to occur in these patients. Patients who reach the age of 80 years without having radical treatment are assumed to transit to watchful waiting and are assumed to be no longer suitable for radical treatment.

Workcentre 9: active surveillance visit

Patients enter the AS visit workcentre if their last event was non-fatal. At this point individuals can either receive a scheduled test (PSA or biopsy) or receive radical treatment (either at the patient’s choice or because of symptomatic disease progression). The model assumes that every patient undergoes a PSA test on entry into the workcentre. The last event is used to update the TTNEs in the model. For example, if a patient reached the age of 80 years and was moved onto watchful waiting, the time to the next PSA test is dictated by the watchful waiting test schedule rather than the AS test schedule.

Workcentre 10: watchful waiting general practitioner visit

Patients on watchful waiting are assumed to undergo a PSA test every 12 months. The TTNEs are updated. Stage is updated in the model if the disease has progressed. If a patient developed metastatic disease, the model assumes that hormone treatment will be initiated. Otherwise, the patient will remain on watchful waiting. The cost of the scheduled GP consultation and the PSA test is added to the running total of costs.

Workcentre 11: watchful waiting (dummy workcentre)

This dummy workcentre calculates when the next event occurs. This can be either other-cause death, disease progression (local or metastatic) or a scheduled test. Disease progression is assumed to be symptomatic so a patient will present outside their scheduled appointment and will be offered hormone treatment.

Workcentre 12: radical treatment

Patients who enter the radical treatment workcentre do not have metastatic disease and their disease was classified according to the CG58 risk criteria at diagnosis. 70 All patients with low-risk disease will have previously been on AS, but have switched onto radical treatment (thus their disease may no longer be considered low risk). The model assumes that these patients are offered the same treatment as patients with intermediate-risk disease: radical prostatectomy (open), radiotherapy (and hormones) or brachytherapy. Patients with high-risk localised disease or locally advanced disease are only eligible for hormones plus radiotherapy or hormone therapy alone.

Radical treatment is assumed to have an impact on time to local progression and the frequency of three adverse events (sexual dysfunction, urinary dysfunction and bowel dysfunction). The model assumes that outcomes from treatment are the same for all risk categories since the available randomised controlled trial (RCT) evidence does not suggest otherwise. As noted earlier, the model equates biochemical progression (the primary outcome from trials of radical treatment) with local disease progression. The model assumes that time to prostate cancer death is not directly influenced by radical treatment. Patients having radical prostatectomy may die perioperatively due to surgical complications, with risk increasing with age.

The three adverse events included in the model are associated with different disutilities, which we assume to be lifelong and additive [that is the impact on health-related quality of life (HRQoL) for each adverse event is independent of other adverse events].

If patients do not die of other causes, they will receive follow-up comprising an annual bone scan, a PSA test every 6 months and a urology consultation. In the first 2 years following treatment, the PSA test will be done in secondary care and the consultation as an outpatient visit. After that, the PSA test will be undertaken in primary care and the consultation will be by telephone with a urology consultant. Follow-up is assumed to cease at the time of local progression (or death from other causes).

Workcentre 13: hormone treatment + chemotherapy + best supportive care

The hormone treatment plus chemotherapy plus BSC workcentre calculates the patient’s time to prostate cancer death and determines the proportion of this period which is ‘progression-free’; this is assumed to be dependent on the treatment received. The remaining time to other-cause death remains unaffected by treatment. The model assumes that first-line treatment (intermittent hormones, continuous hormones, bilateral orchidectomy or bicalutamide monotherapy) determines overall survival and the sequence of later lines of treatment. Progression-free survival (PFS) from each line of treatment (up to four lines of treatment in the base-case analysis) is added, with any remaining time before prostate cancer death spent in a progressive disease state while receiving BSC. If patients survive the first three lines of treatment, chemotherapy is given as the fourth-line treatment (either using a docetaxel-based or mitoxantrone-based combination regimen). A fixed proportion of patients will not receive chemotherapy (not all patients will be fit enough). Owing to evidence limitations, mean health state sojourn times were used so all patients allocated to the same treatment will have the same outcomes. Cause of death is determined (prostate cancer or other cause) and PFS is adjusted to ensure the sum of progression-free intervals does not exceed overall survival. That is, if the sum of progression-free intervals exceeds the sampled overall survival time for an individual patient, the final PFS interval is truncated.

Workcentre 14: death

Health outcomes are calculated for each simulated patient at time of death. The cost of terminal care is added here, if the patient has died from prostate cancer. Survival is calculated by adding together the time each patient has spent in different segments of the model. There are up to seven time segments which reflect all possible paths through the model ( Figure 5 ).

Time segments (numbered) used to calculate overall survival.

The numbered time segments in Figure 5 refer to the following routes through the model:

• Segment 1: initial attendance to death (non-cancer or undiagnosed cancer patients) or cancer diagnosis.
• Segment 2: from the start of radical treatment to cure, biochemical relapse or other-cause death.
• Segment 3: from the start of AS to initiating radical treatment, until death or until beginning watchful waiting. (Note: if patients do not receive AS no time will be spent in this segment.)
• Segment 4: from the start of watchful waiting to the start of palliative treatment, or death.
• Segment 5: hormone treatment to end of PFS from third-line (palliative) treatment [i.e. men with castration-refractory prostate cancer (CRPC)].
• Segment 6: from the start of fourth-line (palliative) treatment to beginning of BSC or death.
• Segment 7: BSC to death.

Discounted and undiscounted life-years and QALYs are calculated for all patients. The lifetime costs of adverse events are added in the death workcentre, including the cost of screening with flexible sigmoidoscopy every 5 years for patients who receive radical radiotherapy, in line with the CG58 recommendation. 54

• Evidence used to inform the model parameters

The model was populated using evidence identified within the 2008 NICE prostate cancer guideline 71 supplemented with additional evidence identified through rapid literature searches and/or expert opinion. We did not conduct systematic reviews for all of these parameters, as this was not possible within the resources available for the study, and there are certain parameters (e.g. unit costs) whereby a conventional systematic review approach is neither required nor preferred. 57 This is likely to mirror the pragmatic approach taken to populate health economic models during routine development of NICE CGs. The model includes the following groups of parameters:

• disease epidemiology and baseline patient characteristics (incidence and prevalence of the condition and subgroups, baseline risks, rates of progression of disease and mortality rates)
• test operating characteristics (e.g. sensitivity and specificity) of the tests included in the pathway
• clinical effectiveness of the treatments included in the pathway (e.g. PFS, time to biochemical relapse, perioperative mortality)
• patient behaviour (e.g. probabilities of opting out of biopsy or of attending routine PSA tests)
• utilities associated with disease, treatment and adverse events
• resource use and
• unit costs.

Disease epidemiology and baseline patient characteristics

Data were required to define the initial characteristics of men with and without prostate cancer. We used national cancer registry data obtained from the South West Public Health Observatory (SWPHO) to provide information on age, clinical stage at diagnosis and Gleason score at diagnosis for patients with diagnosed prostate cancer (SWPHO 2010, data held on file). The national registry database does not record PSA score, hence it was necessary to calculate patients’ prostate cancer risk according to two of the three CG58 risk criteria (see Table 5 ). Age-specific PSA values from men in the watchful waiting arm of the Bill-Axelson and colleagues RCT 78 were used to estimate PSA scores on model entry for men with prostate cancer (cited by Tilling and colleagues 82 ).

Evidence relating to age-specific PSA scores of the cancer-free population in the model was taken from the Krimpen longitudinal community-based study. 83 We have no UK data relating to this patient group so we assumed that PSA scores in these patients follow the same age distribution as for men with prostate cancer.

There is uncertainty regarding the true disease prevalence in men referred to secondary care with suspected prostate cancer. Owing to an absence of empirical estimates, we assumed a value of 25% based on expert opinion which roughly reflects the results from non-UK autopsy studies (20–34%). 84 – 86 Data on death from causes other than prostate cancer were taken from national life tables; these were adjusted by removing all deaths attributed to prostate cancer. 87

Independent survival curves for local disease progression, metastatic disease progression and prostate cancer death were taken from the 2011 publication of the Bill-Axelson and colleagues RCT, 78 which compared radical prostatectomy with watchful waiting (this was the closest proxy to information on the natural history of the disease without treatment). This study reported numbers of patients who experienced local progression, metastases and prostate cancer death at 5-year and 10-year time points. It should be noted that the Bill-Axelson and colleagues trial 78 outcomes relate to the point of documented progression and metastases rather than the true underlying time of histological change. These outcomes are also based on a Scandinavian population of men in the pre-PSA testing/screening era hence they may not fully reflect the UK population within the model. We used model calibration methods to derive correlated conditional distributions for these events. We implemented a random-walk variant of the Metropolis–Hastings algorithm 88 based on the methods described by Whyte and colleagues 89 directly in SIMUL8 and fitted the model against the unconditional data from Bill-Axelson and colleagues 78 and other-cause mortality estimates from the UK. We ran the algorithm over four separate chains with different starting vectors in order to estimate plausible distributions for each event, conditional on the population having experienced the previous event. The joint distributions of progression parameters were used directly in the probabilistic sensitivity analysis. Figure 6 presents a comparison of the maximum a posteriori estimates produced by the calibration process against the observed data reported by Bill-Axelson and colleagues; 78 the figures show that the calibration provides a good fit to the observed data.

Observed vs. calibrated frequencies of disease events. LP, local progression; PC, prostate cancer.

Diagnostic test accuracy

We assumed a sensitivity of 77% for TRUS-guided biopsy. 90 We assumed that PSA, DRE, MRI, CT and bone scans are perfect tests. We also assumed that the TRUS-guided biopsy is perfectly specific (i.e. no false-positive results), whereas in reality its use as a diagnostic test may lead to overtreatment. Test accuracy studies are difficult to undertake in this area, as pathological confirmation will not be carried out for patients with negative biopsy results. The simplifying assumptions were necessary not only because of the lack of gold-standard comparison studies, but also due to the complexity of including the implications of misdiagnosis and misclassification from these tests in the model and the limited information available on the natural history progression of prostate cancer.

A small proportion of patients will experience an infection as a result of biopsy (probability = 0.47%), and this is represented in the model. 91 Not all patients are willing to undergo biopsy; we assume 12% of men will opt-out. 92 Uncertainty surrounding these parameters was characterised using beta distributions.

Clinical effectiveness

Where more than one treatment is recommended at a particular point in the pathway, we used proportions elicited from the Department of Health’s National Radiotherapy Implementation Group and experts on the NICE Prostate Cancer GDG. The management and treatment options in the model were grouped according to their clinical intent (e.g. delaying and/or avoiding recurrence or increasing PFS) and the key outcome measures used in the clinical studies from which efficacy estimates were drawn. Perhaps surprisingly, there is a lack of evidence on the comparative effectiveness of currently available radical treatments for prostate cancer. Therefore, through necessity, evidence from different trials was used and compared against single arms of other trials using naive indirect comparison methods ( Table 6 ). Radical prostatectomy is also associated with an excess mortality risk. 93 As discussed above, biochemical relapse after radical treatment is used as a proxy for local progression due to a lack of direct evidence on local progression per se. The estimates used in the model, characterised in terms of first- and second-order uncertainty, are detailed in Table 6 .

Radical treatment efficacy and adverse event parameters

Palliative treatments ( Table 7 ) were also difficult to model, as we did not identify any RCTs that explicitly evaluated planned sequences of treatments. Therefore, we assumed that first-line palliative treatment was the sole determinant of overall survival due to prostate cancer. Subsequent lines of treatment are assumed only to increase the proportion of the patient’s remaining survival time that is progression free. This manipulation of the evidence requires that we ignore first-order uncertainty in these parameters and therefore use mean sojourn times for estimates of overall and PFS, which is not ideal. The uncertainty in these mean values is still, however, reflected in the probabilistic sensitivity analysis.

Palliative treatment efficacy parameters

Health utilities

The lack of published evidence relating to the impact of prostate cancer and its treatment on HRQoL has been widely acknowledged. The health utility values used in the model were drawn from recent economic evaluations of prostate cancer ( Table 8 ). We did not identify any HRQoL evidence published after these studies.

Utility data

We incorporated the HRQoL impact of the three most common adverse events attributable to radical treatment (bowel function, urinary function and sexual function) as disutilities ( Table 9 ). Owing to the absence of data on the duration of adverse events, the model assumes that these last for the remaining lifetime of the patient. The impact of this assumption is not tested further here; however, the flexibility of the model allows such assumptions to be amended easily. Owing to a lack of evidence, the differential impact of adverse events on health utilities due to specific palliative treatments was not captured.

Disutility data

Resource use and unit costs

In accordance with the perspective of this analysis, the only costs considered were those relevant to the UK NHS and PSS. Costs were estimated in 2010–11 prices ( Table 10 ). Resource use estimates for the model were drawn from the NICE prostate cancer guideline (CG58) recommendations 70 following the prostate cancer service pathway (see Appendix 4 ). The cost of primary care contact before initial referral, primary care services during prostate cancer treatment and cardiovascular screening were not included in the model as these were difficult to ascertain from the current guideline recommendations and resource use patterns are likely to vary. In order to reflect the additional terminal care costs incurred by patients in the last month of life, a one-off cost of just over £4000 was applied to men who died of prostate cancer. This cost was used in the NICE TA101 having been estimated from costing data originally supplied by Sanofi-Aventis on men with hormone-refractory disease. 108

Unit cost parameters

Drug costs used in the base-case analysis were based on prices listed in the British National Formulary (BNF). 111

Handling uncertainty

With the exception of PSA trajectories which are sampled only according to first-order uncertainty, the model is fully probabilistic. Sampling of parameter uncertainty for the probabilistic sensitivity analysis was implemented by sampling the necessary distributions externally in Microsoft Excel ® (Microsoft Corporation, Redmond, WA, USA) and reading them into SIMUL8. This approach has the added advantage that changes in model results reflect only the impact of changes to the pathway (e.g. new chemotherapy B vs. current chemotherapy A) rather than randomness in the sampling of the parameters that make up the model structure; a similar approach was also used in the second case study (see Chapter 5 ). A total of 1500 probabilistic samples were used to propagate parameter uncertainty through the model, and all headline results are presented as the expectation of the mean rather than point estimates of parameters.

Verification and validation

Errors and inconsistencies in the model were checked for throughout the model development process, following the methods set out by Chilcott and colleagues. 23 The model was verified internally (to ensure correct programming) and validated externally (to ensure consistency with expected results, e.g. that survival times and levels of service use were realistic). A variety of methods were used including black box testing (testing the behaviour of the model) and white box testing (scrutinising the programming code). In addition, the model was programmed to record intermediate model outcomes (e.g. survival contributions attributable to particular segments of the pathway and costs associated with specific workcentres) in order to assess whether changes to the pathway impacted on those parts of the model as expected.

Once we were satisfied that the model was behaving as intended, we then assessed the number of patients required to achieve stability in the model results. We adopted a pragmatic approach to this using the results of the base-case model only. We ran the model with the base-case service configuration with different numbers of patients and compared the results from each section to the results for 1,000,000 simulated men. Figure 7 indicates that the costs and QALYs become fairly stable (< 2% deviation) at around 100,000 simulated patients. Conservatively, we adopted a cohort of 200,000 simulated individuals for the economic analysis.

Model stability according to size of simulated cohort.

• Modelling decision options across the service pathway

Nine topics were shortlisted from topics highlighted by NICE for possible inclusion in an update of the 2008 prostate cancer guideline (see Box 7 in Chapter 3 ). Details of how the nine topics were shortlisted are discussed in Chapter 3 .

Each topic implied an alternative clinical pathway, incorporating one or more changes to the recommendations made in CG58. 54

Figure 8 shows where these alternative recommendations are located in the clinical pathway. Each topic was transformed into a population, intervention, comparator, outcome (PICO)-style review question, described below.

Location of topics (A to I) in the clinical pathway. GnRH, gonadotropin-releasing hormone; LHRHa, luteinising hormone-releasing hormone analogue; LRP, laparoscopic prostatectomy; PRP, transperineal prostatectomy; RALRP, robot-assisted laparoscopic prostatectomy; (more...)

Topic A: pelvic radiotherapy with adjuvant hormone therapy for men with localised prostate cancer

The stated patient population for this topic – men with localised prostate cancer – is broad but reference is made to the SPCG-7 trial, 96 which included men with locally advanced or high-risk localised prostate cancer. The NICE prostate cancer guideline 54 recommended that these patients should be offered either radiotherapy with hormone treatment or hormone treatment alone. In practice, many men will only be offered hormone treatment, without the option of additional radiotherapy. A focused literature search conducted by NICE 112 identified three published papers from two new RCTs. 95 , 113 Only one paper had published full results of the trial at the time of analysis (the SPCG-7 trial 96 ). Six RCTs identified in the NICE prostate cancer guideline 71 have published additional follow-up results with findings in support of combined radiotherapy and hormone therapy. Additionally, two observational studies that compared quality of life following radiotherapy plus hormone therapy to that following radiotherapy alone were identified.

The PICO question was formulated to mimic the clinical question addressed in the only additional RCT published in full since 2008; an update to the SPCG-7 trial. Combined hormone treatment plus radiotherapy was compared with hormone treatment alone for men with locally advanced or high-risk localised prostate cancer. As the SPCG-7 trial was used to populate the base-case model, this economic question was evaluated without needing to modify the model structure.

Topic B: surgical techniques for localised prostate cancer: open radical retropubic prostatectomy, transperineal prostatectomy, laparoscopic prostatectomy or robot-assisted laparoscopic prostatectomy

This topic suggests four alternative surgical techniques [radical retropubic prostatectomy (RRP), transperineal prostatectomy (PRP), laparoscopic prostatectomy (LRP) and robot-assisted laparoscopic prostatectomy (RALRP)] for men with localised prostate cancer undergoing surgery. The NICE prostate cancer guideline 54 did not recommend a specific procedure for radical prostatectomy. The base-case model was populated with data from Bill-Axelson and colleagues. 78 Accordingly, the cost used was the NHS reference cost for a standard open procedure.

Eleven studies were identified by NICE, including three systematic reviews of observational studies, three additional observational studies and three RCTs. The three RCTs investigate different pairwise comparisons, as shown in Figure 9 .

Randomised controlled trial evidence network of surgical techniques for localised prostate cancer.

We limited our analysis to RCT evidence only. The systematic reviews suggested some problems with the reliability of the observational evidence and in some cases the methods of synthesis do not appear to be robust. Of the three RCTs, only the trial reported by Martis and colleagues 114 provided longer-term outcome data (biochemical recurrence); the others focussed on perioperative outcomes. 115 , 116 The time to biochemical recurrence survival curves reported for PRP and RRP in Martis and colleagues 114 are almost identical, hence we assumed no difference in biochemical recurrence-free survival between the two procedures. We sampled biochemical recurrence-free survival from the curve used in the base-case model. 78 It seems reasonable, given the absence of evidence to suggest otherwise, that LRP and RALRP are also associated with the same biochemical recurrence rate.

Differential perioperative mortality outcomes associated with specific techniques are not captured within the model. However, differences in the frequency of adverse events associated with each surgical procedure are captured; RALRP is associated with fewer sexual and urinary problems than LRP, which has a similar adverse event profile to RRP and PRP (although LRP results in slightly more urinary problems than RRP and PRP). 114 – 116

Another notable difference between the procedures is the difference in length of hospital stay for LRP or RALRP. We account for a shorter hospital stay for PRP (one-third less than for RRP) as reported in Martis and colleagues. 114 RALRP is also associated with a shorter length of stay, estimated to be 1 day less than for the standard open procedure in a recent UK business case analysis (Oxford Radcliffe Hospitals NHS Trust). 117 RALRP requires a significant capital investment which we include as an approximate figure of £3000 per patient in addition to the non-capital costs, based on the most expensive robot system and assuming a fairly large centre with a throughput of around 150 patients per year (Ramsay and colleagues 118 ). RALRP is also associated with more costly consumables and a longer operating time (Oxford Radcliffe Hospitals NHS Trust). The current NHS reference cost for prostatectomy includes both RRP and PRP procedures. LRP is costed separately, but bundled with other laparoscopic urological procedures. Full details are given in Appendix 5 .

Topic C: high-dose-rate brachytherapy + external beam radiotherapy for men with localised or locally advanced prostate cancer

Brachytherapy alone is currently recommended by NICE as an option for the treatment of men with intermediate- or low-risk disease, but is not recommended for patients with high-risk disease. Brachytherapy combination therapy was not considered in CG58. The patient population overlaps with topic D, hence we evaluated topics C and D together.

Seven papers investigating high-dose-rate brachytherapy (HDR) in combination with external beam radiotherapy were identified in a focussed search conducted by NICE; two of these were RCTs 119 , 120 and five were observational studies. 121 – 125 We restricted our analysis to use only the RCT data on biochemical relapse and frequency of adverse events.

Topic D: low-dose-rate brachytherapy + external beam radiotherapy for men with localised or locally advanced prostate cancer

Low-dose-rate brachytherapy (LDR) combination therapy was not considered in the NICE prostate cancer guideline. 54

No comparative data on the clinical effectiveness of LDR and external beam radiotherapy was identified. One US cohort study, reported by Sylvester and colleagues, 126 was identified. This study reported 15-year follow-up of 223 patients given iodine-125 or palladium-103 brachytherapy plus neoadjuvant radiotherapy. These data were used to estimate biochemical relapse-free survival and the frequency of adverse events.

Topic E: degarelix (a luteinising hormone-releasing hormone antagonist) for men with locally advanced or metastatic prostate cancer

No luteinising hormone-releasing hormone (LHRH) antagonists were recommended in the NICE prostate cancer guideline. 54 Degarelix is now licensed in the UK and was recently recommended by the Scottish Medicines Consortium under a patient access scheme. One non-inferiority RCT comparing low-dose degarelix (240/80 mg), high-dose degarelix (240/160 mg) and standard 7.5-mg monthly dose of leuprorelin (Prostap ® , Takeda) was identified. 127 The primary outcome measure in this study was the cumulative probability of testosterone with other outcomes including the incidence of PSA failure. As this study only had 12-month follow-up data and did not report outcomes according to those used in the full guideline model, some assumption about impact on overall and PFS was required.

Since Klotz and colleagues 127 showed equivalence of both doses of degarelix compared with leuprorelin at 12 months, we could (tentatively) assume equivalence in terms of progression-free and overall survival, in which case the cost-effectiveness will be determined by differences in cost alone. The trial does indicate some differences between the three arms in terms of the frequency of adverse events; however, these are not statistically significant, and the base-case model does not reflect differences between interventions in terms of adverse events in the palliative treatment section of the model. Thus, we assumed that any potential difference in adverse events has no impact on either survival or HRQoL. The drug schedules are the same (a starting dose at time 0, and monthly injections thereafter). Thus the drug cost for the first year of treatment (using BNF prices) will be £3352, £1812 and £903 for the high-dose degarelix (240 mg/160 mg thereafter), low-dose degarelix (240 mg/80 mg thereafter) and leuprorelin (7.5 mg monthly) respectively.

Given the above assumptions, formal modelling is not required to show that leuprorelin would be dominant (i.e. cheaper and equally effective). Therefore, given the limitations in the available evidence, this topic was not evaluated using the full guideline model.

Topic F: intermittent hormone therapy versus continuous hormone therapy for men with metastatic prostate cancer

The NICE prostate cancer guideline (CG58) 54 did not address the question of intermittent compared with continuous hormone therapy for patients with metastatic prostate cancer. Two RCTs 97 , 128 have shown almost identical survival outcomes, with a slighter longer time to progression in the continuous hormones arm. At the time of analysis only one RCT had published its results in full, 97 and these data were used in the base-case model. No changes to the base-case model were required to evaluate this topic.

Topic G: radium-223 chloride versus strontium-89 for men with castration-refractory prostate cancer and painful bone metastases

This topic was suggested because of the promising results shown in a Phase III RCT, ALSYMPCA (Alpharadin in Symptomatic Prostate Cancer Patients). 129 This study suggests that radium-223 chloride compared with placebo plus BSC, including strontium-89, significantly improves overall survival in patients with CRPC that has spread to the bone. Roughly 90% of men with castration-refractory disease suffer from painful bone metastases which are currently not treated directly; these patients receive ‘best supportive care’, which includes strontium-89 to relieve pain.

Strontium-89 is included in the base-case model as an additional cost near the end of life; however, the health benefit of pain relief is not accounted for in the model. The model structure was therefore adapted to allow for the small survival improvement at the end of the pathway, using the survival difference reported in the ALSYMPCA trial. 129 However, as the radium-223 chloride did not yet have a list price and was scheduled for review by NICE, this topic was not evaluated using the full guideline model. The structure of the model would easily allow such an evaluation in the future.

Topic H: intensity-modulated radiation therapy and image-guided radiation therapy versus conformal radiotherapy

Intensity-modulated radiotherapy and image-guided radiation therapy (IGRT) have been recommended by the Department of Health National Radiotherapy Advisory Group. Neither intervention was evaluated by NICE in CG58. 54

A recent HTA report conducted a thorough review of the clinical evidence and found eight studies reported across 13 publications. 105 The authors concluded that ‘the studies are too heterogeneous both for meta-analysis and to attempt to identify variation in effects by dose’ . Given the limitations described, the authors restricted their economic analysis to those studies that reported biochemical relapse-free survival. They used the outcomes from each study as a different scenario in evaluate in their economic model ( Table 11 ).

Modelling scenarios in Hummel and colleagues 2010

We replicated the third modelling scenario (based on data from Morgan and colleagues 132 ) from Hummel and colleagues 105 using biochemical recurrence data, frequency of sexual function and urinary adverse events from Widmark and colleagues. 96 We also included an increased frequency of bowel adverse events with intensity-modulated radiation therapy (IMRT), as reported in Vora and colleagues. 133

Topic I: active surveillance in previously unscreened ‘low-risk’ men

This topic suggestion indicates that men with low-risk disease according to the D’Amico classification are a heterogeneous group and implies ‘Active Surveillance’ may not be the optimal treatment strategy for some of these patients. However, the question is vague as it does not propose an alternative risk classification system or an alternative treatment pathway to evaluate. Although both of these options could in principle be evaluated using the pathway model, no modelling was attempted for this topic as further work would be needed to define an answerable clinical and economic question.

Table 12 summarises the modelling undertaken for each update topic, the structural impact on the base-case model and the data requirements for each scenario. Full details of the data used are given in Appendix 5 .

Summary of amendments to evaluate update topics

Base-case estimates of costs and health outcomes

The results of the base-case model provide a mean estimate of the numbers of patients in each section of the pathway and the associated mean costs and mean health effects (life-years and QALYs gained) for the total cohort of patients in the model. Table 13 shows the estimated number of men in each section of the base-case (current service) model, reported for a cohort of 1000 men referred into secondary care with suspected prostate cancer. The analysis suggests that just over 20% of men presenting to secondary care with symptoms suspicious of prostate cancer will be diagnosed with prostate cancer.

Intermediate results (per 1000 men referred)

On average, around one in three men diagnosed with prostate cancer will receive ‘watchful waiting’ and 1 in 10 men will receive AS. Approximately 60% of men diagnosed with prostate cancer will receive some form of radical treatment, including those men who switch to treatment after some time of AS. Approximately half of all men diagnosed with prostate cancer will at some point in their lives receive hormone treatment. Around one-third of men diagnosed with prostate cancer are expected to receive palliative chemotherapy.

Table 14 shows the contribution of each segment of the model to overall life-years gained and QALYs gained. The results show that, on average, each man referred into secondary care with suspected prostate cancer can expect to live for 13.96 years and will accrue around 11.18 QALYs (undiscounted).

Health outcomes by base-case model segment (per 1000 men referred)

The costs associated with each workcentre within the model are shown in Table 15 . The model suggests that over the lifetimes of a cohort of 1000 men, expenditure on radical treatment is almost three times that of palliative treatment. For the 1000 men referred, the expected discounted lifetime cost associated with the base-case configuration of UK services is around £6.5M (£6500 for each man referred).

Costs from the base-case model (per 1000 men referred)

Incremental cost-effectiveness analysis

Table 16 presents the mean service costs and health outcomes for all 13 possible variations to the pathway, based on 1000 patients. A full incremental analysis was undertaken within each guideline topic; these results are described below.

Summary of mean costs and outcomes for all options tested (per 1000 men referred)

Three possible alternatives were compared in topic A. In the base-case model we assumed that 50% of men with high-risk or locally advanced disease would receive radiotherapy with adjuvant hormone treatment, whereas the remaining 50% would receive hormone treatment alone. Option A2 assumed that all eligible men would receive radiotherapy with adjuvant hormone treatment. Option A3 assumed that all eligible men would receive hormone treatment alone.

The results of the analysis are shown in Table 17 . This suggests that offering all men with high-risk or locally advanced disease radiotherapy in addition to hormone treatment, rather than hormone treatment alone, is expected to be the most effective and the most expensive option. Offering hormone therapy alone is expected to produce the fewest QALYs and the lowest overall cost. The base-case service, which involves a combination of the other two options, is ruled out due to extended dominance. Radiotherapy plus hormone therapy compared with hormone therapy alone is expected to yield a discounted ICER of around £1522 per QALY gained.

Incremental cost-effectiveness results: topic A (per 1000 men referred)

Figure 10 presents cost-effectiveness acceptability curves for the three options compared in topic A. At very low values of λ (when health is valued less) hormone therapy alone is expected to produce the greatest net benefit (NB). At threshold values of around ≥ £5000, radiotherapy plus hormone therapy is expected to produce the most NB. Assuming a willingness-to-pay threshold of £20,000 per QALY gained, the probability that radiotherapy plus hormone therapy produces the greatest NB is approximately 1.0.

Cost-effectiveness acceptability curves: topic A.

Topic B: surgical techniques for localised prostate cancer

Topic B involves a comparison of four alternative surgical procedures for men eligible to undergo radical prostatectomy. The base-case strategy assumed all men would undergo a standard open procedure. Option B2 assumed that all men would have a PRP. Option B3 assumed men would have a LRP. Option B4 assumed that all men would receive a RALRP.

The results of the economic analysis of this topic are presented in Table 18 . Unsurprisingly, the model results indicate very little difference in terms of incremental health gains between the evaluated options. The model suggests that RALRP (option B4) is associated with a slight increase in QALYs compared with the other options. This is also the least expensive option, hence it is expected to dominate all other options.

Incremental cost-effectiveness results: topic B (per 1000 men referred)

Figure 11 presents cost-effectiveness acceptability curves for the four options compared in topic B. Assuming a willingness-to-pay threshold of £20,000 per QALY gained, the analysis shows that RALRP is always likely to produce the greatest NB, compared with the standard RRP open procedure. However, there is still considerable structural uncertainty with respect to the duration of adverse events and the costs of managing these, which are not addressed with probabilistic sensitivity analysis.

Cost-effectiveness acceptability curves: topic B.

Topic C/D: brachytherapy + external beam radiotherapy for men with localised or locally advanced prostate cancer

The evaluation of topic C/D involved a comparison of five alternative options. The base-case model assumes that patients with intermediate-risk disease will receive radiotherapy plus hormone treatment and those with high-risk disease may receive radiotherapy plus hormones or brachytherapy monotherapy. Option CD2 involve brachytherapy in combination with external beam radiation as high dose. Option CD3 involves brachytherapy in combination with external beam radiation as low dose. Option CD4 represents brachytherapy monotherapy. Option CD5 represents radiotherapy plus adjuvant hormone treatment.

Table 19 presents the results for the economic analysis of topic C/D. The results suggest that brachytherapy monotherapy (option CD4) is associated with the highest expected QALY gain and the lowest cost. All other options, including the current base case, are dominated by this strategy.

Incremental cost-effectiveness results: topic C/D (per 1000 men referred)

Figure 12 presents the cost-effectiveness acceptability curves for topic C/D. Assuming a willingness-to-pay threshold of £20,000 per QALY gained, the probability that brachytherapy monotherapy produces the greatest NB is approximately 0.84.

Cost-effectiveness acceptability curve: topic C/D.

Topic F involved the economic comparison of three options. The base-case model assumed 90% of men who received either continuous or intermittent hormone treatment would receive luteinising hormone-releasing hormone analogue (LHRHa) continuously, with 10% receiving LHRHa intermittently. Option F2 represents continuous hormone therapy and option F3 represents intermittent hormone therapy.

The results of the economic analysis of topic F are presented in Table 20 . These suggest that continuous hormone treatment is expected to produce the greatest number of QALYs at the highest cost. Option F3 (intermittent hormones) is expected to be dominated by the base-case service. Continuous hormone therapy is expected to cost approximately £2700 per QALY gained when compared with the base-case service.

Incremental cost-effectiveness results: topic F (per 1000 men referred)

Figure 13 presents the cost-effectiveness acceptability curves for topic F. Assuming a willingness-to-pay threshold of £20,000 per QALY gained, the probability that continuous hormone therapy produces the greatest expected NB is approximately 0.87.

Cost-effectiveness acceptability curve for topic F.

Topic H: intensity-modulated radiation therapy versus conformal radiotherapy

Topic H compares IMRT (option H2) against the base-case strategy (3D-conformal radiotherapy). The headline economic results are presented in Table 21 . The economic analysis suggests that IMRT is expected to result in fewer QALYs and a greater expected cost than 3D-conformal radiotherapy.

Incremental cost-effectiveness results: topic H (per 1000 men referred)

Figure 14 presents cost-effectiveness acceptability curves for topic H. Assuming a willingness-to-pay threshold of £20,000 per QALY gained, the probability that 3D-conformal radiotherapy produces more NB than IMRT is approximately 0.99.

Cost-effectiveness acceptability curves: topic H.

• Summary of economic results and implications for updating guideline topics

Table 22 summarises the results of the economic analyses in terms of the expected absolute net benefit for each option and the probability that each option produces the greatest net benefit as compared with other options within each guideline topic. The option which is preferred on the grounds of expected cost-effectiveness within each topic is highlighted in bold.

Summary of NB results (per 1000 men referred)

For topics A, B, C/D and F, the economic analysis indicates that the current base-case service is not expected to produce the greatest amount of net benefit. In each of these circumstances, additional health benefits may be attainable by pursuing other treatment options. This would indicate that these topics should be considered for update in the guideline. On the basis of the magnitude of expected INB lost between each option as compared with the base-case service, topics A, C/D and F represent the top three priorities for update on economic grounds. Topic B is also associated with lost net benefit, although this is less than that for the other topics.

The full guideline model presented within this chapter captures the key events, costs and health outcomes associated with the main elements of care for men referred into secondary care with suspected prostate cancer. The model reflects the broad range of components of the care pathway including diagnosis and imaging, GP monitoring, treatment planning, watchful waiting, AS, radical treatments, follow-up care and palliative treatments. The full guideline model differs from conventional piecewise models in that it adopts a broader pathway-level scope while retaining a high level of depth across the individual pathway components. Although most conventional models are developed to address a single decision problem at a specific decision point in a care pathway, this full guideline model provides a platform for the evaluation of multiple options for service change across the whole service pathway. Although these are presented solely as analyses of individual guideline topics, the model also has the functionality to evaluate multiple topics simultaneously. This represents a more powerful decision-making tool than has been used in the majority of existing CGs.

Headline probabilistic model results

We evaluated six of the nine selected guideline topics. Our analysis indicates that for five of these topics the current guideline recommendations are not expected to produce the greatest NB. Although these results are not definitive – as, for example, they are not based on systematic reviews of the evidence – they are indicative of areas where further investigation is likely to be of value. In particular, the economic analysis indicates that:

• offering all men with high-risk or locally advanced disease radiotherapy plus hormone treatment is expected to have an ICER of around £1500 per QALY gained when compared with hormone therapy alone
• RALRP is expected to dominate all other surgical options for localised prostate cancer
• brachytherapy monotherapy is expected to dominate alternative radiotherapy options for men with localised or locally advanced prostate cancer
• continuous hormone treatment is expected to yield an ICER of around £2700 compared with the current mix of continuous and intermittent hormone treatments
• 3D-conformal radiotherapy is expected to dominate intensity-modulated radiotherapy.

In terms of net benefit lost (by choosing the base-case service over other potentially more cost-effective decision alternatives), the following three topics represent the highest priorities for update:

• Topic A: Pelvic radiotherapy with adjuvant hormone therapy for men with localised prostate cancer.
• Topic C/D: Brachytherapy plus external beam radiotherapy for men with localised or locally advanced prostate cancer.
• Topic F: Intermittent hormone therapy compared with continuous hormone therapy for men with metastatic prostate cancer.

Evidently, there is disagreement between the topics which would be prioritised on economic grounds and those which would be prioritised by the stakeholders who responded to our survey (see Chapter 3 ). Although both the economic analysis and the surveys indicate that some benefit may be obtained by prioritising topic B (surgical techniques), this is associated with a comparatively small amount of net benefit lost relative to the base-case service.

Limitations of the analysis

As with any health economic model, the credibility of this model and its results are largely dependent on the quality of the evidence used to inform it. There are a number of limitations of the economic analyses presented here, the majority of which derive from limitations in the evidence base. It is important to recognise that most of these problems are not a result of the modelling methodology itself; rather, the same problems with evidence would apply to the development of any health economic model, irrespective of its scope. One of the key values of mathematical modelling, in particular modelling on this scale, is its ability to draw out the key gaps and uncertainties in the evidence base. The following key simplifications should be borne in mind when interpreting the results of the economic analysis:

• The available registry data did not include the PSA score at diagnosis. Gleason score is assumed to be fixed from the point of diagnosis when in reality this could change following a repeat biopsy.
• PSA-based criteria for informing treatment decisions were not fully captured in the active treatment portions of the model.
• The potential impact of misclassification of diagnostic tests was not reflected in the model because of the inherent difficulties of modelling inaccurate diagnoses and the impact on outcomes. In addition, test operating characteristics were captured only for TRUS.
• The model includes only a partial representation of disease natural history – the model does not include the incidence of prostate cancer over time – that is, all patients either have or do not have prostate cancer on model entry.
• Much of the evidence used to inform the treatment portions of the model required naive indirect comparisons due to a lack of randomised evidence.
• We assumed that biochemical progression and disease recurrence have an equivalent impact on clinical decision-making and subsequent prognosis.
• Survival benefits for sequences of palliative treatments were assumed to be driven by the first-line treatment in the sequence. In addition, sequences of palliative treatments are modelled according to an overall mean time and do not fully reflect treatment variations between patients.

Key evidence limitations and model simplifications

The adoption of an individual patient-level simulation approach can place heavy demands on a model in terms of data. In the absence of well reported summary statistics, such as variance-covariance matrices across multiple patient characteristics, access to individual-level data on patient characteristics at model entry is essential to fully characterise the correlations between the key patient characteristics. We used UK cancer registry data on age, clinical stage and Gleason score from the SWPHO registry. This registry data set did not however include information on patients’ PSA scores; instead these were ‘back-calculated’ conditional on the characteristics for which we had data. Furthermore, we did not identify any robust evidence concerning the relationship between PSA score, underlying disease progression and treatment. We also necessarily assumed that Gleason score was fixed from the point of diagnosis as we found no evidence to reflect the potential trajectory of change in Gleason score over time. Given these issues, the lack of evidence invariably limits the level of depth (or detail) reflected in the pre-diagnostic portion of the model. The consequence of this lack of evidence led to certain elements of the model becoming ‘blunt’ and in some instances to a separation between our conceptual understanding of how diagnostic and treatment decisions are made in practice and the extent to which the model can reflect these decisions. As a consequence, we were unable to capture any of the NICE prostate cancer guideline CG58 recommendations based on observed changes in PSA score, doubling time or velocity.

In addition, the simulation model includes only a partial representation of the natural history of prostate cancer. As a result, the portion of the model dealing with the underlying natural history and diagnosis is fairly simplistic. This set of simplifications was driven by significant limitations in the available evidence base with respect to underlying natural history progression and the lack of good-quality evidence relating to the probabilities and consequences of incorrect diagnostic decision-making. We did not assess the impact of the error associated with PSA testing, MRI or bone scans. We also assumed TRUS-guided biopsy was associated with perfect specificity and the evidence used to estimate the false-negative rate is dated. 90 As with the evaluation of any diagnostic intervention, the lack of evidence regarding the costs and consequences of counterfactual pathways that would be followed by patients with misclassified disease presents a further challenge which we chose not to fully address within the model.

Our estimation of the natural history of prostate cancer was crude, calibrated using data on patients in the watchful waiting arm of the Bill-Axelson and colleagues RCT 78 and UK-specific life tables using the Metropolis–Hastings algorithm. Although we believe the calibration method is appropriate, undoubtedly other information sources would tell us something about these unobservable parameters. This could include evidence from screening trials, autopsy studies or evidence from prostate cancer surveillance and monitoring studies. The design and implementation of a more comprehensive calibration process would increase the robustness of the natural history estimation, but would require considerable additional effort and resource. Given that none of the topics selected for evaluation actually related to diagnostic interventions or screening, this additional effort would have had, at best, a limited payoff for the context of the case study. However, it is acknowledged that the value of explicitly modelling epidemiology and natural history progression may be greater in guidelines for other diseases. Further extension of this component of the model may increase the utility of the model in addressing other decision problems elsewhere in the prostate cancer pathway.

The treatment portion of the model is also subject to a number of problems relating to the availability and quality of evidence. No modelling approach can reconcile the absence of head-to-head trials comparing all relevant radical treatment options and a comprehensive evidence network. In most instances, we had little choice but to use naive indirect comparisons to capture the relative effects of radical and palliative treatments. This breaks randomisation between studies and can lead to significant bias and confounding. However, again, we believe this problem lies in the evidence base rather than the modelling approach per se. In other instances, we were also limited by relevant trials reporting less relevant or useful outcomes. The palliative treatment portion of the model is intended to reflect the impact of different sequences of treatments on HRQoL and survival. However, we did not identify any studies which assessed planned sequences of treatment. As such we assumed that the first-line treatment determines the survival benefit for the sequence, with subsequent treatments influencing the amount of time for which the patient is progression-free. This is a common problem in cancer evaluations and is again not specific to this particular modelling methodology. In addition, because PFS includes survival as an event, we were unable to reflect first-order uncertainty in this part of the model.

• Usefulness of the broader modelling approach

Volume of economic evidence generated

A total of nine topics were selected for evaluation. Two of these topics dealt with potentially competing interventions at the same point in the pathway (topics C and D). Six topics were subjected to formal CEAs using the full guideline model. The economic analysis of three topics was not attempted. Topic E was not evaluated due to a total lack of evidence, topic G because the intervention did not have a list price (and is not being actively marketed), and topic I was not undertaken as the question was not sufficiently defined to identify the relevant comparator(s).

It is reasonable to suggest that the full guideline model provides considerably more economic information than would otherwise be available from the conventional piecewise approach. It remains unclear, however, whether or not the resources and effort required to develop the full guideline model exceeds what would be required to undertake the same economic analyses using five individual de novo piecewise models.

With the exception of topic I (AS strategies for previously unscreened men), all of the topics selected for the case study reflect active treatments for diagnosed disease. In principle, the full guideline model could also be used to address a wide range of other decision problems across the prostate cancer service which were not selected for inclusion in the update (e.g. assessing the optimal frequency of GP monitoring visits or assessing alternative biopsy techniques).

Development time

The time and resource required to develop the model were considerable. Model development began in August 2010 and the final results were produced in November 2012. Two modellers were involved in designing and implementing the model. It is reasonable to suggest that a considerable proportion of this time involved developing familiarity with the software package and the inevitable learning curve associated with developing models on this scale. It may have been possible to develop the same model within the timescales of a ‘live’ CG, although this could represent a risk to the delivery of the guideline. The magnitude of this risk will inevitably vary across different guidelines and different disease areas. Alternatively, it may be possible to develop this type of model before the CG development process begins.

Problems of the approach

Although the adoption of a broad model scope is attractive in terms of the volume and consistency of economic evidence that can be generated using a single model, it does carry with it a number of potential risks and costs. For example, one could argue that the scope was too broad – we modelled the breadth of the whole pathway from secondary care referral yet only topics related to treatment were evaluated using the model. Thus, considerable development time was devoted to developing parts of the model for which topics were not actually prioritised. Of course, the case study was not undertaken as part of a live guideline process and the model may have potential for evaluating a wider range of topics than those selected for inclusion in the case study.

The development of a single model may offer consistency but also carries a cost in terms of running the evaluation. It remains debateable whether simulation models are easier or harder to check than cohort-based models. However, when an error is identified, either in the conceptual or quantitative basis of the model, this error will influence all decision problems addressed using that model. Where errors are spotted, this can mean rerunning all analyses, potentially multiple times. Where errors are not spotted, these may permeate through the evaluation of multiple topics (although the precise nature of the error will determine whether or not this makes a difference to the conclusions of any individual topic). Given the likely computational burden associated with this type of model, this can represent a negative aspect of the approach. In this case study, all analyses were rerun five times each of which required approximately 1200 computation hours. Although this was not ideal, it was possible by spreading the model runs across multiple computers using multithreading; while not substantial in this case, pursuing this type of modelling approach may have implications for purchasing both hardware and software.

A final potential problem relates to how this type of large-scale complex model would be interpreted by a GDG. We did not have access to the GDG itself and so we were unable to gauge whether they would find this type of model more or less useful than conventional piecewise models. Testing of the approach, and the qualitative elicitation of the views of GDG members should be considered a priority for future research.

Included under terms of UK Non-commercial Government License .

• Cite this Page Lord J, Willis S, Eatock J, et al. Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project. Southampton (UK): NIHR Journals Library; 2013 Dec. (Health Technology Assessment, No. 17.58.) Chapter 4, Case study 1: full guideline model for prostate cancer.
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Images pf a 64-year-old male patient who underwent MRI for clinical suspicion of prostate cancer (external test set). (RadiologicalSoc.NorthAmerica via SWNS)

The model can be used alongside doctors to improve detection rates of the killer disease.

AI just as good as trained radiologists at detecting prostate cancer: study

• By Talker News
• Aug 6, 2024
• Aug 6, 2024 Updated 8 hrs ago

By Stephen Beech via SWNS

Artificial intelligence is as good as trained radiologists at detecting prostate cancer, according to a new study.

Scientists hope the model can be used alongside doctors to improve detection rates of the killer disease.

Prostate cancer is the second most common cancer in men worldwide, and around one in eight men in the UK will be diagnosed with it in their lifetime.

Now a deep learning model has been found to perform at the level of an abdominal radiologist in the detection of clinically significant prostate cancer on MRI.

Radiologists usually use a technique that combines different MRI sequences, called multiparametric MRI, to diagnose clinically significant prostate cancer.

Results are expressed through the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS), a standardized interpretation and reporting approach.

However, lesion classification using PI-RADS has limitations, according to the American research team.

Study senior author Dr. Naoki Takahashi, of the Mayo Clinic in Rochester, Minnesota, said: “The interpretation of prostate MRI is difficult.

“More experienced radiologists tend to have higher diagnostic performance.”

Applying artificial intelligence (AI) algorithms to prostate MRI has shown promise for improving cancer detection and reducing observer variability, which is the inconsistency in how people measure or interpret things that can lead to errors.

However, a major drawback of existing AI approaches is that the lesion needs to be annotated by a radiologist or pathologist at the time of initial model development and again during model re-evaluation and retraining after clinical implementation.

Dr. Takahashi said: “Radiologists annotate suspicious lesions at the time of interpretation, but these annotations are not routinely available, so when researchers develop a deep learning model, they have to redraw the outlines.

“Additionally, researchers have to correlate imaging findings with the pathology report when preparing the dataset. If multiple lesions are present, it may not always be feasible to correlate lesions on MRI to their corresponding pathology results.

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"Also, this is a time-consuming process.”

Dr. Takahashi and his team developed a new type of deep learning model to predict the presence of clinically significant prostate cancer without requiring information about lesion location.

They compared its performance with that of abdominal radiologists among a large group of patients without known clinically significant prostate cancer who underwent MRI at multiple sites of an academic institution.

The researchers trained a convolutional neural network (CNN) - a sophisticated type of AI that is capable of discerning subtle patterns in images beyond the capabilities of the human eye - to predict clinically significant prostate cancer.

Among 5,735 examinations in 5,215 patients, 1,514 showed clinically significant prostate cancer.

On both the internal test set of 400 exams and an external test set of 204 exams, the deep learning model’s performance in clinically significant prostate cancer detection was not different from that of experienced abdominal radiologists.

A combination of the deep learning model and the radiologist’s findings performed better than radiologists alone on both the internal and external test sets, according to the findings published in the journal Radiology .

Since the output from the deep learning model does not include tumor location, the researchers used something called a gradient-weighted class activation map (Grad-CAM) to localize the tumors.

The study showed that for true positive examinations, Grad-CAM consistently highlighted the clinically significant prostate cancer lesions.

Dr. Takahashi sees the model as a potential assistant to the radiologist that can help improve diagnostic performance on MRI through increased cancer detection rates with fewer false positives.

He said: “I do not think we can use this model as a standalone diagnostic tool.

“Instead, the model’s prediction can be used as an adjunct in our decision-making process.”

The researchers have continued to expand the dataset, which is now twice the number of cases used in the original study.

Dr. Takahashi says the next step is to study how radiologists interact with the model’s prediction.

He added: “We’d like to present the model’s output to radiologists and assess how they use it for interpretation and compare the combined performance of radiologist and model to the radiologist alone in predicting clinically significant prostate cancer."

Originally published on talker.news , part of the BLOX Digital Content Exchange .

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• Systematic Review
• Open access
• Published: 01 August 2024

The preventive and carcinogenic effect of metals on cancer: a systematic review

• Amir Hossein Khoshakhlagh 1 ,
• Mahdiyeh Mohammadzadeh 2 , 3 &
• Agnieszka Gruszecka-Kosowska 4  

BMC Public Health volume  24 , Article number:  2079 ( 2024 ) Cite this article

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Metrics details

Many studies have investigated the role of metals in various types of malignancies. Considering the wide range of studies conducted in this field and the achievement of different results, the presented systematic review was performed to obtain the results of investigations on the prevention and occurrence of various types of cancer associated with metal exposures.

In this review, research was conducted in the three databases: Scopus, PubMed, and Web of Science without historical restrictions until May 31, 2024. Animal studies, books, review articles, conference papers, and letters to the editors were omitted. The special checklist of Joanna Briggs Institute (JBI) was used for the quality assessment of the articles. Finally, the findings were classified according to the effect of the metal as preventive or carcinogenic.

The total number of retrieved articles was 4695, and 71 eligible results were used for further investigation. In most studies, the concentration of toxic metals such as lead (Pb), chromium (Cr (VI)), arsenic (As), cadmium (Cd), and nickel (Ni) in the biological and clinical samples of cancer patients was higher than that of healthy people. In addition, the presence of essential elements, such as selenium (Se), zinc (Zn), iron (Fe), and manganese (Mn) in tolerable low concentrations was revealed to have anti-cancer properties, while exposure to high concentrations has detrimental health effects.

Conclusions

Metals have carcinogenic effects at high levels of exposure. Taking preventive measures, implementing timely screening, and reducing the emission of metal-associated pollutants can play an effective role in reducing cancer rates around the world.

Peer Review reports

Introduction

Currently, one of the main causes of morbidity and mortality worldwide is cancer. More than 1 person out of 5 people get cancer [ 1 ]. Based on projections, the number of new cancer patients will increase from 14.1 million reported in 2012 to 21.6 million estimated in 2030 [ 2 ]. Lifestyle behaviors such as obesity, smoking, and unhealthy diet, genetic changes, chronic diseases, and environmental interactions play key roles in cancer etiopathogenesis [ 3 , 4 ].

Cancer is a disease caused by the uncontrolled and unnormal cell growth, resulting in the possibility to invade and metastasize any part of the body and formation of a tumor [ 5 ]. There are several types of cancer, including carcinoma, sarcoma, lymphoma, and leukemia [ 6 ]. The causes of cancer are mutations in the genes responsible for cell growth and division control [ 6 ]. Among the causes of the mutations, genetic inheritance, hormonal imbalances, and exposure to environmental factors such as certain chemicals can be mentioned [ 6 ]. Cancer pathophysiology involves three stages. The first one is caused by the initiation of mutations in the cell DNA leading to the activation of cancer genes and the inactivation of tumor suppressor genes. In the second stage promotion of the mutation cell takes place, which is stimulated by the differentiation and rapid growth of the mutation cell, forming a small cluster of abnormal cells. In the third stage the progression takes place, where abnormal cells continue to divide and grow, forming tumors that invade the surrounding tissue, and spread to other parts of the body through the blood flow or lymph system [ 6 ].

Studies have shown that various metals can impact on cancer induction by the same mechanisms, such as manipulating the state of chromatin and gene expression [ 7 ] or producing ROS and increasing oxidative stress [ 8 ]. Nickel was found to activate the signal pathways induced by hypoxia, mediated by competition with Fe in the prolyl-hydroxylase [ 8 ]. Arsenic and Cd have also been shown to compete or replace important metals such as Zn and Ca in proteins as the main mechanisms of cell gene and cell toxicity [ 8 ]. Also, As and Cd were found to suppress cell autophagy, which is an important factor in tumor suppression [ 8 ]. Considering genetic damage through both oxidative and nonoxidative (DNA adducts) mechanisms, metals were revealed to cause significant changes in DNA methylation and histone modifications, that results in epigenetic silencing or reactivation of gene expression [ 9 ]. Moreover, it was revealed in in vitro genotoxicity experiments and animal carcinogenicity studies that metals can cause cocarcinogenic and comutagenic effects as metals are likely to interfere with DNA repair processes [ 9 ].

Existing reports estimate that environmental factors, such as insecticides and pesticides, pollutants in air, water, and soil, cause 24% of the global disease burden measured in healthy life years lost and 23% of all types of premature deaths [ 10 ]. Studies indicate that the difference in exposure to environmental-related risk factors and the ability to obtain adequate health care increase the environmental burden of the apperance of diseases in developing countries 15 times compared to developed countries [ 11 , 12 , 13 ]. Environmental anthropogenic changes, such as water, soil, and air pollution, the growth of industrial activities, agricultural practices, the use of chemical fertilizers and pesticides, and food processing, play an important role in increasing the incidence of cancer by disrupting the balance of trace elements and metals in the environment [ 14 , 15 ].

The mechanism of toxicity and carcinogenicity of toxic metals is presented in Fig. 1 . After entering the body through exposure via digestion, inhalation, and dermal contact, metals can accumulate in the vital organs of the body such as the liver, kidney, and bones [ 16 ]. This feature can cause complications caused by the toxic effects of these elements in humans, including digestive system and kidney dysfunction, immune system dysfunction, nervous system disorders, vascular damage, birth defects, skin lesions, and epigenetic processes that lead to cancer [ 17 , 18 ]. These processes lead to the deactivation of tumor suppressor genes, DNA repair enzymes, the transformation of proto-oncogenes into oncogenes, as well as changes in DNA methylation [ 19 ]. Metals also significantly affect the development of malignancies by activating redox-sensitive transcription factors, a protein that signals pathways involved in cell growth, apoptosis, disruption of cycle regulation in cells, as well as cell differentiation [ 20 , 21 ], histone modifications and non-coding RNA expression [ 22 , 23 ]. In addition, the initiation and progression of cancer have been found to be related to oxidative stress and the activation of inflammatory mediators. Connecting and activating transporters and cell surface receptors [ 24 ], activating metallothioneins [ 25 ] and specific enzymes and modulating selected intracellular kinases and phosphatases [ 26 , 27 ] are other mechanisms of metal carcinogenesis. The following protein kinase: extracellular signal-regulated (ERK1/2) and protein kinase B (PKB or AKT) are the main elements of intracellular signaling that are effective in the cell proliferation regulation process [ 28 , 29 ] and are sensitive to increasing/decreasing metal concentrations [ 29 ].

Mechanism of toxicity and carcinogenicity of metals after human exposure

Although the carcinogenic role of metals has been proven in many studies [ 30 , 31 , 32 ], some researchers have reached contradictory results. The results of a recent case–control study showed that there was an inverse relationship between plasma Se levels and the chance of developing renal cell carcinoma [ 33 ]. Also, many studies reported higher concentrations of Mn and Fe in healthy people than in patients with various types of cancer such as lung [ 34 ], brain tumors [ 35 ], testicular [ 31 ], kidney [ 36 ], and non-melanoma skin [ 37 ].

However, many metals, such as copper (Cu), iron (Fe), selenium (Se), strontium (Sr), manganese (Mn), zinc (Zn), and molybdenum (Mo), are essential for life at low concentrations [ 38 ]. These metals have protective roles in processes such as chromosome damage and oxidation [ 38 , 39 ]; and by regulating cell metabolism and DNA, RNA, and protein synthesis, they have preventive effects on cancer [ 40 ]. They also act as cofactors for some antioxidants such as superoxide dismutases [ 41 ], play a role in cell differentiation and apoptosis, and are essential for all stages of the cell cycle [ 42 ]. However, despite the evidence demonstrating their preventive role in cancer [ 33 , 34 , 43 ], if their concentration exceeds the body's homeostatic capacity, they can lead to degenerative conditions and even cancer [ 44 ].

The development of knowledge and technology in recent decades has led to the use of various solutions to prevent the spread of metals in the environment and reduce the adverse effects of exposure to these elements. Among these solutions, we can mention using Nanomaterials to reduce the consumption of substances containing heavy metals in the water remediation process [ 45 ] and chelating agents and barrier creams to prevent excessive exposure to heavy metals [ 46 ]. In addition, global removal of lead from gasoline, control of exposure to As in drinking water in Chile [ 47 ], and using bio-filters to remove these elements from wastewater [ 48 ] and landfill leachate [ 49 ] are among the preventive measures taken in this field. But considering the increasing spread of metals in the environment and the chronic exposure of people to these toxic elements, it seems that the measures taken are not enough.

Considering the broad exposure of humans to metals, the important role of these elements in cancer causation, and the contradictory results in various studies, our systematic review was performed to retrieve the scientific literature without historical restrictions until May 31, 2024. The main aims of this review were as follows: 1) evaluation of the types and concentrations of metals in the environmental or biological samples of exposed people, 2) investigation of the carcinogenic effect associated with metal concentrations, and 3) estimating the potential role of controlling metal exposures in cancer prevention.

Study protocol

The presented systematic review complies with the statement of Preferred Reporting Items for Systematic Review and Meta-analyses guidelines (PRISMA) [ 50 ] and fully adherence with the protocol that was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (registration number CRD42023397867) on 8 February 2023.

PECO statement

To develop the research question, a Population, Exposure, Comparator, and Outcome (PECO) protocol was used, and the statement is presented in Table 1 . In this statement, the exact purpose of the present systematic review, the terms of search, and the inclusion and exclusion criteria of studies related to the impact of exposure to different levels of metals and their impact on cancer occurrence or prevention were specified.

Search strategy and study selection

To access all studies presenting the effects of metals on the prevention or occurrence of cancer in humans, a systematic search without historical restrictions was conducted until May 31, 2024 in the following databases: Scopus, PubMed, and Web of Science for the following keywords: “heavy metal*”, “trace element*”, “trace metal*”, cancer, tumor, carcinogen, cancerogenic, neoplasm, oncogen*, neoplasia*, malignancy.

Criteria of study entering and extracting

In the review performed, studies focused on the effects of metals on cellular and biochemical changes (without examining the metal effect on the cancer appearance) were excluded. In addition, animal studies, books, review articles, conference papers, and letters to editors were omitted. In this systematic review, only original peer-reviewed articles in English were investigated. From the selected studies information on the name of the authors, publication year, study design, country, number of people in the investigated human subpopulation, their age, gender, type of metals, type of environmental and human samples, average metal concentration in samples, and type of cancer were gathered.

Quality assessment

The quality of the investigated studies was evaluated by two researches (M.M. and A.H.Kh.) independently, and the Joanna Briggs Institute (JBI) checklists for cohort studies, case–control studies, and analytical cross-sectional studies were used for this purpose. The JBI checklists assess the risk of bias in studies by asking 8 questions related to sample selection criteria, exposure assessment, confounding factors, and appropriate statistical analysis. According to the percentages assigned to each of the answers in the questionnaire (“yes”, “no”, “unclear”, or “not applicable”), the quality of the articles was determined at 3 levels, namely: 1) Q1 of high quality and low risk of bias (answer “yes” in ≥ 50–75%), 2) Q2 of average quality and unclear risk of bias (answer “unclear” in ≥ 50–75%), and 3) Q3 of low quality and high risk of bias (answer “no” in ≥ 50–75%) [ 51 ]. All articles that were of adequate quality were included in the study.

Result synthesis

Meta-analysis as quantitative synthesis was not suitable in this study, as we obtained a too diverse range of study designs and other heterogeneities were found in methodological and contextual aspects. Therefore, the results of the study, which included types of metals, the mean concentrations of metals in the samples, the type of cancer, and the role of metal in the prevention or appearance of cancer (Table S1), were narratively combined. A narrative synthesis was performed in two stages, including (1) initial synthesis using general grouping based on preventive/carcinogenic role and (2) exploration of associations within and between studies to investigate the relationship between metal exposure levels and severity of the outcome.

The entire process of the present systematic review which was carried out by the research team members shown in Fig. 2 . This process includes 7 steps as follows: topic selection, keywords extraction, systematic search, screening and data extraction, evaluating risk of bias, resolving contradictions and ambiguities, and synthesizing results.

The seven stages of the systematic review management process

Results and discussion

Study selection.

The process of articles selection performed in this study is presented in Fig. 3 using a PRISMA flow diagram. Through the systematic search, 4,695 articles were retrieved, of which 1558 were obtained from Web of Science, 2553 from the Scopus, and 584 from the PubMed database. After removing 983 duplicates, 3712 articles were screened by title and abstract. This step resulted in the exclusion of 3556 studies. Then, 156 full texts were subjected to additional check to assess the criteria of the entry and the exit, and quality assessment. Further 85 studies were excluded due to: lack of inclusion criteria ( N  = 25), in detail: not metal (7), benign tumor (4), not relevant (11), report on the cumulative effect of various contaminants on cancers (3), not determining and reporting the concentration of metals (self-reports) ( N  = 34), failure to report original data ( N  = 7), and investigating the effects of metals in cells on a laboratory scale (N = 19). Finally, in the current review, we included the total number of articles equal to 66 studies.

PRISMA flow diagram of the literature search

The studies in this review included 56 case–control studies [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 ], 8 cohort studies [ 37 , 100 , 101 , 102 , 103 , 104 , 105 , 106 ], 7 cross-sectional studies [ 107 , 108 , 109 , 110 , 111 , 112 , 113 ], and one observational study [ 114 ]. They were performed in various countries around the world: 8 in Pakistan, 7 in China, 6 in Taiwan, 5 in Sweden, 5 in USA, 4 in Iran, 6 in Poland, 3 in Turkey, 3 in Tunisia, 3 in Egypt, 2 in Spain, 2 in Romania, 2 in Iraq, 2 in India, 2 in Croatia, and one in Finland-Sweden-Iceland, Japan, Greece, the United Kingdom, New Zealand, United Arab Emirates, Ukraine, Italy, Nigeria, Russia, and Belgium. Four studies investigated metals in the natural environment and 67 in human biological samples such as tissue, serum, urine, blood, hair, and nails. The potential sources of exposure to metals are presented in Fig. 4 .

Environmental sources of exposure to metals based on studies included in this systematic review

The main analytical methods used to measure element concentrations were the Atomic Absorption Spectrophotometry (AAS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). A total of 1,369,887 people were examined in the 66 included studies. Table 2 presents a summary of the included 66 articles (for a complete table, see Table S1).

Toxic metals

The results of the majority of selected studies showed that metals such as Pb, As, Cr, Cd, and Ni act significantly in the development or progression of several types of cancer. Most toxic metals can damage cellular macromolecules such as DNA, RNA, proteins, and lipids by producing superoxide anion radical and hydroxyl radical reactive oxygen species (ROS) through the Fenton reaction and change cellular homeostasis [ 115 ]. In addition, oxidative stress caused by metals can induce genetic and epigenetic changes, abnormal cell signaling, increased micronuclei, chromosomal aberrations and mitotic index, and uncontrolled cell growth [ 116 , 117 ]. In their role as endocrine disrupting chemicals [ 118 ], metals cause interference in the estrogen and androgen signaling pathways and affect the expression of genes involved in the growth and secretory function of the prostate gland [ 119 ].

Metals like Cd, Pb, As, Ni, and Cr(VI) compete with essential elements in the formation of ligands with enzymes and other proteins, including calcium-substituted lead, zinc-substituted cadmium, and aluminum-substituted mentioned with most of the rare elements [ 120 ]. These substitutions cause disturbances in important biochemical reactions, antioxidant imbalance, and adverse effects on various hormonal activities and the functioning of essential enzymes [ 121 ]. Gaman et al. (2021) reported that some metals can cross the blood–brain barrier through this replacement, accumulate in neurons and other brain cells, and potentially cause brain malignancies [ 35 ]. Despite the sensitivity of the placental barrier to toxic substances [ 122 ], metals can pass through the placental barrier by deceiving the transport proteins in the cell membrane of the placenta and causing important biochemical changes during fetal development [ 123 ]. The toxicity of each metal depends on its physicochemical characteristics, as well as on the biological properties of the target cells, in addition to the dose and duration of exposure [ 124 ].

The results of this systematic review showed that most of the examined people were elderly (> 60 years). Therefore, the age of exposed people can also play an important role in various malignancies [ 125 ]. The aging process in humans is associated with significant changes in cells, including telomere wear, genomic instability, epigenetic changes, cell aging, quantitative and qualitative changes in protein spectra, mitochondrial dysfunction, change in intercellular communication, and exhaustion of stem cells [ 125 ].

Studies show that an increase of 1 μg of Cd in urine, increases the risk of lung cancer by 1.25 times [ 94 ]. A case–control study in the Italian population showed that a 1.11-fold increase in Cd in the diet doubles the incidence of melanoma [ 126 ]. Men et al. (2020) showed that the concentration of Cd in the urine of breast cancer patients was almost twice that of the control population [ 89 ], which was consistent with the results of a previous epidemiological study [ 127 ]. Also, the study conducted by Abdul Qayyum et al. (2019) reported double the levels of this metal in the blood and scalp hair of patients with lymphoma [ 30 ]. Meanwhile, some researchers concluded that the levels of Cd in patients with stomach cancer were 1.37 times higher than those of the control group [ 91 ]. Although the difference in the concentration of this element between the group of patients and healthy people reported in [ 30 ] was higher than that of [ 91 ], the highest levels of Cd (case group) in these two studies were equal to 25.04 μg/dL and 344.6 μg/dL, respectively. In addition, the results of a recent case–control study showed that the Cd concentration in the whole blood of testicular cancer patients was 1.57 times higher than that of the control group [ 31 ]; which was consistent with the results obtained by Chang et al. (2016) [ 80 ]. The study examining the impact of heavy metals in urothelial carcinoma, revealed that the urinary concentration of Cd in patients were 1.52 times (1.53 μg/g CR) higher than in healthy people [ 80 ].

Many studies have proven the role of Cd in human lung, kidney, liver, breast, hematopoietic system, bladder, stomach, prostate, and pancreas cancers [ 128 , 129 ]. Cadmium is a toxic metal with a long biological half-life, having low excretion levels related to the absence of efficient elimination mechanisms [ 130 ]. This element is spread in the environment through various sources, including industrial applications as stabilizers in PVC products, Ni–Cd batteries and pigments, fossil fuel combustion, use of phosphate fertilizers, electronic waste recycling, smoking, and volcanic activities and can cause chronic human exposure [ 131 , 132 ].

Cadmium affects several cellular processes including cell cycle progression, proliferation, differentiation, DNA replication, and apoptosis [ 133 ], which may play a major role in Cd genotoxicity [ 134 ]. This process is also true for other diseases caused by exposure to Cd such as lung, prostate, or breast cancer [ 135 ], as well as non-cancerous diseases such as diabetes and cardiovascular diseases [ 136 ]. It can also replace essential elements like Fe, Cu, and Zn in various cytoplasmic and membrane proteins and cause oxidative stress through the Fenton reaction [ 90 ]. Chronic exposure to Cd, in addition to inhibiting the activity of superoxide dismutase, which is known as one of the strongest antioxidant enzymes [ 137 ], also causes dysplastic lesions in the gastric glands [ 138 ].

Studies have shown that Cd creates a key step in the initiation of cancer and tumor stimulation by disrupting E-cadherin at cell junctions [ 139 ]. This mechanism has been reported to accelerate cancer growth by activating proto-oncogenes and genes involved in cell proliferation, reducing p53 function, and inhibiting DNA methylation, causing the clonal expansion of damaged and mutated cells [ 140 ]. Studies have shown that increased oxidative stress caused by exposure to Cd may cause cancer [ 141 ]. Zhang et al. (2016) in an epidemiological study concluded that the concentration of Cd in prostate tissue and plasma from patients with prostate cancer is substantially higher compared to healthy individuals [ 142 ]; this is consistent with the study of Zimta et al. (2019) [ 143 ]. However, a recent meta-analysis did not find any epidemiological evidence of the effect of Cd exposure in the general or occupational population with an increased risk of prostate cancer [ 144 ]. Epidemiological studies indicate that exposure to this heavy metal can increase the risk of lung cancer [ 145 ]. Based on available evidence, a doubling of Cd concentration leads to an approximate 68% increase in the relative risk of lung cancer [ 146 ]. Chronic exposure to Cd causes a significant decrease in the amount of glomerular filtration [ 147 ] and kidney and bladder cancer [ 82 ]. However, some recent studies did not find any relationship between occupational exposure to Cd and kidney cancer [ 36 ]. The study of Madrigal et al. (2019) also revealed that the decrease in kidney function associated with occupational exposure to Cd differs according to gender and the presence of comorbidities, that is, diabetes mellitus and high blood pressure [ 148 ].

Lead is a key player for esophageal and stomach cancers [ 112 ], brain tumors [ 35 ], kidney [ 33 ], thyroid [ 91 ], and testicular cancer [ 31 ]. Metal plating industries, mines, paints, cosmetic products, batteries, electronic waste, and the combustion of fossil fuels containing Pb are among the most important sources of Pb emission in the environment [ 149 ]. Increasing levels of this element in the environment can increase levels of human exposure and cause adverse health effects.

So far, many studies have investigated the effect of Pb concentration on various types of malignancy [ 31 , 92 , 93 ]. In their study, Tariba Lovaković et al. [ 31 ] investigated the effect of heavy metal levels on testicular cancer and concluded that Pb levels in the whole blood of patients (30 μg/L) were 1.25 times those of healthy people (23.9 μg/L). In addition, the results of a case–control study in Pakistan showed that stomach cancer sufferers have 1.8 times more Pb in their blood [ 91 ]; which was consistent with the results of the study by Sarkar et al. (2020) [ 87 ]. In this research, 150 tissue samples (case = 50, control = 100) were evaluated in terms of the concentration of this element and showed that the Pb levels in the case group were 1.15 times higher than those in the control group [ 87 ]. Similar results were observed with other studies on thyroid cancer [ 86 ], breast cancer [ 84 ], and bladder carcinoma [ 82 ].

Furthermore, Chrysochou et al. (2021) observed that the serum Pb level in leukemia patients was 154 times higher than that of healthy individuals [ 93 ], which is consistent with the results of the study by Guzel et al. [ 150 ]. Guzel et al. (2012) indicated that the blood and prostatic Pb levels in patients with prostatic intraepithelial neoplasia are significantly higher than those with benign prostatic hypertrophy (BPH) [ 150 ]. Studies have found a positive correlation between Pb and Al elements with increased levels of methylated MGMT and methylated MLH1, DNA repair enzymes [ 88 ], which is consistent with the results obtained by Scanlon et al. (2017). This study points to an effective role for metals in silencing the MLH1 promoter and in initiating the carcinogenesis process [ 151 ]. Devóz et al. (2017) showed that occupational exposure to Pb causes changes in DNA methylation and gene expression regulation in exposed workers [ 152 ]. The BRAF and KRAS genes are important in the process of the RAS/RAF/MAPK signaling pathway and in this way can regulate cell growth, differentiation, proliferation and apoptosis in malignant and non-malignant cells [ 153 ]. In a study, Talaat Abd Elaziz et al. (2020) investigated the effect of occupational exposure to heavy metals on colorectal cancer in industrial workers in Egypt and concluded that compared to unexposed tissues, there was a statistically significant increase in the expression of the BRAF and KRAS genes in malignant tissues [ 88 ]. This relationship has a positive correlation with methylated repair enzymes MLH1 and MGMT and exposure to Pb and Al metals [ 88 ]. Some researchers reported that mutations in the KRAS and BRAF genes in patients with colorectal carcinoma play a role in disease progression and response to treatment [ 154 ]. Until now, many epidemiological studies have shown a relationship between workers' exposure to inorganic Pb and lung, kidney, and brain cancers [ 155 , 156 ], but no correlation was found with prostate cancer [ 95 ]. Several factors potentially account for the differences in the studies, including genetics, sex, exposure dose, duration of exposure, bone accumulation over time, and also the number of people examined in each study.

Arsenic is very relevant because groundwater and drinking water supplies can exceed the Maximum Contaminant Level Goal (MCLG) of 10 µg/L (USAEPA) and indicate a health risk [ 31 , 86 , 92 ]. In the USA, around 7% of the wells contain As above the MCLG [ 157 ]. Sarkar et al. (2020) revealed that the mean level of As in the group of patients with colorectal cancer vs healthy people was 2.12 ± 1.04 and 1.43 ± 0.73 ppm, respectively, and this difference was statistically significant [ 87 ]. Also, exposure to this element at a sufficient dose and for a suitable period, by creating a dose–response relationship between As and cancer, leads to the progression of carcinogenesis [ 82 ]. In addition, the evaluation of serum levels of Cd and As with bladder cancer indicates that As levels in patients are 1.48 times those of healthy people [ 32 ]. Chrysochou et al. (2021) reported a 1440-fold difference in serum As concentration in Leukemia patients compared to the control group [ 93 ]. The results of this research were consistent with the study conducted by Chang et al. (2016) [ 80 ].

Furthermore, Chrysochou et al. (2021) showed that there is a strong relationship between As, Cd, Ni, and Pb in patients with chronic leukemia compared to the control group [ 93 ] and the twofold levels of As increase the risk of thyroid cancer 5.35 times [ 86 ]. Arsenic alters the transcription process in gene-related actions, inhibits glutathione, decreases antioxidant defense mechanisms, and increases free radicals [ 158 , 159 ]. Talaat et al. (2011) [ 159 ] showed that Kindlin-2 protein is expressed in the stromal element of transplanted and archival samples of human bladder cancer associated with As. Prostate tissues are a target of As [ 105 , 160 ]. Some studies do not support the relationship between As in prostate cancer and breast cancer [ 89 , 95 ]. There are significant differences in tissue As concentrations. The reason could be the difference in the type of cancer and the accumulation of these metals in different tissues according to the biological characteristics of the involved cells [ 124 ]. Malandrino et al. (2020) [ 161 ] showed that the concentration of many elements such as As in thyroid tissue was higher than in sternothyroid muscle and subcutaneous neck fat, which could be a good explanation for this difference.

Today, there are many natural and human sources for exposure to As, which can increase the risk of various types of cancer in exposed people. Among the most important of these sources, the industries of metal smelting, fossil fuel combustion, coal power plants, industrial wastewater, pesticides, mining, and volcanic activities [ 162 ] are mentioned.

In this review, 23 studies examined the relationship between Ni levels and cancer, and 78% of the studies reported elevated concentrations of this metal associated with several cancers. Concentrations of Ni in serum were different in acute and chronic leukemias vs the control group (22 and 2.2 times, respectively) [ 93 ]. These results are consistent with studies conducted in the breast [ 84 ], thyroid [ 91 ], bladder [ 82 ], lymphoma [ 30 ], prostate [ 96 ], and brain [ 35 ]. Lovaković et al. (2021) also showed that the total blood/serum Ni concentration of patients with testicular germ cell tumors (TGCT) was significantly higher than that of healthy subjects [ 31 ]. Nickel has various carcinogenic mechanisms, including induction of DNA changes, reduction in lymphocyte telomere length, inhibition of intercellular transfer mechanisms, inhibition of nucleotide excision maintenance, oxidative stress and DNA methylation, and endocrine disruptor.

Despite the evidence confirming the carcinogenicity of Ni, some studies have reported results different from those mentioned [ 31 , 85 , 94 ]. The accumulation and effects of Ni are different according to the type of cell/tumor [ 163 ]. Lee et al. (2022) in a recent case–control study in Taiwan showed that urinary Ni levels in patients with lung cancer were 8.72 μg/g CR, in patients with other malignancies were 5.9 μg/g CR, and in healthy subjects 11.63 μg/g CR [ 94 ]. The results of their study were consistent with the research conducted by Tariba Lovaković et al. (2021) [ 31 ]. Tariba Lovaković et al. in the study of the relationship between exposure to low levels of metals and testicular cancer concluded that although the urinary levels of Ni in healthy people are higher than in patients, this concentration was opposite in whole blood and serum samples [ 31 ]. The reason for this difference can be related to the physicochemical characteristics and the absorption and metabolism process of each metal in different target tissues.

In addition, increasing levels of exposure to Ni from volcanic activity, wild forest fires, windblown dust, fossil fuel combustion, commercial and industrial applications such as electroplating, stainless steel, battery manufacturing industries, industrial waste, stainless steel kitchen utensils, dental or orthopedic implants, tobacco, and cheap jewelry can increase the risk of various types of malignancies [ 164 , 165 ].

In this systematic review, 87% of Cr studies associated with cancer reported higher concentrations in cancer patients than in controls [ 93 , 112 ]. The results of examination of the metal imbalance in the blood of patients with thyroid cancer showed that the levels of this element in the case group were 1.28 times those of the control group (757.9 vs 588.8 μg/dL) [ 91 ]. In addition, Afzal et al. (2020) achieved similar results in the study of stomach cancer [ 90 ]. The investigation of the effect of heavy metals on breast cancer progression also reported a 2.6-fold difference between sick and healthy people [ 54 ], which is consistent with the results of the study by Abdel-Gawad et al. (2016) [ 82 ] and Chang et al. (2016) [ 80 ]. Qayyum and Shah (2019) investigated the average Cr contents in blood and hair from lymphoma patients versus healthy controls and found a significant difference, 59.43 µg/dl vs 34.95 µg/dl, respectively [ 30 ].

Chromium levels can increase in the environment through mining, steel and metal alloy industries, paint production, wood and paper processing, burning coal ash, or the use of municipal waste for energy production and second generation fertilizer production [ 166 ]. Chromium is present in contaminated drinking water [ 167 ], and its effects other than oncological, include an endocrine disrupting capacity [ 168 ]. Das et al. (2015) reported mitochondrial-dependent apoptosis in male somatic cells and spermatogonial stem cells by hexavalent Cr(Cr (VI)), a pathway for reproductive abnormalities and infertility. Paternal exposure to heavy metals and solvents increases the risk of their sons developing a testicular germ cell tumor (TGCT) [ 169 ]. However, some previous evidence shows that there is no statistically significant risk between paternal exposure to heavy metals/welding fumes and boys suffering from TGCT, which requires more and more extensive studies to reach a definitive result [ 170 ]. A recent case–control study proved that maternal exposure to Cr increases the chance of Tenosynovial Giant Cell Tumor (TGCT) occurrence in male children [ 171 ]. Krstev et al. (2019) reported in a meta-analysis that occupational exposures to pesticides, Cr (VI), polycyclic aromatic hydrocarbons, diesel fumes, metal fabrication environments, vehicle batteries, flight personnel, jobs causing circadian disruption, firefighters, sewage and petroleum and gasoline workers increase the risk of prostate cancer [ 172 ]. In vitro and in vivo studies show that even exposure to low doses of Cr (VI) can affect the epithelial-mesenchymal pathway (EMT), cause the growth and migration of prostate cancer cells, and follow the progress of tumor and metastasis [ 173 ]. Rafnsson et al. (1997) reported the cancer incidence of an Iceland cohort of 1172 masons exposed to wet concrete aerosols and Cr (VI) exposures associated with cement. These masons had an increase in lung cancer with a standardized incidence ratio (SIR) of 1.69 and 1.77 after 30 years of work [ 174 ].

Mercury (Hg)

In this systematic review, 11 studies investigated the effect of Hg levels on thyroid [ 58 , 86 ], breast [ 68 , 89 ], lung [ 76 , 94 , 109 ], prostate [ 95 ], brain tumor [ 35 ], and other types of cancers [ 105 ]. The results showed that 62.5% of the case–control studies conducted in this field confirmed higher levels of Hg in the biological samples of patients compared to healthy people. Pizent et al. (2022) investigated the role of metal exposure in prostate cancer and concluded that the mean Hg levels in blood/serum of the case group were 2.43 times that of the control group [ 95 ]. In addition, some researchers have reported a twofold difference in the concentration of this metal in the urine of healthy people and those with thyroid cancer, confirming the existence of a direct relationship between exposure to heavy metals and the development of malignancies [ 86 ]. The results of these studies were consistent with the investigations conducted by Binkowski et al. (2015) [ 76 ] and Alatise et al. (2010) [ 68 ].

Cement, iron and steel, gold, and chloro-alkali industries, non-ferrous metal smelting, dental amalgam, forest fires, and volcanoes are among the main sources of Hg release in the environment and exposure of humans to this dangerous element [ 175 ]. Some laboratory studies concluded that Hg causes hypomethylation and hypermethylation of G protein signaling; therefore, it can act as a driving force for tumor growth [ 176 ]. One of the most important mechanisms in pathologies caused by Hg is oxidative stress [ 177 ]. Mercury has a strong ability to deplete intracellular thiols (especially glutathione) and bind to thiol groups on proteins. Although the exact mechanism of ROS production by Hg is not yet known, it seems that this mechanism depends on the physical and chemical form of the element [ 95 ]. For example, the detection of Hg in blood reveals exposure to organic methylmercury (MeHg) associated with eating seafood or inhaling elemental mercury vapor [ 95 ]. Studies have shown that the high affinity of this organic compound for selenohydryl groups, thiols, and selenides can disrupt the structure and function of antioxidant enzymes and proteins [ 178 ].

Although many researchers have proven the role of exposure to Hg in various types of malignancies, some studies have reached contradictory results. The results obtained from the study by Lee et al. (2022) showed that the urinary Hg concentration in patients with lung cancer was measured at 1.57 μg/g CR, while in healthy subjects urinary Hg concentration was equal to 2.3 μg/g CR [ 94 ]. In addition, a case–control study conducted in China showed that urinary Hg levels in the control group (24 μg/L) were 6 times higher than those with breast cancer (4 μg/L) [ 89 ]. The results of these two studies were consistent with the research conducted by Gaman et al. (2019) [ 35 ]. In this study, the concentration of Hg in tissue-blood samples from healthy people was higher than in patients with brain tumors [ 35 ].

The difference in the results of this section can be considered related to the difference in the type of biological sample examined and the duration of Hg detection (according to the Hg half-life). Studies have shown that Hg is distributed and accumulated in most vital organs, mainly in the central nervous system and kidneys [ 179 ]. Blood Hg concentration decreases with a half-life of nearly 50 days [ 180 ]. Researchers reported that 90% of this element is excreted through feces and only 10% through urine [ 181 , 182 ].

• Essential elements

Some of the metals such as Co, Cu, Fe, Mn, Cr (III), Zn, Se, and Mo also have a protective role in the processes of chromosome damage and oxidation [ 38 , 39 ] and are vital for the body in low concentrations. Metals play key roles in antioxidant defense, hemoglobin and energy production in the aerobic respiration process, preserving normal nerve and muscle function, and aiding the immune system [ 183 ]. Some of these essential metals are also an integral part of antioxidant enzymes such as selenoenzymes, glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) and are effective in maintaining sufficient amounts of metallothionein (MT), reducing ROS, and subsequently, oxidative DNA damage [ 184 ]. In addition, they can act as cofactors of DNA repair proteins and as essential components of ribonucleotide reductase in DNA synthesis and help in nucleotide acid metabolism [ 185 ].

The researchers showed that the increase in the concentration of essential elements in the body follows a U-shaped dose–response relationship. Accordingly, at very low doses of essential micronutrients, a high level of complications will occur (known as "deficiency"), which will decrease as the dose increases. In addition, very high doses also have the potential to cause high levels of side effects, which decrease as the dose is reduced. When the deficiency is removed by increasing the dose and no adverse reactions are detected, the organism reaches a state of homeostasis. Generally, Co, Cu, Fe, Mg, Mn, Mo, Se, and Zn are accepted as essential metals, each of which has a unique deficiency point and toxicity. This is happening while there is no deficiency and homeostasis area in the dose–response curve in toxic elements. In this situation, depending on the measured endpoint, the shape of this curve may be an inverted U or J shape. According to studies, if endpoints are related to growth, longevity, fertility, or cognitive function, the response is inverted U-shaped and in the case of disease, it is seen as J-shaped [ 186 ].

The results of the present study showed higher concentrations of these metals in the healthy control group than in the case group, which supports their preventive role in the onset and progression of cancer. Case–control studies indicate an inverse relationship between plasma Zn level and the risk of cancers of the lung [ 34 ], cervix [ 59 ], lung [ 34 , 78 ], prostate [ 74 , 75 ], thyroid [ 91 ], mesothelioma [ 92 ], and lymphoma [ 30 ] cancers. Under physiological conditions, Zn prevents the formation of free radicals, participates in the cellular immunity of T lymphocytes, and prevents tumor growth [ 187 ]. Zinc also plays a role in the antiangiogenic activity of endostatin cell proliferation and intracellular signaling pathways, and the serum level reduction can increase the risk of cancer [ 43 ].

Selenium is an important micronutrient for cancer prevention, which can effectively treat kidney cancer by inhibiting the proteins hypoxia-inducible factor 1- and 2-alpha (HIF 1α and HIF 2α) and the nuclear factor erythroid 2- (Nrf2) [ 188 ]. According to the studies, the presence of permissible concentrations of Se (230–250 ng/mL of blood) reduces the risk of cancer [ 189 ]. The results of the study by Hsueh et al. (2021) showed that higher plasma Se levels significantly reduce the chances of renal cell carcinoma (RCC) [ 33 ], which was consistent with the results of Bock et al. (2018). Chitta et al. (2013) showed that Se can reduce As toxicity by changing cytotoxicity, genotoxicity, and oxidative stress [ 190 ]. It also reduces the Cd oxidative stress by reducing serum malondialdehyde and increasing the activity of superoxide dismutase and glutathione peroxidase [ 191 ]. The mechanism of the Se effect against the reduction of As and Cd toxicity may be through Se-related antioxidant enzymes or the activation of the Nrf2 pathway [ 192 ].

Many studies in the present review showed that the concentration of Mn in the control group was higher than in the case group [ 31 , 34 , 35 , 94 , 112 ]. Manganese is also considered as an essential micronutrient for intracellular activities. Some studies have shown that nanoparticles of compounds of Mn suppress oxidative stress and DNA damage by imitating several enzymes [ 193 ]. It also acts as a cofactor for antioxidants such as Mn superoxide dismutase (MnSOD), which play a role in antioxidant defense [ 194 ]. Sohrabi et al. (2021) reported in a cross-sectional study that the Mn content in cancerous tissues is lower than in adjacent healthy tissues [ 112 ], which supports this mechanism.

Copper is an essential element and an integral constituent of various metalloenzymes. Studies have determined that the normal range of this element is 700–1400 ng/mL of blood [ 189 ]. Marzano et al. (2009) investigated various types of Cu complexes as possible antitumor agents [ 195 ]. Both decreased or increased Cu levels are associated with genetic disorders of Cu metabolism and with high environmental exposure resulting in severe pathologies [ 196 ]. Available reports indicate that the lack of trace elements such as Fe, Zn, and Cu is related to bladder cancer [ 197 , 198 ].

The high concentration of Fe in healthy people compared to patients in some studies suggests a potential effect of Fe in preventing the occurrence or progression of cancer [ 34 , 35 , 36 , 66 , 81 , 90 ]. Fonseca-Nunes et al. (2015) showed that higher serum Fe and ferritin is inversely related to the risk of stomach cancer [ 199 ]. Cook et al. (2012), however, reported that Fe metrics were not associated with neither gastric cardia or non-cardia cancers when taking into account the roles of Helicobacter pylori and gastric atrophy [ 200 ]. Puliyel et al. (2015) discussed Fe toxicity in children and adults undergoing treatments for neoplastic processes and the importance of transferrin saturation (TS), associated with toxic free Fe, and showed that high TS is associated with cancer development and indeed, lowering free Fe decreases the risk [ 201 ]. Cook et al. (2012) also obtained similar results [ 200 ]. Also, the investigations conducted revealed that the level of Fe in cancerous tissues was lower compared to healthy tissues [ 201 ], which could be due to the lower absorption of this metal in cancerous tissue. Fe is a vital micronutrient for oxygen transport and oxidative metabolism [ 202 ]. Deficiency of Fe, for example in Fe-deficient anemia, has a serious impact on physical and cognitive function with a severe reduction in quality of life [ 203 ]. It can also cause side effects such as increased DNA damage, decreased antioxidant defense, decreased enzyme activity, and subsequently increased genomic instability [ 203 ].

Trivalent cobalt (Co (III)) and chromium (Cr (III)) also participate in many biological mechanisms as essential elements. Cobalt constitutes the central atom of cobalamin (vitamin B12), and is also considered an important nutrient to maintain testicular function and normal fertility [ 204 ]. This element impacts DNA synthesis and cell division and participates in purines and pyrimidine production as a cofactor of methionine synthase [ 205 ]. Based on its capacity, Cr can be classified into an essential or toxic group. Trivalent chromium has an effective role in the metabolism of carbohydrates, proteins, and fats. However, Cr (VI) is classified as a toxic metal due to its angiogenic and carcinogenic activities [ 206 , 207 ]. Bibi et al. (2020) investigated the imbalance of metals in the blood of thyroid cancer patients versus healthy individuals [ 91 ]. Concentrations of Pb (774.6 vs. 416.2 μg/dL), Cr (757.9 vs. 588.8 μg/dL), Cd (472.5 vs. 344.6 μg/dL) and Ni (360.5 vs. 200 μg/dL) were higher in cancer patients, while Co was higher in controls (2073 vs. 977 μg/dL).

There is a complex balance among essential elements in the prevention and progression of cancer, and several other factors, such as genetic protector and detrimental mechanisms, and the environment play a key role in the final outcomes [ 42 , 44 , 93 ].

Strengths and limitations

The strength of the present study is that it is the first systematic review that examines two opposite effects of metal exposure on carcinogenesis. This study provides new information on the toxicity of metals, their carcinogenic properties, and, on the other hand, their preventive properties against cancer at different doses. Furthermore, the selected studies were retrieved by a comprehensive systematic search, without restrictions on publication date, study type, or country of investigation. Adopting this approach allowed the highest number of relevant articles to be entered without the loss of scientific data. However, the limitation of this study was the lack of access to the full text of some articles and limiting the database search to studies in the English language only.

Gaps and recommendations

A comprehensive review of published studies shows the existence of some gaps in this important area of ​​health, including the lack of information on some heavy metals. According to the available evidence, most studies published in this field have investigated the concentration of metals in only one type of biological sample, which may lack sufficient accuracy and representation. For example, measuring the levels of some metals in urine samples can reflect short-term exposure. Therefore, depending on the physicochemical characteristics and the unique absorption and metabolism processes of each metal in target tissues, detecting element levels only in one type of biological body sample may not be a suitable measure to reflect internal exposure levels for all types of metals. Therefore, more detailed studies are recommended to cover this important gap.

It is also recommended to design more prospective cohort studies with independent replications to investigate the role of prenatal exposure to toxic metals in the individual's incidence of various types of cancer during the years after birth. Investigating the unique characteristics and activities of vital body organs such as the thyroid, brain, and kidney, which can make them more susceptible to the carcinogenic activity of some toxic metals, seems necessary.

Considering the slow process of removing toxic metals from the body, it seems that using methods to accelerate this process can play an effective role in reducing the accumulation of these elements in the target organs and preventing adverse health effects. The surveys conducted indicate that few clinical trial studies have been conducted in this field [ 208 ]. Therefore, it is recommended to design and implement more clinical studies to achieve an effective method.

The presented review indicated that chronic exposure to metals, even in low concentrations, can be a potential risk factor for various types of cancer. The main elements found in the biological samples of the patients included Pb, Cr (VI), As, Cd, and Ni and these elements were identified in many studies. Generally, the levels of these metals in clinical cases were higher than in controls, which demonstrates the carcinogenicity of these metals. In addition, the investigation of the effect of essential metals such as Se, Zn, Fe, and Mn on the occurrence of adverse health effects revealed that low concentrations of these elements indicated anticancer properties and that their high concentrations could be the cause of biological toxicity. Therefore, according to the biological accumulation property of heavy metals in the vital organs of the body, regular monitoring of toxic metal concentrations in biological and clinical samples is recommended to identify possible sources of their exposure. In addition, authorities should adopt stricter laws to control and reduce metal emissions into the environment and to protect workers. This will significantly reduce the risk from dermal, inhalational, and ingestion exposure pathways from these metals and will play an effective role in reducing the cancer occurrence.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Department of Occupational Health Engineering, School of Health, Kashan University of Medical Sciences, Kashan, Iran

Amir Hossein Khoshakhlagh

Department of Health in Emergencies and Disasters, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Mahdiyeh Mohammadzadeh

Climate Change and Health Research Center (CCHRC), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran

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Khoshakhlagh, A.H., Mohammadzadeh, M. & Gruszecka-Kosowska, A. The preventive and carcinogenic effect of metals on cancer: a systematic review. BMC Public Health 24 , 2079 (2024). https://doi.org/10.1186/s12889-024-19585-5

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Further evidence for null association of phenol sulfotransferase SULT1A1 polymorphism with prostate cancer risk: a case-control study of familial prostate cancer in a Japanese population

Affiliation.

• 1 Maebashi, 3718511, Japan. [email protected]
• PMID: 18368507
• DOI: 10.1007/s11255-008-9364-5

Sulfation is a key pathway in xenobiotic metabolism and chemical defense, and phenol sulfotransferase SULT1A1 plays a central role in this reaction. Genetic polymorphism of the SULT1A1 gene, SULT1A1, was reported to be associated with risks of several cancers; however, one study showed no significant relation between SULT1A1 genotype with prostate cancer risk. The present study was conducted to confirm the association of a G638A polymorphism, Arg213His, in SULT1A1 with familial prostate cancer risk in a Japanese population. A case-control study consisting of 126 cases and 119 controls was performed. In controls, GG, GA, and AA genotypes were observed in 85 (71.4%), 32 (26.9%), and 2 (1.7%), respectively; whereas, GG, GA, and AA genotypes were observed in 94 (74.6%), 32 (25.4%), and 0 cases, respectively. No significant differences were found in genotypic frequencies among cases and controls. Furthermore, stratification of cases according to clinical stages (localized or metastatic), pathological grades (Gleason score <7, or >7), age at diagnosis (<70 years or >70) and the number of affected relatives (2 or >2) did not show any significant differences among categories. These findings suggested that genetic polymorphism of SULT1A1 might not be involved in genetic susceptibility to prostate cancer.

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