Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculator for Initial Prostate Biopsy by Incorporating the 2014 International Society of Urological Pathology Gleason Grading and Cribriform growth

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Combination of prostate volume and apparent diffusion coefficient can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies

BACKGROUND

Nowadays, there are many patients who undergo unnecessary prostate biopsies after receiving a prostate imaging reporting and data system (PI-RADS) score of 3. Our purpose is to identify cutoff values of the prostate volume (PV) and minimum apparent diffusion coefficient (ADCmin) to stratify those patients to reduce unnecessary prostate biopsies.

METHODS

Data from 224 qualified patients who received prostate biopsies from January 2019 to June 2023 were collected. The Mann-Whitney U test was used to compare non-normal distributed continuous variables, which were recorded as median (interquartile ranges). The correlation coefficients were calculated using Spearman's rank correlation analysis. Categorical variables are recorded by numbers (percentages) and compared by χ2 test. Both univariate and multivariate logistic regression analysis were used to determine the independent predictors. The receiver-operating characteristic curve and the area under the curve (AUC) were used to evaluate the diagnostic performance of clinical variables.

RESULTS

Out of a total of 224 patients, 36 patients (16.07%) were diagnosed with clinically significant prostate cancer (csPCa), whereas 72 patients (32.14%) were diagnosed with any grade prostate cancer. The result of multivariate analysis demonstrated that the PV (p < 0.001, odds ratio [OR]: 0.952, 95% confidence interval [95% CI]: 0.927-0.978) and ADCmin (p < 0.01, OR: 0.993, 95% CI: 0.989-0.998) were the independent factors for predicting csPCa. The AUC values of the PV and ADCmin were 0.779 (95% CI: 0.718-0.831) and 0.799 (95% CI: 0.740-0.849), respectively, for diagnosing csPCa. After stratifying patients by PV and ADCmin, 24 patients (47.06%) with "PV < 55 mL and ADCmin < 685 μm2/s" were diagnosed with csPCa. However, only one patient (1.25%) with PV ≥ 55 mL and ADCmin ≥ 685 μm2/s were diagnosed with csPCa.

CONCLUSIONS

In this study, we found the combination of PV and ADCmin can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies. These patients with "PV ≥ 55 mL and ADCmin ≥ 685 μm2/s" may safely avoid prostate biopsies.

Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3

BACKGROUND

For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required.

PURPOSE

To develop and validate machine learning (ML) models based on clinical and imaging parameters for csPCa detection in patients with PI-RADS v2.1 ≥ 3.

STUDY TYPE

Retrospective.

SUBJECTS

One thousand eighty-three patients with PI-RADS v2.1 ≥ 3, randomly split into training (70%, N = 759) and validation (30%, N = 324) datasets, and 147 patients enrolled prospectively for testing.

FIELD STRENGTH/SEQUENCE

3.0 T scanners/T2-weighted fast spin echo sequence and DWI with diffusion-weighted single-shot gradient echo planar imaging sequence.

ASSESSMENT

The factors evaluated for csPCa detection were age, prostate specific antigen, prostate volume, and the diameter and location of the index lesion, PI-RADSv2.1. Five ML models for csPCa detection were developed: logistic regression (LR), extreme gradient boosting, random forest (RF), decision tree, and support vector machines. The csPCa was defined as Gleason grade ≥2.

STATISTICAL TESTS

Univariable and multivariable LR analyses to identify parameters associated with csPCa. Area under the receiver operating characteristic curve (AUC), Brier score, and DeLong test were used to assess and compare the csPCa diagnostic performance with the LR model. The significance level was defined as 0.05.

RESULTS

The RF model exhibited the highest AUC (0.880-0.904) and lowest Brier score (0.125-0.133) among the ML models in the validation and testing cohorts, however, there was no difference when compared to the LR model (P = 0.453 and 0.548). The sensitivity and negative predictive values in the validation and testing cohorts were 93.8%-97.6% and 82.7%-95.1%, respectively, at a threshold of 0.450 (99% sensitivity of the RF model).

DATA CONCLUSION

The RF model might help for assessing the risk of csPCa and preventing overdiagnosis and unnecessary biopsy for men with PI-RADSv2.1 ≥ 3.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Association of Pathological Features and Multiparametric MRI-Based Radiomics With TP53-Mutated Prostate Cancer

BACKGROUND

TP53 mutations are associated with prostate cancer (PCa) prognosis and therapy.

PURPOSE

To develop TP53 mutation classification models for PCa using MRI radiomics and clinicopathological features.

STUDY TYPE

Retrospective.

POPULATION

388 patients with PCa from two centers (Center 1: 281 patients; Center 2: 107 patients). Cases from Center 1 were randomly divided into training and internal validation sets (7:3). Cases from Center 2 were used for external validation.

FIELD STRENGTH/SEQUENCE

3.0T/T2-weighted imaging, dynamic contrast-enhanced imaging, diffusion-weighted imaging.

ASSESSMENT

Each patient's index tumor lesion was manually delineated on the above MRI images. Five clinicopathological and 428 radiomics features were obtained from each lesion. Radiomics features were selected by least absolute shrinkage and selection operator and binary logistic regression (LR) analysis, while clinicopathological features were selected using Mann-Whitney U test. Radiomics models were constructed using LR, support vector machine (SVM), and random forest (RF) classifiers. Clinicopathological-radiomics combined models were constructed using the selected radiomics and clinicopathological features with the aforementioned classifiers.

STATISTICAL TESTS

Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC). P value <0.05 indicates statistically significant.

RESULTS

In the internal validation set, the radiomics model had an AUC of 0.74 with the RF classifier, which was significantly higher than LR (AUC = 0.61), but similar to SVM (AUC = 0.69; P = 0.422). For the combined model, the AUC of RF model was 0.84, which was significantly higher than LR (0.64), but similar to SVM (0.80; P = 0.548). Both the combined RF and combined SVM models showed significantly higher AUCs than the radiomics models. In the external validation set, the combined RF and combined SVM models showed AUCs of 0.83 and 0.82.

DATA CONCLUSION

Pathological-radiomics combined models with RF, SVM show the association of TP53 mutations and pathological-radiomics features of PCa.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer

BACKGROUND

Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease.

OBJECTIVE

To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management.

DESIGN, SETTING, AND PARTICIPANTS

Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models.

RESULTS AND LIMITATIONS

The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study.

CONCLUSIONS

The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP.

PATIENT SUMMARY

We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

Cancer detection and complications of transperineal prostate biopsy with antibiotics when indicated

OBJECTIVES

To describe the prostate cancer (PCa) detection rate, including clinically significant prostate cancer (csPCa), in a large cohort of patients who underwent transperineal ultrasonography-guided systematic prostate biopsy (TPB-US) using a probe-mounted transperineal access system, with magnetic resonance imaging (MRI) cognitive fusion in case of a Prostate Imaging-Reporting and Data System grade 3-5 lesion, under local anaesthesia in an outpatient setting. Additionally, to compare the incidence of procedure-related complications with a cohort of patients undergoing transrectal ultrasonography-guided (TRB-US) and transrectal MRI-guided biopsies (TRB-MRI).

PATIENTS AND METHODS

This was an observational cohort study in men who underwent TPB-US prostate biopsy in a large teaching hospital. For each participant, prostate-specific antigen level, clinical tumour stage, prostate volume, MRI parameters, number of (targeted) prostate biopsies, biopsy International Society of Uropathology (ISUP) grade and procedure-related complications were assessed. csPCa was defined as ISUP grade ≥2. Antibiotic prophylaxis was only given in those with an increased risk of urinary tract infection.

RESULTS

A total of 1288 TPB-US procedures were evaluated. The overall detection rate for PCa in biopsy-naive patients was 73%, and for csPCa it was 63%. The incidence of hospitalization was 1% in TPB-US (13/1288), compared to 4% in TRB-US (8/214) and 3% in TRB-MRI (7/219; P = 0.002).

CONCLUSIONS

Contemporary combined systematic and target TPB-US with MRI cognitive fusion is easy to perform in an outpatient setting, with a high detection rate of csPCa and a low incidence of procedure-related complications.

Outcomes of a Diagnostic Pathway for Prostate Cancer Based on Biparametric MRI and MRI-Targeted Biopsy Only in a Large Teaching Hospital

BACKGROUND

Diagnostic pathways for prostate cancer (PCa) balance detection rates and burden. MRI impacts biopsy indication and strategy.

METHODS

A prospectively collected cohort database (N = 496) of men referred for elevated PSA and/or abnormal DRE was analyzed. All underwent biparametric MRI (3 Tesla scanner) and ERSPC prostate risk-calculator. Indication for biopsy was PIRADS ≥ 3 or risk-calculator ≥ 20%. Both targeted (cognitive-fusion) and systematic cores were combined. A hypothetical full-MRI-based pathway was retrospectively studied, omitting systematic biopsies in: (1) PIRADS 1-2 but risk-calculator ≥ 20%, (2) PIRADS ≥ 3, receiving targeted biopsy-cores only.

RESULTS

Significant PCa (GG ≥ 2) was detected in 120 (24%) men. Omission of systematic cores in cases with PIRADS 1-2 but risk-calculator ≥ 20%, would result in 34% less biopsy indication, not-detecting 7% significant tumors. Omission of systematic cores in PIRADS ≥ 3, only performing targeted biopsies, would result in a decrease of 75% cores per procedure, not detecting 9% significant tumors. Diagnosis of insignificant PCa dropped by 52%. PCa undetected by targeted cores only, were ipsilateral to MRI-index lesions in 67%.

CONCLUSIONS

A biparametric MRI-guided PCa diagnostic pathway would have missed one out of six cases with significant PCa, but would have considerably reduced the number of biopsy procedures, cores, and insignificant PCa. Further refinement or follow-up may identify initially undetected cases. Center-specific data on the performance of the diagnostic pathway is required.

Standardized prostate cancer incidence and mortality rates following initial non-malignant biopsy result

OBJECTIVES

To compare the incidence of subsequent prostate cancer diagnosis and death following an initial non-malignant systematic transrectal ultrasonography (TRUS) biopsy with that in an age- and calendar-year matched population over a 20-year period.

SUBJECTS AND METHODS

This population-based analysis compared a cohort of all men with initial non-malignant TRUS biopsy in Denmark between 1995 and 2016 (N = 37 231) with the Danish population matched by age and calendar year, obtained from the NORDCAN 9.1 database. Age- and calendar year-corrected standardized prostate cancer incidence (SIR) and prostate cancer-specific mortality ratios (SMRs) were calculated and heterogeneity among age groups was assessed with the Cochran's Q test.

RESULTS

The median time to censoring was 11 years, and 4434 men were followed for more than 15 years. The corrected SIR was 5.2 (95% confidence interval [CI] 5.1-5.4) and the corrected SMR was 0.74 (95% CI 0.67-0.81). Estimates differed among age groups (P < 0.001 for both), with a higher SIR and SMR among younger men.

CONCLUSION

Men with non-malignant TRUS biopsy have a much higher incidence of prostate cancer but a risk of prostate cancer death below the population average. This underlines that the oncological risk of cancers missed in the initial TRUS biopsy is low. Accordingly, attempts to increase the sensitivity of initial biopsy are unjustified. Moreover, current follow-up after non-malignant biopsy is likely to be overaggressive, particularly in men over the age of 60 years.

Beyond blood biomarkers: the role of SelectMDX in clinically significant prostate cancer identification

INTRODUCTION

New potential biomarkers to pre-intervention identification of a clinically significant prostate cancer (csPCa) will prevent overdiagnosis and overtreatment and limit quality of life impairment of PCa patients.

AREAS COVERED

We have developed a comprehensive review focusing our research on the increasing knowledge of the role of SelectMDX® in csPCa detection. Areas identified as clinically relevant are the ability of SelectMDX® to predict csPCa in active surveillance setting, its predictive ability when combined with multiparametric MRI and the role of SelectMDX® in the landscape of urinary biomarkers.

EXPERT OPINION

Several PCa biomarkers have been developed either alone or in combination with clinical variables to improve csPCa detection. SelectMDX® score includes genomic markers, age, PSA, prostate volume, and digital rectal examination. Several studies have shown consistency in the ability to improve detection of csPCa, avoidance of unnecessary prostate biopsies, helpful in decision-making for clinical benefit of PCa patients with future well designed, and impactful studies.

[Common errors, pitfalls, and management of complications of prostate biopsy : The most common diagnostic and procedural challenges of transrectal fusion prostate biopsy in the initial diagnosis of clinically significant prostate cancer]

BACKGROUND

Transrectal (TR) prostate biopsy is the gold standard in diagnosis of prostate cancer (PC). It requires a precise and safe technique for sample acquisition.

OBJECTIVE

Several approaches will be discussed to avoid overdiagnosis, false-negative results, and complications of the procedure.

MATERIALS AND METHODS

We analyzed national and European guidelines, systematic reviews, meta-analyses, as well as prospective and retrospective studies to describe current trends in indication and performance of biopsies.

RESULTS

Incorporation of risk calculators and magnetic resonance imaging (MRI) into daily routine reduces biopsy rates and results in a more precise diagnosis of clinically significant prostate cancer (csPC). Combination of random- and MRI-fusion guided biopsy-but also extending the radius of sampling by 10 mm beyond the MRI lesion and a transperineal (TP) sampling approach - lead to a higher tumor-detection rate. Bleeding is the most common complication after prostate biopsy and is usually self-limiting. Postbiopsy infection rates can be reduced through TP biopsy.

CONCLUSION

TR MRI-fusion guided biopsy is a widely acknowledged tool in primary diagnostics of csPC. Higher detection rates and safety can be achieved through a TP sampling approach.

Systematic review of methods used to improve the efficacy of magnetic resonance in early detection of clinically significant prostate cancer

BACKGROUND AND OBJECTIVE

Prostate cancer (PC) is the malignant neoplasm with the highest incidence after lung cancer worldwide. The objective of this study is to review the literature on the methods that improve the efficacy of the current strategy for the early diagnosis of clinically significant PC (csPC), based on the performance of magnetic resonance imaging (RM) and targeted biopsies when suspicious lesions are detected, in addition to systematic biopsy.

EVIDENCE ACQUISITION

A systematic literature review was performed in PubMed, Web of Science and Cochrane according to the PRISMA criteria (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), using the search terms: multiparametric magnetic resonance imaging, biparametric magnetic resonance imaging, biomarkers in prostate cancer, prostate cancer y early diagnosis. A total of 297 references were identified and, using the PICO selection criteria, 21 publications were finally selected to synthesize the evidence.

EVIDENCE SYNTHESIS

With the consolidation of MRI as the test of choice for the diagnosis of prostate cancer, the role of PSA density (PSAD) becomes relevant as a predictive tool included in prediction nomograms, without added cost. PSAD and diagnostic markers, combined with MRI, offer a high diagnostic power with an area under curve (AUC) above 0.7. Only the SHTLM3 model integrates markers in the creation of a nomogram. Prediction models also offer consistent efficacy with an AUC greater than 0.8 when associating MRI.

CONCLUSIONS

The efficacy of MRI in clinically significant prostate cancer detection can be improved with different parameters in order to generate predictive models that support decision making.

Diagnostic Accuracy of Predictive Models in Prostate Cancer: A Systematic Review and Meta-Analysis

Aim

Accurate diagnosis of prostate cancer (PCa) has a fundamental role in clinical and patient care. Recent advances in diagnostic testing and marker lead to standardized interpretation and increased prescription by clinicians to improve the detection of clinically significant PCa and select patients who strictly require targeted biopsies.

Methods

In this study, we present a systematic review of the overall diagnostic accuracy of each testing panel regarding the panel details. In this meta-analysis, using a structured search, Web of Science and PubMed databases were searched up to 23 September 2019 with no restrictions and filters. The study's outcome was the AUC and 95% confidence interval of prediction models. This index was reported as an overall and based on the WHO region and models with/without MRI.

Results

The thirteen final articles included 25,691 people. The overall AUC and 95% CI in thirteen studies were 0.78 and 95% CI: 0.73–0.82. The weighted average AUC in the countries of the Americas region was 0.73 (95% CI: 0.70–0.75), and in European countries, it was 0.80 (95% CI: 0.72–0.88). In four studies with MRI, the average weighted AUC was 0.88 (95% CI: 0.86–0.90), while in other articles where MRI was not a parameter in the diagnostic model, the mean AUC was 0.73 (95% CI: 0.70–0.76).

Conclusions

The present study's findings showed that MRI significantly improved the detection accuracy of prostate cancer and had the highest discrimination to distinguish candidates for biopsy.

Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials

BACKGROUND

A model was built that characterized effects of individual factors on five-year prostate cancer (PCa) risk in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial (PLCO) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT). This model was validated in a third San Antonio Biomarkers of Risk (SABOR) screening cohort.

METHODS

A prediction model for 1- to 5-year risk of developing PCa and Gleason > 7 PCa (HG PCa) was built on PLCO and SELECT using the Cox proportional hazards model adjusting for patient baseline characteristics. Random forests and neural networks were compared to Cox proportional hazard survival models, using the trial datasets for model building and the SABOR cohort for model evaluation. The most accurate prediction model is included in an online calculator.

RESULTS

The respective rates of PCa were 8.9%, 7.2%, and 11.1% in PLCO (n = 31,495), SELECT (n = 35,507), and SABOR (n = 1790) over median follow-up of 11.7, 8.1 and 9.0 years. The Cox model showed higher prostate-specific antigen (PSA), BMI and age, and African American race to be associated with PCa and HGPCa. Five-year risk predictions from the combined SELECT and PLCO model effectively discriminated risk in the SABOR cohort with C-index 0.76 (95% CI [0.72, 0.79]) for PCa, and 0.74 (95% CI [0.65,0.83]) for HGPCa.

CONCLUSIONS

A 1- to 5-year PCa risk prediction model developed from PLCO and SELECT was validated with SABOR and implemented online. This model can individualize and inform shared screening decisions.

Updating the Rotterdam Prostate Cancer Risk Calculator with Invasive Cribriform and/or Intraductal Carcinoma for Men with a Prior Negative Biopsy

The Rotterdam Prostate Cancer Risk Calculator (RPCRC) is a well-validated tool for upfront risk stratification to reduce the number of prostate biopsies and magnetic resonance imaging scans among both biopsy-naïve and previously biopsied men. The presence of invasive cribriform and/or intraductal carcinoma (CR/IDC) identifies men with aggressive grade group (GG) 2 tumors. This finding was recently incorporated in the RPCRC for biopsy-naïve men to predict the probability of no PCa, indolent PCa (GG 1 disease and GG 2 disease without CR/IDC), and clinically significant PCa (csPCa: GG 2 disease with CR/IDC and higher). The aim of the current study was to update the RPCRC for men with a previous negative biopsy with the presence of CR/IDC. A total of 2215 men were eligible for analyses, of whom 1776 (80%) were not diagnosed with PCa, 358 (16%) were diagnosed with indolent PCa, and 81 (4%) were diagnosed with csPCa according to the original 2014 Gleason grading. The optimism-corrected area under the curve was 0.69 for any PCa and 0.77 for csPCa. With a threshold of 10% for indolent PCa or 1% for csPCa, 20% of all prostate biopsies could be avoided and 2% of all csPCa cases would be missed. Our results support upfront risk stratification with the updated RPCRC.

Patient summary

Risk stratification for men without a prior diagnosis of prostate cancer can reduce the number of prostate biopsies and magnetic resonance imaging scans carried out in this patient population. Our study shows that it is possible to update the Rotterdam Prostate Cancer Risk Calculator for men with a previous negative biopsy with the presence of invasive cribriform and/or intraductal carcinoma.

An analysis of three different prostate cancer risk calculators applied prior to prostate biopsy: A Turkish cohort validation study

The study aimed to investigate the best-performing of three risk calculators (RCs) for the Turkish population in predicting cancer-free status and high-risk prostate cancer (PCa) in patients undergoing transrectal ultrasound-guided prostate biopsy. The electronic medical records of 527 patients who underwent prostate biopsy for the first time due to PSA of 0.3-50 ng/dl and/or cancer suspicion at digital rectal examination (DRE) between January 2017 and December 2020 were retrieved retrospectively. The predictive power of the RCs in the biopsy and the surgical cohort was calculated by two urologists using European Randomised Study of Screening for Prostate Cancer (ERSPC) RC, the North American Prostate Cancer Prevention Trial-RC (PCPT-RC), and the Prostate Biopsy Collaborative Group (PBCG)-RC. All three RCs were successful in predicting PCa and high-risk disease at ROC analysis (p < 0.0001). Of these three nomograms, PBCG-RC outperformed PCPT-RC 2.0 and ERSPC-RH in predicting benign pathology outcomes at biopsy. A better performance of PBCG-RC was also observed in terms of prediction of high-risk disease at biopsy. Using any of the available RCs prior to biopsy is of greater assistance to prostate-specific antigen and DRE than examination alone. The study results show that PBCG-RC performed before biopsy has a higher predictive power than the other two RCs.

Changing the Prostate Cancer Detection Paradigm: Clinical Application of European Association of Urology Guideline-recommended Magnetic Resonance Imaging-based Risk Stratification in Men with Suspected Prostate Cancer

BACKGROUND

Multiparametric magnetic resonance imaging using the Prostate Imaging Reporting and Data System version 2.1 allows for a personalized, risk-stratified approach to indicating prostate biopsies (PBx) in order to reduce PBx and concomitant complications in men with suspected prostate cancer (PCa). One way to achieve this goal is to implement the risk-stratified pathway (RSP) using the Rotterdam Prostate Cancer Risk Calculator.

OBJECTIVE

To describe the clinical implementation of the RSP and to examine its impact on the number of PBx and the resulting changes in the PCa detection pattern compared with men undergoing PBx in a detection-focused pathway (DFP) without prior risk assessment.

DESIGN, SETTING, AND PARTICIPANTS

An institutional dataset of 505 consecutive patients with suspected PCa between July 2019 and February 2020 was used.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Chi-square test and Mann-Whitney U test were employed to examine differences in the number of PBx and the PCa detection pattern between the DFP (n = 195, 38.6%) and the RSP (n = 310, 61.4%). To minimize differences in risk stratification, inverse probability of treatment weighting was used.

RESULTS AND LIMITATIONS

After implementing the RSP, the overall biopsy rate could be reduced by 11.2% (100% vs 88.8%, p < 0.001. Additionally, compared with the DFP, the number of biopsy cores per patient was reduced in the RSP (14 [interquartile range {IQR} 14-15] vs 14 [IQR 6-14], p < 0.001) and the detection of clinically significant PCa was increased (44.3% vs 57.7%, p = 0.038). Overdiagnosis of clinically insignificant disease was decreased in the RSP (22.8% vs 12.6%, p = 0.039).

CONCLUSIONS

Implementation of the RSP in clinical practice reduced the number of PBx and brought forth a shift in the PCa detection pattern toward clinically significant disease, while reducing overdiagnosis of clinically insignificant disease.

PATIENT SUMMARY

In this study, we examined the impact of risk stratification on the number of prostate biopsies (PBx) and the consecutive detection pattern in men with suspected prostate cancer (PCa). We found that the risk-stratified pathway reduced the number of PBx while simultaneously shifting the PCa detection pattern toward clinically significant PCa.

Prostate cancer screening using a combination of risk-prediction, MRI, and targeted prostate biopsies (STHLM3-MRI): a prospective, population-based, randomised, open-label, non-inferiority trial

BACKGROUND

Screening for prostate cancer using prostate-specific antigen (PSA) reduces prostate cancer mortality but can lead to adverse outcomes. We aimed to compare a traditional screening approach with a diagnostic strategy of blood-based risk prediction combined with MRI-targeted biopsies.

METHODS

We did a prospective, population-based, randomised, open-label, non-inferiority trial (STHLM3-MRI) in Stockholm county, Sweden. Men aged 50-74 years were randomly selected by Statistics Sweden and invited by mail to participate in screening; those with an elevated risk of prostate cancer, defined as either a PSA of 3 ng/mL or higher or a Stockholm3 score of 0·11 or higher were eligible for randomisation. Men with a previous prostate cancer diagnosis, who had undergone a prostate biopsy within 60 days before the invitation to participate, with a contraindication for MRI, or with severe illness were excluded. Eligible participants were randomly assigned (2:3) using computer-generated blocks of five, stratified by clinically significant prostate cancer risk, to receive either systematic prostate biopsies (standard group) or biparametric MRI followed by MRI-targeted and systematic biopsy in MRI-positive participants (experimental group). The primary outcome was the detection of clinically significant prostate cancer at prostate biopsy, defined as a Gleason score of 3 + 4 or higher. We used a margin of 0·78 to assess non-inferiority for the primary outcome. Key secondary outcome measures included the proportion of men with clinically insignificant prostate cancer (defined as a Gleason score of 3 + 3), and the number of any prostate MRI and biopsy procedures done. We did two comparisons: Stockholm3 (using scores of 0·11 and 0·15 as cutoffs) versus PSA in the experimental group (paired analyses) and PSA plus standard biopsy versus Stockholm3 plus MRI-targeted and systematic biopsy (unpaired, randomised analyses). All analyses were intention to treat. This study is registered with ClinicalTrials.gov, NCT03377881.

FINDINGS

Between Feb 5, 2018, and March 4, 2020, 49 118 men were invited to participate, of whom 12 750 were enrolled and provided blood specimens, and 2293 with elevated risk were randomly assigned to the experimental group (n=1372) or the standard group (n=921). The area under the receiver-operating characteristic curve for detection of clinically significant prostate cancer was 0·76 (95% CI 0·72-0·80) for Stockholm3 and 0·60 (0·54-0·65) for PSA. In the experimental group, a Stockholm3 of 0·11 or higher was non-inferior to a PSA of 3 ng/mL or higher for detection of clinically significant prostate cancer (227 vs 192; relative proportion [RP] 1·18 [95% CI 1·09-1·28], p<0·0001 for non-inferiority), and also detected a similar number of low-grade prostate cancers (50 vs 41; 1·22 [0·96-1·55], p=0·053 for superiority) and was associated with more MRIs and biopsies. Compared with PSA of 3 ng/mL or higher, a Stockholm3 of 0·15 or higher provided identical sensitivity to detect clinically significant cancer, and led to fewer MRI procedures (545 vs 846; 0·64 [0·55-0·82]) and fewer biopsy procedures (311 vs 338; 0·92 (0·86-1·03). Compared with screening using PSA and systematic biopsies, a Stockholm3 of 0·11 or higher combined with MRI-targeted and systematic biopsies was associated with higher detection of clinically significant cancers (227 [3·0%] men tested vs 106 [2·1%] men tested; RP 1·44 [95% CI 1·15-1·81]), lower detection of low-grade cancers (50 [0·7%] vs 73 [1·4%]; 0·46 [0·32-0·66]), and led to fewer biopsy procedures. Patients randomly assigned to the experimental group had a lower incidence of prescription of antibiotics for infection (25 [1·8%] of 1372 vs 41 [4·4%] of 921; p=0·0002) and a lower incidence of admission to hospital (16 [1·2%] vs 31 [3·4%]; p=0·0003) than those in the standard group.

INTERPRETATION

The Stockholm3 test can inform risk stratification before MRI and targeted biopsies in prostate cancer screening. Combining the Stockholm3 test with an MRI-targeted biopsy approach for prostate cancer screening decreases overdetection while maintaining the ability to detect clinically significant cancer.

FUNDING

The Swedish Cancer Society, the Swedish Research Council, and Stockholm City Council.

Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy

Background

Machine learning has many attractive theoretic properties, specifically, the ability to handle non predefined relations. Additionally, studies have validated the clinical utility of mpMRI for the detection and localization of CSPCa (Gleason score ≥ 3 + 4). In this study, we sought to develop and compare machine-learning models incorporating mpMRI parameters with traditional logistic regression analysis for prediction of PCa (Gleason score ≥ 3 + 3) and CSPCa on initial biopsy.

Methods

A total of 688 patients with no prior prostate cancer diagnosis and tPSA ≤ 50 ng/ml, who underwent mpMRI and prostate biopsy were included between 2016 and 2020. We used four supervised machine-learning algorithms in a hypothesis-free manner to build models to predict PCa and CSPCa. The machine-learning models were compared to the logistic regression analysis using AUC, calibration plot, and decision curve analysis.

Results

The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) yielded similar diagnostic accuracy with logistic regression, while classification and regression tree (CART, AUC = 0.834 and 0.867) had significantly lower diagnostic accuracy than logistic regression (AUC = 0.894 and 0.917) in prediction of PCa and CSPCa (all P < 0.05). However, the CART illustrated best calibration for PCa (SSR = 0.027) and CSPCa (SSR = 0.033). The ANN, SVM, RF, and LR for PCa had higher net benefit than CART across the threshold probabilities above 5%, and the five models for CSPCa displayed similar net benefit across the threshold probabilities below 40%. The RF (53% and 57%, respectively) and SVM (52% and 55%, respectively) for PCa and CSPCa spared more unnecessary biopsies than logistic regression (35% and 47%, respectively) at 95% sensitivity for detection of CSPCa.

Conclusion

Machine-learning models (SVM and RF) yielded similar diagnostic accuracy and net benefit, while spared more biopsies at 95% sensitivity for detection of CSPCa, compared with logistic regression. However, no method achieved desired performance. All methods should continue to be explored and used in complementary ways.

Clinical use of the SelectMDx urinary-biomarker test with or without mpMRI in prostate cancer diagnosis: a prospective, multicenter study in biopsy-naïve men

Background

Risk stratification in men with suspicion of prostate cancer (PCa) requires reliable diagnostic tests, not only to identify high-grade PCa, also to minimize the overdetection of low-grade PCa, and reduction of “unnecessary” prostate MRIs and biopsies. This study aimed to evaluate the SelectMDx test to detect high-grade PCa in biopsy-naïve men. Subsequently, to assess combinations of SelectMDx test and multi-parametric (mp) MRI and its potential impact on patient selection for prostate biopsy.

Methods

This prospective multicenter diagnostic study included 599 biopsy-naïve patients with prostate-specific antigen level ≥3 ng/ml. All patients underwent a SelectMDx test and mpMRI before systematic transrectal ultrasound-guided biopsy (TRUSGB). Patients with a suspicious mpMRI also had an in-bore MR-guided biopsy (MRGB). Histopathologic outcome of TRUSGB and MRGB was used as reference standard. High-grade PCa was defined as ISUP Grade Group (GG) ≥ 2. The primary outcome was the detection rates of low- and high-grade PCa and number of biopsies avoided in four strategies, i.e., (1) SelectMDx test-only, (2) mpMRI-only, (3) SelectMDx test followed by mpMRI when SelectMDx test was positive (conditional strategy), and (4) SelectMDx test and mpMRI in all (joint strategy). A positive SelectMDx test outcome was a risk score of ≥−2.8. Decision curve analysis (DCA) was performed to assess clinical utility.

Results

Prevalence of high-grade PCa was 31% (183/599). Thirty-eight percent (227/599) of patients had negative SelectMDx test in whom biopsy could be avoided. Low-grade PCa was not detected in 35% (48/138) with missing 10% (18/183) high-grade PCa. Yet, mpMRI-only could avoid 49% of biopsies, not detecting 4.9% (9/183) of high-grade PCa. The conditional strategy reduces the number of mpMRIs by 38% (227/599), avoiding biopsy in 60% (357/599) and missing 13% (24/183) high-grade PCa. Low-grade PCa was not detected in 58% (80/138). DCA showed the highest net benefit for the mpMRI-only strategy, followed by the conditional strategy at-risk thresholds >10%.

Conclusions

SelectMDx test as a risk stratification tool for biopsy-naïve men avoids unnecessary biopsies in 38%, minimizes low-grade PCa detection, and misses only 10% high-grade PCa. Yet, using mpMRI in all patients had the highest net benefit, avoiding biopsy in 49% and missing 4.9% of high-risk PCa. However, if mpMRI availability is limited or expensive, using mpMRI-only in SelectMDx test positive patients is a good alternative strategy.

The cost-effectiveness of prostate cancer screening using the Stockholm3 test

Objectives

The European Randomized Study of Screening for Prostate Cancer found that prostate-specific antigen (PSA) screening reduced prostate cancer mortality, however the costs and harms from screening may outweigh any mortality reduction. Compared with screening using the PSA test alone, using the Stockholm3 Model (S3M) as a reflex test for PSA ≥ 1 ng/mL has the same sensitivity for Gleason score ≥ 7 cancers while the relative positive fractions for Gleason score 6 cancers and no cancer were 0.83 and 0.56, respectively. The cost-effectiveness of the S3M test has not previously been assessed.

Methods

We undertook a cost-effectiveness analysis from a lifetime societal perspective. Using a microsimulation model, we simulated for: (i) no prostate cancer screening; (ii) screening using the PSA test; and (iii) screening using the S3M test as a reflex test for PSA values ≥ 1, 1.5 and 2 ng/mL. Screening strategies included quadrennial re-testing for ages 55–69 years performed by a general practitioner. Discounted costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated.

Results

Comparing S3M with a reflex threshold of 2 ng/mL with screening using the PSA test, S3M had increased effectiveness, reduced lifetime biopsies by 30%, and increased societal costs by 0.4%. Relative to the PSA test, the S3M reflex thresholds of 1, 1.5 and 2 ng/mL had ICERs of 170,000, 60,000 and 6,000 EUR/QALY, respectively. The S3M test was more cost-effective at higher biopsy costs.

Conclusions

Prostate cancer screening using the S3M test for men with an initial PSA ≥ 2.0 ng/mL was cost-effective compared with screening using the PSA test alone.

Does Multiparametric Magnetic Resonance of Prostate Outperform Risk Calculators in Predicting Prostate Cancer in Biopsy Naïve Patients?

Background

European Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa).

Objectives

To compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC.

Design, Setting, and Participants

Single-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI.

Outcome Measurements and Statistical Analysis

Patients’ probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models.

Results and Limitation

The FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator.

Conclusions

mpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators

Prostate Cancer Risk Calculator Apps in a Taiwanese Population Cohort: Validation Study

Background

Mobile health apps have emerged as useful tools for patients and clinicians alike, sharing health information or assisting in clinical decision-making. Prostate cancer (PCa) risk calculator mobile apps have been introduced to assess risks of PCa and high-grade PCa (Gleason score ≥7). The Rotterdam Prostate Cancer Risk Calculator and Coral–Prostate Cancer Nomogram Calculator apps were developed from the 2 most-studied PCa risk calculators, the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the North American Prostate Cancer Prevention Trial (PCPT) risk calculators, respectively. A systematic review has indicated that the Rotterdam and Coral apps perform best during the prebiopsy stage. However, the epidemiology of PCa varies among different populations, and therefore, the applicability of these apps in a Taiwanese population needs to be evaluated. This study is the first to validate the PCa risk calculator apps with both biopsy and prostatectomy cohorts in Taiwan.

Objective

The study’s objective is to validate the PCa risk calculator apps using a Taiwanese cohort of patients. Additionally, we aim to utilize postprostatectomy pathology outcomes to assess the accuracy of both apps with regard to high-grade PCa.

Methods

All male patients who had undergone transrectal ultrasound prostate biopsies in a single Taiwanese tertiary medical center from 2012 to 2018 were identified retrospectively. The probabilities of PCa and high-grade PCa were calculated utilizing the Rotterdam and Coral apps, and compared with biopsy and prostatectomy results. Calibration was graphically evaluated with the Hosmer-Lemeshow goodness-of-fit test. Discrimination was analyzed utilizing the area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed for clinical utility.

Results

Of 1134 patients, 246 (21.7%) were diagnosed with PCa; of these 246 patients, 155 (63%) had high-grade PCa, according to the biopsy results. After confirmation with prostatectomy pathological outcomes, 47.2% (25/53) of patients were upgraded to high-grade PCa, and 1.2% (1/84) of patients were downgraded to low-grade PCa. Only the Rotterdam app demonstrated good calibration for detecting high-grade PCa in the biopsy cohort. The discriminative ability for both PCa (AUC: 0.779 vs 0.687; DeLong’s method: P<.001) and high-grade PCa (AUC: 0.862 vs 0.758; P<.001) was significantly better for the Rotterdam app. In the prostatectomy cohort, there was no significant difference between both apps (AUC: 0.857 vs 0.777; P=.128).

Conclusions

The Rotterdam and Coral apps can be applied to the Taiwanese cohort with accuracy. The Rotterdam app outperformed the Coral app in the prediction of PCa and high-grade PCa. Despite the small size of the prostatectomy cohort, both apps, to some extent, demonstrated the predictive capacity for true high-grade PCa, confirmed by the whole prostate specimen. Following our external validation, the Rotterdam app might be a good alternative to help detect PCa and high-grade PCa for Taiwanese men.

Biomarker discrimination and calibration with MRI-targeted biopsies: an analysis with the Stockholm3 test

BACKGROUND

The validated Stockholm3 test is used to improve PC detection. Stockholm3, however, was developed using systematic biopsies. We aimed to assess Stockholm3 operating performance when using MRI-targeted biopsies for PC detection.

METHODS

A prospective cohort of 532 men was considered for prostate biopsy during 2016-2017. All men underwent Stockholm3 testing and MRI before biopsy. All PIRADs ≥3 lesion underwent targeted biopsy; all men underwent systematic biopsy. The primary outcome was ISUP Grade Group ≥2 (GG ≥ 2) PC. Detection strategies included: (1) systematic biopsies alone, (2) targeted biopsies alone, (3) targeted with associated systematic biopsies for MRI+, and (4) all biopsies in all men. For each strategy, the Stockholm3 operating characteristics were assessed with discrimination, calibration, and decision curve analysis (DCA).

RESULTS

Median age was 65 years, median PSA was 6.2 ng/mL, median Stockholm3 score was 16.5%, and overall detection of GG ≥ 2 PC was 36% (193/532). Stockholm3 showed accurate discrimination for separating GG ≥ 2 cancer from benign and GG1, with an area under the curve of 0.84-0.86 depending on the biopsy strategy. Calibration analysis showed that Stockholm3 underestimated risks for GG ≥ 2 PC risk using MRI-targeted biopsies: there was a net benefit over biopsies in all men for Stockholm3 at risk thresholds varying from >3% in systematic biopsies to >15% in targeted with systematic biopsies in MRI+ men. When using a Stockholm3 score of >10% cutoff, a range of 32-38% of biopsies could be avoided while missing 5-11% of GG ≥ 2 PC and 0-3% of GG ≥ 3 PC.

CONCLUSIONS

Stockholm3 shows high discriminatory performance in an MRI-targeted biopsy setting, however risks are underpredicted due to MRI-targeted biopsies being more sensitive than the systematic biopsies for which Stockholm3 was developed. Stockholm3, along with any risk prediction model developed for systematic prostate biopsy decisions, will need recalibration for optimal use in an MRI-driven biopsy setting.

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed.

Incorporating Prognostic Biomarkers into Risk Assessment Models and TNM Staging for Prostate Cancer

In current practice, prostate cancer staging alone is not sufficient to adequately assess the patient's prognosis and plan the management strategies. Multiple clinicopathological parameters and risk tools for prostate cancer have been developed over the past decades to better characterize the disease and provide an enhanced assessment of prognosis. Herein, we review novel prognostic biomarkers and their integration into risk assessment models for prostate cancer focusing on their capability to help avoid unnecessary imaging studies, biopsies and diagnosis of low risk prostate cancers, to help in the decision-making process between active surveillance and treatment intervention, and to predict recurrence after radical prostatectomy. There is an imperative need of reliable biomarkers to stratify prostate cancer patients that may benefit from different management approaches. The integration of biomarkers panel with risk assessment models appears to improve prostate cancer diagnosis and management. However, integration of novel genomic biomarkers in future prognostic models requires further validation in their clinical efficacy, standardization, and cost-effectiveness in routine application.

Effects of replacing PSA with Stockholm3 for diagnosis of clinically significant prostate cancer in a healthcare system - the Stavanger experience

OBJECTIVE

To describe early experience of replacing PSA with Stockholm3 for detection of prostate cancer in primary care.

DESIGN AND METHODS

Longitudinal observations, comparing outcome measures before and after the implementation of Stockholm3.

SETTING

Stavanger region in Norway with about 370,000 inhabitants, 304 general practitioners (GPs) in 97 primary care clinics, and one hospital.

INTERVENTION

GPs were instructed to use Stockholm3 instead of PSA as standard procedure for diagnosis of prostate cancer.

MAIN OUTCOME MEASURES

Proportion of GP clinics that had ordered a Stockholm3 test. Number of men referred to needle biopsy. Distribution of clinically significant prostate cancer (csPC) (Gleason Score ≥7) and clinically non-significant prostate cancer (cnsPC) (Gleason Score 6), in needle biopsies. Estimation of direct healthcare costs.

RESULTS

Stockholm3 was rapidly implemented as 91% (88/97) of the clinics started to use the test within 14 weeks. After including 4784 tested men, the percentage who would have been referred for prostate needle biopsy was 29.0% (1387/4784) if based on PSA level ≥3ng/ml, and 20.8% (995/4784) if based on Stockholm3 Risk Score (p < 0.000001). The proportion of positive biopsies with csPC increased from 42% (98/233) before to 65% (185/285) after the implementation. Correspondingly, the proportion of cnsPC decreased from 58% (135/233) before to 35% (100/285) after the implementation (p < 0.0017). Direct healthcare costs were estimated to be reduced by 23-28% per tested man.

CONCLUSION

Replacing PSA with Stockholm3 for early detection of prostate cancer in primary care is feasible. Implementation of Stockholm3 resulted in reduced number of referrals for needle-biopsy and a higher proportion of clinically significant prostate cancer findings in performed biopsies. Direct healthcare costs decreased. KEY POINTS A change from PSA to Stockholm3 for the diagnosis of prostate cancer in primary care in the Stavanger region in Norway is described and assessed. •Implementation of a new blood-based test for prostate cancer detection in primary care was feasible. A majority of GP clinics started to use the test within three months. •Implementation of the Stockholm3 test was followed by: -a 28% reduction in number of men referred for urological prostate cancer work-up -an increase in the proportion of clinically significant cancer in performed prostate biopsies from 42 to 65% -an estimated reduction in direct health care costs between 23 and 28%.

Modern biomarkers in prostate cancer diagnosis

INTRODUCTION

The most common malignant neoplasm of the urinary tract is prostate cancer (PCa), which is a heterogeneous disease, ranging from very slowly developing and slightly benign to progressing, aggressive, metastatic and fatal, even when properly treated. Existing, imperfect diagnostic methods often lead to over-diagnosis and over-treatment of PCa. That is why new, better PCa biomarkers are being developed.

MATERIAL AND METHODS

This review summarizes the current results of the most promising and clinically used PCa biomarkers, as well as having the potential to create new diagnostic and prognostic tools, based on the Web of Science (www.apps.webofknowledge.com) and Scopus (www.scopus) databases. com).

RESULTS

Limited specificity of the prostate-specific antigen (PSA) test brings a need to develop new and better diagnostic tools. In the last few years, new approaches for providing significantly better biomarkers, an alternative to PSA, have been introduced. Modern biomarkers show improvement in being used as not only a diagnostic procedure, but also for staging, evaluating aggressiveness and managing the therapeutic process. We describe the methods recommended in the diagnosis of PCa and new PCa molecular diagnostics technologies. Individual biomarkers are used in various stages of the PCa diagnostic process, which was presented on the developed diagnostic flowchart describing the role of biomarkers in prostate cancer management.

CONCLUSIONS

Given the diverse nature of PCa, one diagnostic test will not answer all questions, so the use of several diagnostic methods will allow physicians to provide patients with better, personalized clinical advice.

The role of metabolic syndrome in high grade prostate cancer: development of a clinical nomogram

BACKGROUND

The aim of our study is to develop a clinical nomogram including metabolic syndrome status for the prediction of high-grade prostate cancer (HG PCa).

METHODS

A series of men at increased risk of PCa undergoing prostate biopsies were enrolled in a single center. Demographic and clinical characteristics of the patients were recorded. Metabolic syndrome was defined according to the adult treatment panel III. A nomogram was generated based on the logistic regression model and used to predict high grade prostate cancer defined as grade group ≥3 (ISUP 2014). ROC curves, calibration plots and decision curve analysis were used to evaluate the performance of the nomogram.

RESULTS

Overall, 738 patients were enrolled. Greater than or equal to 294/738 (40%) of the patients presented PCa and of those patients, 84/294 (39%) presented high grade disease (Grade Group ≥3). On multivariate analysis, DRE (OR: 3.24, 95% CI: 1.80-5.84), PSA (OR: 1.10, 95% CI: 1.05-1.16), PV (OR: 0.98, 95% CI: 0.97-0.99) and MetS (OR: 2.02, 95% CI: 1.13-3.59) were predictors of HG PCa. The nomogram based on the model presented good discrimination (AUC: 0.76), good calibration (Hosmer-Lemeshow Test, P>0.05) and a net benefit in the range of probabilities between 10% and 70%.

CONCLUSIONS

Metabolic syndrome is highly prevalent in patients at risk of prostate cancer and is particularly associated with high-grade prostate cancer. Our nomogram offers the possibility to include metabolic status in the assessment of patients at risk of prostate cancer to identify men who may have a high-grade form of the disease. External validation is warranted before its clinical implementation.

Ethnic variation in prostate cancer detection: a feasibility study for use of the Stockholm3 test in a multiethnic U.S. cohort

BACKGROUND

The Stockholm3 test improves Gleason Grade Group ≥2 (GG ≥ 2) prostate cancer (PC) detection, however it has not been evaluated in an American cohort where clinical practice patterns and ethnicity differ. We aimed to identify subgroups within a Stockholm population with PC risk profiles matching American ethnicity-specific subgroups and compare the detection of PC and describe Stockholm3 performance within these subgroups.

METHODS

All men age 49-70 years presenting for prostate biopsies were evaluated at UIC from 2016 to 2019, as well as men in Stockholm from 2012 to 2014 in the STHLM3 study. Propensity scores (PS) were estimated for each person using logistic regression for age, PSA, prostate volume, family history of PC, 5-alpha reductase inhibitor use, and prior biopsy. 3:1 PS matching was performed for Stockholm to Chicago ethnicity-specific cohorts and odds ratios (OR) were computed to compare detection of GG ≥ 2 PC between groups.

RESULTS

504 Chicago men and 6980 Stockholm men were included. In African American (AA) men, 51% had GG ≥ 2 PC detected, while in risk-matched Stockholm men, 34% had GG ≥ 2 PC detected (OR: 2.1, p < 0.001). There was no statistical difference in GG ≥ 2 PC detected when matching Stockholm men to non-Hispanic Caucasian men (31% vs. 24%, OR: 0.7, p = 0.30) or Hispanic Caucasian men (31% vs. 27%, OR: 1.2, p = 0.42). The AUC for the Stockholm3 test of the matched Stockholm cohorts for AA, non-Hispanic Caucasian, and Hispanic Caucasian men was 0.85, 0.89, and 0.90, respectively.

CONCLUSIONS

Using statistical techniques to simulate a multi-ethnic Chicago cohort within the STHLM3 population, we found an excess risk of GG ≥ 2 PC among AA men. Our hypothesis that the Stockholm3 may have good predictive value in a multiethnic cohort is strengthened, and that recalibration to at least AA men seems likely to be needed to obtain well-calibrated predictions.

Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population

PURPOSE

To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps.

METHODS

1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented.

RESULTS

Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed.

CONCLUSION

Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population.

Comparing the prediction of prostate biopsy outcome using the Chinese Prostate Cancer Consortium (CPCC) Risk Calculator and the Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer (ERSPC) Risk Calculator in Chinese and European men

PURPOSE

To externally validate the clinical utility of Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) and Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (A-ERSPC-RC3) for prediction prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 3 + 4) in both Chinese and European populations.

MATERIALS AND METHODS

The Chinese clinical cohort, the European population-based screening cohort, and the European clinical cohort included 2,508, 3,616 and 617 prostate biopsy-naive men, respectively. The area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analyses were applied in the analysis.

RESULTS

The CPCC-RC's predictive ability for any PCa (AUC 0.77, 95% CI 0.75-0.79) was lower than the A-ERSPC-RC3 (AUC 0.79, 95% CI 0.77-0.81) in the European screening cohort (p < 0.001), but similar for HGPCa (p = 0.24). The CPCC-RC showed lower predictive accuracy for any PCa (AUC 0.65, 95% CI 0.61-0.70), but acceptable predictive accuracy for HGPCa (AUC 0.73, 95% CI 0.69-0.77) in the European clinical cohort. The A-ERSPC-RC3 showed an AUC of 0.74 (95% CI 0.72-0.76) in predicting any PCa, and a similar AUC of 0.74 (95% CI 0.72-0.76) in predicting HGPCa in Chinese cohort. In the Chinese population, decision curve analysis revealed a higher net benefit for CPCC-RC than A-ERSPC-RC3, while in the European screening and clinical cohorts, the net benefit was higher for A-ERSPC-RC3.

CONCLUSIONS

The A-ERSPC-RC3 accurately predict the prostate biopsy in a contemporary Chinese multi-center clinical cohort. The CPCC-RC can predict accurately in a population-based screening cohort, but not in the European clinical cohort.

Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1

OBJECTIVES

To evaluate the agreement among readers with different expertise in detecting suspicious lesions at prostate multiparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.

METHODS

We evaluated 200 consecutive biopsy-naïve or previously negative biopsy men who underwent MRI for clinically suspected prostate cancer (PCa) between May and September 2017. Of them, 132 patients underwent prostate biopsy. Seven radiologists (four dedicated uro-radiologists and three non-dedicated abdominal radiologists) reviewed and scored all MRI examinations according to PI-RADS v2.1. Agreement on index lesion detection was evaluated with Conger's k coefficient, agreement coefficient 1 (AC1), percentage of agreement (PA), and indexes of specific positive and negative agreement. Clinical and radiological features that may influence variability were evaluated.

RESULTS

Agreement in index lesion detection among all readers was substantial (AC1 0.738; 95% CI 0.695-0.782); dedicated radiologists showed higher agreement compared with non-dedicated readers. Clinical and radiological parameters that positively influenced agreement were PSA density ≥ 0.15 ng/mL/cc, pre-MRI high risk for PCa, positivity threshold of PI-RADS score 4 + 5, PZ lesions, homogeneous signal intensity of the PZ, and subjectively easy interpretation of MRI. Positive specific agreement was significantly higher among dedicated readers, up to 93.4% (95% CI 90.7-95.4) in patients harboring csPCa. Agreement on absence of lesions was excellent for both dedicated and non-dedicated readers (respectively 85.1% [95% CI 78.4-92.3] and 82.0% [95% CI 77.2-90.1]).

CONCLUSIONS

Agreement on index lesion detection among radiologists of various experiences is substantial to excellent using PI-RADS v2.1. Concordance on absence of lesions is excellent across readers' experience.

KEY POINTS

• Agreement on index lesion detection among radiologists of various experiences is substantial to excellent using PI-RADS v2.1. • Concordance between experienced readers is higher than between less-experienced readers. • Concordance on absence of lesions is excellent across readers' experience.

A comparative effectiveness analysis of the PBCG vs. PCPT risks calculators in a multi-ethnic cohort

Background

Predictive models that take race into account like the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPT RC) and the new Prostate Biopsy Collaborative Group (PBCG) RC have been developed to equitably mitigate the overdiagnosis of prostate specific antigen (PSA) screening. Few studies have compared the performance of both calculators across racial groups.

Methods

From 1485 prospectively recruited participants, 954 men were identified undergoing initial prostate biopsy for abnormal PSA or digital rectal examination in five Chicago hospitals between 2009 and 2014. Discrimination, calibration, and frequency of avoided biopsies were calculated to assess the performance of both risk calculators.

Results

Of 954 participants, 463 (48.5%) were Black, 355 (37.2%) were White, and 136 (14.2%) identified as Other. Biopsy results were as follows: 310 (32.5%) exhibited no cancer, 323 (33.9%) indolent prostate cancer, and 321 (33.6%) clinically significant prostate cancer (csPCa). Differences in area under the curve (AUC)s for the detection of csPCa between PCPT and PBCG were not statistically different across all racial groups. PBCG did not improve calibration plots in Blacks and Others, as it showed higher levels of overprediction at most risk thresholds. PCPT led to an increased number of avoidable biopsies in minorities compared to PBCG at the 30% threshold (68% vs. 28% of all patients) with roughly similar rates of missed csPCa (23% vs. 20%).

Conclusion

Significant improvements were noticed in PBCG’s calibrations and net benefits in Whites compared to PCPT. Since PBCG’s improvements in Blacks are disputable and potentially biases a greater number of low risk Black and Other men towards unnecessary biopsies, PCPT may lead to better biopsy decisions in racial minority groups. Further comparisons of commonly used risk calculators across racial groups is warranted to minimize excessive biopsies and overdiagnosis in ethnic minorities.

Avoiding Unnecessary Magnetic Resonance Imaging (MRI) and Biopsies: Negative and Positive Predictive Value of MRI According to Prostate-specific Antigen Density, 4Kscore and Risk Calculators

The 2019 European Association of Urology guidelines recommend multiparametric magnetic resonance imaging (mpMRI) for biopsy-naïve patients with clinical suspicion of prostate cancer (PC) and avoiding biopsy in patients with negative mpMRI and low clinical suspicion. However, consensus on the optimal definition of low clinical suspicion is lacking. We evaluated 266 biopsy-naïve patients who underwent mpMRI, the 4Kscore test, and prostate biopsy to define the best strategy to avoid unnecessary testing and biopsies. The European Randomized Study of Screening for Prostate Cancer risk calculator (ERSPC-RC) and prostate-specific antigen density (PSAd) were also considered. For men with Prostate Imaging-Reporting and Data System v2.0 (PI-RADS) 1⿿2 lesions, the highest negative predictive value was observed for those with low or intermediate 4Kscore risk (96.9% and 97.1%), PSAd <0.10ng/ml/cm³ (98.7%), and ERSPC-RC <2% (98.7%). For men with PI-RADS 3⿿5 lesions the lowest positive predictive value was observed for those with low 4Kscore risk (0%), PSAd <0.10ng/ml/cm³ (13.2%), and ERSPC-RC <2% (12.3%). The best biopsy strategy was an initial 4Kscore followed by mpMRI if the 4Kscore was>7.5% and a subsequent biopsy if the mpMRI was positive (PI-RADS 3⿿5) or the 4Kscore was ⿥18%. This would result in missing 2.7% (2/74) of clinically significant PCs (csPCs) and avoiding 34.2% of biopsies. Initial mpMRI followed by biopsy for negative mpMRI (PI-RADS 1⿿2) if the 4Kscore was ⿥18% or PSAd was ⿥0.10ng/ml/cm³ resulted in a similar percentage of csPC missed (2.7% [2/74] and 1.3% [1/74]) but slightly fewer biopsies avoided (25.2% and 28.1%). Physicians should consider clinical risk screening tools when ordering and interpreting mpMRI results to avoid unnecessary testing and diagnostic errors. PATIENT SUMMARY: Performing the 4Kscore test in conjunction with multiparametric magnetic resonance imaging for men with a clinical suspicion of prostate cancer may help to reduce unnecessary biopsies.

Structured Population-based Prostate-specific Antigen Screening for Prostate Cancer: The European Association of Urology Position in 2019

Prostate cancer (PCa) is one of the first three causes of cancer mortality in Europe. Screening in asymptomatic men (aged 55-69yr) using prostate-specific antigen (PSA) is associated with a migration toward lower staged disease and a reduction in cancer-specific mortality. By 20yr after testing, around 100 men need to be screened to prevent one PCa death. While this ratio is smaller than for breast and colon cancer, the long natural history of PCa means many men die from other causes. As such, the nonselective use of PSA testing and radical treatments can lead to overdiagnosis and overtreatment. The European Association of Urology (EAU) supports measures to encourage appropriate PCa detection through PSA testing, while reducing overdiagnosis and overtreatment. These goals may be achieved using personalized risk-stratified approaches. For diagnosis, the greatest benefit from early detection is likely to come in men assessed using baseline PSA levels at the age of 45yr to individualize screening intervals. Multiparametric magnetic resonance imaging as well as risk calculators based on family history, ethnicity, digital rectal examination, and prostate volume should be considered to triage the need for biopsy, thus reducing the risk of overdiagnosis. For treatment, the EAU advocates balancing patient's life expectancy and cancer's mortality risk when deciding an approach. Active surveillance is encouraged in well-informed patients with low-risk and some intermediate-risk cancers, as it decreases the risks of overtreatment without compromising oncological outcomes. Conversely, the EAU advocates radical treatment in suitable men with more aggressive PCa. Multimodal treatment should be considered in locally advanced or high-grade cancers. PATIENT SUMMARY: Implementation of prostate-specific antigen (PSA)-based screening should be considered at a population level. Men at risk of prostate cancer should have a baseline PSA blood test (eg, at 45yr). The level of this test, combined with family history, ethnicity, and other factors, can be used to determine subsequent follow-up. Magnetic resonance imaging scans and novel biomarkers should be used to determine which men need biopsy and how any cancers should be treated.

Prediction Medicine: Biomarkers, Risk Calculators and Magnetic Resonance Imaging as Risk Stratification Tools in Prostate Cancer Diagnosis

This review discusses the most recent evidence for currently available risk stratification tools in the detection of clinically significant prostate cancer (csPCa), and evaluates diagnostic strategies that combine these tools. Novel blood biomarkers, such as the Prostate Health Index (PHI) and 4Kscore, show similar ability to predict csPCa. Prostate cancer antigen 3 (PCA3) is a urinary biomarker that has inferior prediction of csPCa compared to PHI, but may be combined with other markers like TMPRSS2-ERG to improve its performance. Original risk calculators (RCs) have the advantage of incorporating easy to retrieve clinical variables and being freely accessible as a web tool/mobile application. RCs perform similarly well as most novel biomarkers. New promising risk models including novel (genetic) markers are the SelectMDx and Stockholm-3 model (S3M). Prostate magnetic resonance imaging (MRI) has evolved as an appealing tool in the diagnostic arsenal with even stratifying abilities, including in the initial biopsy setting. Merging biomarkers, RCs and MRI results in higher performances than their use as standalone tests. In the current era of prostate MRI, the way forward seems to be multivariable risk assessment based on blood and clinical parameters, potentially extended with information from urine samples, as a triaging test for the selection of candidates for MRI and biopsy.

Genetic and Epigenetic Determinants of Aggressiveness in Cribriform Carcinoma of the Prostate

Among prostate cancers containing Gleason pattern 4, cribriform morphology is associated with unfavorable clinicopathologic factors, but its genetic features and association with long-term outcomes are incompletely understood. In this study, genetic, transcriptional, and epigenetic features of invasive cribriform carcinoma (ICC) tumors were compared with non-cribriform Gleason 4 (NC4) in The Cancer Genome Atlas (TCGA) cohort. ICC (n = 164) had distinctive molecular features when compared with NC4 (n = 102). These include: (i) increased somatic copy number variations (SCNV), specifically deletions at 6q, 8p and 10q, which encompassed PTEN and MAP3K7 losses and gains at 3q; (ii) increased SPOP mut and ATMmut ; (iii) enrichment for mTORC1 and MYC pathways by gene expression; and (iv) increased methylation of selected genes. In addition, when compared with the metastatic prostate cancer, ICC clustered more closely to metastatic prostate cancer than NC4. Validation in clinical cohorts and genomically annotated murine models confirmed the association with SPOPmut (n = 38) and PTENloss (n = 818). The association of ICC with lethal disease was evaluated in the Health Professionals Follow-up Study (HPFS) and Physicians' Health Study (PHS) prospective prostate cancer cohorts (median follow-up, 13.4 years; n = 818). Patients with ICC were more likely to develop lethal cancer [HR, 1.62; 95% confidence interval (CI), 1.05-2.49], independent from Gleason score (GS). IMPLICATIONS: ICC has a distinct molecular phenotype that resembles metastatic prostate cancer and is associated with progression to lethal disease.

Liquid Biopsy Potential Biomarkers in Prostate Cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide with an incidence of 14.8% and a mortality of 6.6%. Shortcomings in comprehensive medical check-ups in low- and middle-income countries lead to delayed detection of PCa and are causative of high numbers of advanced PCa cases at first diagnosis. The performance of available biomarkers is still insufficient and limited applicability, including logistical and financial burdens, impedes comprehensive implementation into health care systems. There is broad agreement on the need of new biomarkers to improve (i) early detection of PCa, (ii) risk stratification, (iii) prognosis, and (iv) treatment monitoring. This review focuses on liquid biopsy tests distinguishing high-grade significant (Gleason score (GS) ≥ 7) from low-grade indolent PCa. Available biomarkers still lack performance in risk stratification of biopsy naïve patients. However, biomarkers with highly negative predictive values may help to reduce unnecessary biopsies. Risk calculators using integrative scoring systems clearly improve decision-making for invasive prostate biopsy. Emerging biomarkers have the potential to substitute PSA and improve the overall performance of risk calculators. Until then, PSA should be used and may be replaced whenever enough evidence has accumulated for better performance of a new biomarker.

Downregulation of miR-133a-3p promotes prostate cancer bone metastasis via activating PI3K/AKT signaling

BACKGROUND

Bone metastasis is a leading cause of morbidity and mortality in advanced prostate cancer (PCa). Downexpression of miR-133a-3p has been found to contribute to the progression, recurrence and distant metastasis in PCa. However, clinical significance of miR-133a-3p in bone metastasis of PCa, and the biological role of miR-133a-3p and its molecular mechanisms underlying bone metastasis of PCa remain unclear.

METHODS

miR-133a-3p expression was evaluated in 245 clinical PCa tissues by real-time PCR. Statistical analysis was performed to evaluate the clinical correlation between miR-133a-3p expression and clinicopathological features, and overall and bone metastasis-free survival in PCa patients. The biological roles of miR-133a-3p in the bone metastasis of PCa were investigated both in vitro and in vivo. Bioinformatics analysis, real-time PCR, western blot and luciferase reporter analysis were applied to demonstrate the relationship between miR-133a-3p and its potential targets. Western blotting and luciferase assays were examined to identify the underlying pathway involved in the anti-tumor role of miR-133a-3p. Clinical correlation of miR-133a-3p with its targets was verified in human PCa tissues.

RESULTS

miR-133a-3p expression is reduced in PCa tissues compared with the adjacent normal tissues and benign prostate lesion tissues, particularly in bone metastatic PCa tissues. Low expression of miR-133a-3p is significantly correlated with advanced clinicopathological characteristics and shorter bone metastasis-free survival in PCa patients by statistical analysis. Moreover, upregulating miR-133a-3p inhibits cancer stem cell-like phenotypes in vitro and in vivo, as well as attenuates anoikis resistance in vitro in PCa cells. Importantly, administration of agomir-133a-3p greatly suppresses the incidence of PCa bone metastasis in vivo. Our results further demonstrate that miR-133a-3p suppresses bone metastasis of PCa via inhibiting PI3K/AKT signaling by directly targeting multiple cytokine receptors, including EGFR, FGFR1, IGF1R and MET. The negative clinical correlation of miR-133a-3p with EGFR, FGFR1, IGF1R, MET and PI3K/AKT signaling activity is determined in clinical PCa tissues.

CONCLUSION

Our results unveil a novel mechanism by which miR-133a-3p inhibits bone metastasis of PCa, providing the evidence that miR-133a-3p may serve as a potential bone metastasis marker in PCa, and delivery of agomir-133a-3p may be an effective anti-bone metastasis therapeutic strategy in PCa.

Prostate cancer with cribriform morphology: diagnosis, aggressiveness, molecular pathology and possible relationships with intraductal carcinoma

INTRODUCTION

The Gleason grading system is one of the most important prognostic factors in prostate cancer (PCa). From the 2005 to the 2014 conference organized by the International Society of Urological Pathology (ISUP), the histological criteria for the Gleason patterns were improved, resulting in the shrinkage of the Gleason pattern (GP) 3 and expansion of the GP 4. Areas Covered: Cribriform, fused, ill-defined and glomeruloid glands are part of the morphologic spectrum of the current GP 4. Cribriform, derived from the Latin word cribrum (i.e. sieve), was introduced by Gleason to describe glands composed of a solid sheet with perforations or lumina. Cribriform morphology has a worse prognosis compared with the other, non-cribriform, GP4 morphologies. A practical implication is that a cribriform growth precludes a patient from selecting an active surveillance (AS) protocol. Expert commentary: The presence of these four growth patterns should be incorporated into the current Grade Group (GG) system. Enhancing our understanding of cribriform tumor behavior will lead to correctly identifying and treating those patients that will die because of PCa, while sparing treatment in those who do not require it.

Stopping screening, when and how?

Screening for prostate cancer (PC) is still controversial despite randomized trials has found that PC mortality is decreased. The major concern is the high rate of over-diagnosis and subsequent harms that may follow in many men who never would have had any symptoms during life-time if not screened. The high rate of over-diagnosis is driven by the large reservoir of small non-significant cancers that increases with age and found in more than half of men over 70 years, the low specificity of prostate specific antigen (PSA) but also by current "blind" biopsy technique that risk accidentally to hit these small non-significant cancers. The risk of over-diagnosis is increasing with age and the trade-off by screening men in high age is probably higher. At what age harms exceeds benefits is not established but modelling studies has demonstrated that after 65-70 years the quality adjusted life-years (QALYs) gained are decreasing but still on the positive side if screening is continued up to 75 years. A dilemma is that most PC deaths occur in men after the age 80 and the effect of screening seems not to last as long as was thought, already 10 years after termination of screening PC mortality has caught up in the screening arm to a level similar to that in the control group. In the European Randomised Study of Screening for Prostate Cancer (ERSPC) screening was terminated between 71 and 75 years and the first effect of screening on mortality was discernible after 7 years and has been relatively stable around 20% since 9 years after study start. It seems questionable from any standpoint to invite men for screening with an expected life length below 10 years, which is the expected life-length of an averaged 78-year-old man in 2018. However, the balance between harms and benefits specifically in the age 70-80 need more attention as also costs need. Permanent side-effects from (unnecessary) treatments and their impact on quality of life must be evaluated better and related to age and individual variations. In future better screening methods with more specific markers and introduction of imaging will hopefully decrease the present large risk of over-diagnosis in elderly men and thereby expand efficient screening to men in the age group >70 who actually is the high risk group of dying from PC.