Prediction of Prostate Cancer: External Validation of the ERSPC Risk Calculator in a Contemporary Dutch Clinical Cohort

Cost Analysis of Prostate Cancer Care Using a Biomarker-enhanced Diagnostic Strategy with Stockholm3

Background and objective

Building on previous research demonstrating better prostate cancer (PC) diagnostics via a biomarker-enhanced approach, this study focuses on cost analysis of PC care using the Stockholm3 test. We assessed the economic impact in European health care systems using real-world evidence for diagnostic outcomes and relevant costs.

Methods

We evaluated two PC diagnostic strategies: (1) the conventional prostate-specific antigen (PSA) strategy with magnetic resonance imaging (MRI) and (2) PSA testing with a reflex to biomarkers at PSA ≥1.5 ng/ml in guiding decisions to perform MRI. Data from the Swedish National Prostate Cancer Register and Capio St. Göran Hospital provided real-world evidence, supplemented by health economic modeling. A comprehensive cost analysis was conducted using a Markov model for treatment pathways for four PC disease states and overall spending, for which costs from various European health care systems were used. A deterministic sensitivity analysis was performed across different cost and diagnostic scenarios.

Key finding and limitations

The average cost for the four disease states was €2 182 for benign disease, €10 023 for low-grade disease, €13 073 for intermediate- to high-grade localized or locally advance disease, and €271 210 for metastatic disease. The overall spending was €358 239 (7.7%) lower per 1000 men tested in the biomarker-enhanced strategy in comparison to the PSA strategy. The primary cost saving was attributed to lower treatment expenses for metastatic disease. Sensitivity analysis affirmed the robustness of the findings across various diagnostic and treatment scenarios.

Conclusions and clinical implications

Biomarker-enhanced diagnostic strategies may reduce health care costs for PC management and are likely to improve quality-adjusted life years in a scenario in which metastatic disease is reduced.

Patient summary

We explored different ways to detect prostate cancer more cost-effectively. We found that using a specific blood test, called Stockholm3, after a PSA (prostate-specific antigen) test to decide if an MRI scan (magnetic resonance imaging) is necessary could save money, mainly by identifying localized cancer earlier and reducing the need for expensive treatments for advanced cancer.

Development and validation of a predictive score for diagnosing prostate cancer in primary care

BACKGROUND

Prostate cancer (PCa) represents the fifth cause of death in the male population worldwide. The prostate-specific antigen (PSA) test demonstrated poor accuracy to assess the presence of PCa. Thus, the PSA testing paradigm should be moved from the systematic screening approach to the early identification of men who are harbouring clinically significant disease. Accurate clinical-based tools to predict PCa should therefore be developed for general practice. We derived and validated a PCa predictive score using a primary care data source.

METHODS

Using the Italian Health Search Database, we formed a cohort of men aged 45-90 years in the period between 2002 and 2015. These patients were followed up until 31 December 2022. Those with less than a 5-year follow-up were excluded. The cohort was randomly divided into 'derivation' and 'validation' samples in a 1:1 ratio. Along with the demographic and clinical determinants forming the score, we investigated the role of PSA kinetics in the prediction accuracy.

RESULTS

In a cohort of 529,082 men aged 45+ years, we identified 14,524 cases of PCa (incidence rate = 2.71 per 1000 person-years; 95% confidence interval = 2.67-2.80). The prediction accuracy of the PCa-HScore featured an explained variation of 12% and a discrimination power of 70%. The calibration slope was almost equal to 1 (p = 0.951, tested for equivalence against the 'perfect' slope) and the PSA kinetics did not improve the prediction accuracy.

CONCLUSIONS

The PCa-HScore might guide the prescription of PSA and/or other clinical strategies in those men reporting certain levels of risk. A related decision support system could therefore be implemented in primary care.

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.

Comparison between the European Randomized Study for Screening of Prostate Cancer (ERSPC) and Prostate Biopsy Collaborative Group (PBCG) risk calculators: Prediction of clinically significant Prostate Cancer risk in a cohort of patients from Argentina

OBJECTIVE

To compare the performance of the risk calculators of the European Randomized Study for Screening of Prostate Cancer (ERSPC) and the Prostate Biopsy Collaborative Group (PBCG) in predicting the risk of presenting clinically significant prostate cancer.

MATERIAL AND METHODS

Retrospectively, patients who underwent prostate biopsy at Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, were identified from January 2018 to December 2021. The probability of having prostate cancer was calculated with the two calculators separately and then the results were compared to establish which of the two performed better. For this, areas under the curve (AUC) were analyzed.

RESULTS

250 patients were included, 140 (56%) presented prostate cancer, of which 92 (65.71%) had clinically significant prostate cancer (Gleason score ≥7). The patients who presented cancer were older, had a higher prostate-specific antigen (PSA) value, and had a smaller prostate size. The AUC to predict the probability of having clinically significant prostate cancer was 0.79 and 0.73 for PBCG-RC and ERSPC-RC respectively (P=0.0084).

CONCLUSION

In this cohort of patients, both prostate cancer risk calculators performed well in predicting clinically significant prostate cancer risk, although the PBCG-RC showed better accuracy.

Development and validation of a nomogram based on biparametric MRI PI-RADS v2.1 and clinical parameters to avoid unnecessary prostate biopsies

BACKGROUND

Biparametric MRI (bpMRI) is a faster, contrast-free, and less expensive MRI protocol that facilitates the detection of prostate cancer. The aim of this study is to determine whether a biparametric MRI PI-RADS v2.1 score-based model could reduce unnecessary biopsies in patients with suspected prostate cancer (PCa).

METHODS

The patients who underwent MRI-guided biopsies and systematic biopsies between January 2020 and January 2022 were retrospectively analyzed. The development cohort used to derive the prediction model consisted of 275 patients. Two validation cohorts included 201 patients and 181 patients from 2 independent institutions. Predictive models based on the bpMRI PI-RADS v2.1 score (bpMRI score) and clinical parameters were used to detect clinically significant prostate cancer (csPCa) and compared by analyzing the area under the curve (AUC) and decision curves. Spearman correlation analysis was utilized to determine the relationship between International Society of Urological Pathology (ISUP) grade and clinical parameters/bpMRI score.

RESULTS

Logistic regression models were constructed using data from the development cohort to generate nomograms. By applying the models to the all cohorts, the AUC for csPCa was significantly higher for the bpMRI PI-RADS v2.1 score-based model than for the clinical model in both cohorts (p < 0.001). Considering the test trade-offs, urologists would agree to perform 10 fewer bpMRIs to avoid one unnecessary biopsy, with a risk threshold of 10-20% in practice. Correlation analysis showed a strong correlation between the bpMRI score and ISUP grade.

CONCLUSION

A predictive model based on the bpMRI score and clinical parameters significantly improved csPCa risk stratification, and the bpMRI score can be used to determine the aggressiveness of PCa prior to biopsy.

Assessment of complications after transperineal and transrectal prostate biopsy using a risk-stratified pathway identifying patients at risk for post-biopsy infections

INTRODUCTION

Evidence of transperineal (TP) superiority over transrectal (TR) biopsy is growing due to lower infectious complication rates. However, TR biopsy is the most common procedure, and it seems that a cross-over to TP is delayed by logistical challenges such as costs, complexity, and lack of experience. We investigate whether well-selected patients without any risk factors may further undergo TR biopsy if all precautions to avoid infections are warranted.

MATERIALS AND METHODS

Data were collected in our academic institution between August 2021 and March 2022 and after clinical implementation of the currently updated European Association of Urology guideline recommendations on the performance of prostate biopsy. Patients underwent either TP or TR biopsy according to a riskstratification based on risk factors of infectious complications. Follow-up asked for post-biopsy complications. Inverse Probability of Treatment Weighting (IPTW) propensity score was used to balance baseline characteristics. Complications were subdivided into infectious and non-infectious complications.

RESULTS

In total, 294 patients were included with 161 patients undergoing TR vs. 133 patients undergoing TP biopsy. Complication rates were 2.2% for TP vs. 5.5% for TR biopsy concerning all complications. Infectious complication rates only were 0.7% for TP vs. 1.8% for TR biopsy. After IPTW adjustment, differences were statistically significant different (p = 0.01).

CONCLUSION

Our study revealed that even in a well-selected patient cohort with presumably lower risk of infectious complications, TR biopsy leads to more post-biopsy complications than TP biopsy. This conclusion should motivate the urological community to switch to TP biopsy.

External validation of the Rotterdam prostate cancer risk calculator within a high-risk Dutch clinical cohort

PURPOSE

This study aims to externally validate the Rotterdam Prostate Cancer Risk Calculator (RPCRC)-3/4 and RPCRC-MRI within a Dutch clinical cohort.

METHODS

Men subjected to prostate biopsies, between 2018 and 2021, due to a clinical suspicion of prostate cancer (PCa) were retrospectively included. The performance of the RPCRC-3/4 and RPCRC-MRI was analyzed in terms of discrimination, calibration and net benefit. In addition, the need for recalibration and adjustment of risk thresholds for referral was investigated. Clinically significant (cs) PCa was defined as Gleason score ≥ 3 + 4.

RESULTS

A total of 1575 men were included in the analysis. PCa was diagnosed in 63.2% (996/1575) of men and csPCa in 41.7% (656/1575) of men. Use of the RPCRC-3/4 could have prevented 37.3% (587/1575) of all MRIs within this cohort, thereby missing 18.3% (120/656) of csPCa diagnoses. After recalibration and adjustment of risk thresholds to 20% for PCa and 10% for csPCa, use of the recalibrated RPCRC-3/4 could have prevented 15.1% (238/1575) of all MRIs, resulting in 5.3% (35/656) of csPCa diagnoses being missed. The performance of the RPCRC-MRI was good; use of this risk calculator could have prevented 10.7% (169/1575) of all biopsies, resulting in 1.2% (8/656) of csPCa diagnoses being missed.

CONCLUSION

The RPCRC-3/4 underestimates the probability of having csPCa within this Dutch clinical cohort, resulting in significant numbers of csPCa diagnoses being missed. For optimal performance of a risk calculator in a specific cohort, evaluation of its performance within the population under study is essential.

Augmenting prostate magnetic resonance imaging reporting to incorporate diagnostic recommendations based upon clinical risk calculators

Risk calculators have offered a viable tool for clinicians to stratify patients at risk of prostate cancer (PCa) and to mitigate the low sensitivity and specificity of screening prostate specific antigen (PSA). While initially based on clinical and demographic data, incorporation of multiparametric magnetic resonance imaging (MRI) and the validated prostate imaging reporting and data system suspicion scoring system has standardized and improved risk stratification beyond the use of PSA and patient parameters alone. Biopsy-naïve patients with lower risk profiles for harboring clinically significant PCa are often subjected to uncomfortable, invasive, and potentially unnecessary prostate biopsy procedures. Incorporating risk calculator data into prostate MRI reports can broaden the role of radiologists, improve communication with clinicians primarily managing these patients, and help guide clinical care in directing the screening, detection, and risk stratification of PCa.

External Validation of the Prostate Biopsy Collaborative Group Risk Calculator and the Rotterdam Prostate Cancer Risk Calculator in a Swedish Population-based Screening Cohort

Background

External validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed.

Objective

To assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC).

Design, setting, and participants

We used data from the prospective, population-based STHLM3 screening study, performed in 2012–2015. Participants with prostate-specific antigen ≥3 ng/ml who underwent systematic prostate biopsies were included.

Outcome measurements and statistical analysis

Probabilities for clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology grade ≥2, were calculated for each participant. External validity was assessed by calibration, discrimination, and clinical usefulness for both original and recalibrated models.

Results and limitations

Out of 5841 men, 1054 (18%) had csPCa. Distribution of risk predictions differed between RCs; median risks for csPCa using the RPCRC and PBCG-RC were 3.3% (interquartile range [IQR] 2.1–7.1%) and 20% (IQR 15–28%), respectively. The correlation between RC risk estimates on individual level was moderate (Spearman’s r = 0.55). Using the RPCRC’s recommended risk threshold of ≥4% for finding csPCa, 36% of participants would get concordant biopsy recommendations. At 10% risk cut-off, RCs agreed in 23% of cases. Both RCs showed good discrimination, with areas under the curves for the RPCRC of 0.74 (95% confidence interval [CI] 0.72–0.76) and the PBCG-RC of 0.70 (95% CI 0.68–0.72). Calibration was adequate using the PBCG-RC (calibration slope: 1.13 [95% CI 1.03–1.23]), but the RPCRC underestimated the risk of csPCa (calibration slope: 0.73 [0.68–0.79]). The PBCG-RC showed a net benefit in a decision curve analysis, whereas the RPCRC showed no net benefit at clinically relevant risk threshold levels. Recalibration improved clinical benefit, and differences between RCs decreased.

Conclusions

Assessment of calibration is essential to ensure the clinical value of risk prediction tools. The PBCG-RC provided clinical benefit in its current version online. On the contrary, the RPCRC cannot be recommended in this setting.

Patient summary

Predicting the probability of finding prostate cancer on biopsy differed between two assessed risk calculators. After recalibration, the agreement of the models improved, and both were shown to be clinically useful.

Reducing prostate biopsies and magnetic resonance imaging with prostate cancer risk stratification

Objectives

Patients and Methods

Results

Conclusion

Predictors of clinically significant prostate cancer in biopsy-naïve and prior negative biopsy men with a negative prostate MRI: improving MRI-based screening with a novel risk calculator

Purpose:

A pre-biopsy decision aid is needed to counsel men with a clinical suspicion for clinically significant prostate cancer (csPCa), despite normal prostate magnetic resonance imaging (MRI).

Methods:

A risk calculator (RC) for csPCa (International Society of Urological Pathology grade group (ISUP) ⩾ 2) presence in men with a negative-MRI (Prostate Imaging–Reporting and Data System (PI-RADS) ⩽ 2) was developed, and its performance was compared with RCs of the European Randomized Study of Screening for Prostate Cancer (ERSPC), Prostate Biopsy Collaborative Group (PBCG), and Prospective Loyola University mpMRI (PLUM). All biopsy-naïve and prior negative biopsy men with a negative-MRI followed by systematic prostate biopsy were included from October 2015 to September 2021. The RC was developed using multivariable logistic regression with the following parameters: age (years), family history of PCa (first- or second-degree family member), ancestry (African Caribbean/other), digital rectal exam (benign/malignant), MRI field strength (1.5/3.0 Tesla), prior negative biopsy status, and prostate-specific antigen (PSA) density (ng/ml/cc). Performance of RCs was compared using receiver operating characteristic (ROC) curve analysis.

Results:

A total of 232 men were included for analysis, of which 18.1% had csPCa. Parameters associated with csPCa were family history of PCa (p < 0.0001), African Caribbean ancestry (p = 0.005), PSA density (p = 0.002), prior negative biopsy (p = 0.06), and age at biopsy (p = 0.157). The area under the curve (AUC) of the developed RC was 0.76 (95% CI 0.68–0.85). This was significantly better than the RCs of the ERSPC (AUC: 0.59; p = 0.001) and PBCG (AUC: 0.60; p = 0.002), yet similar to PLUM (AUC: 0.69; p = 0.09).

Conclusion:

The developed RC (Prostate Biopsy Cohort Amsterdam (‘PROBA’ RC), integrated predictors for csPCa at prostate biopsy in negative-MRI men and outperformed other widely used RCs. These findings require external validation before introduction in daily practice.

A Prospective Multicenter Comparison Study of Risk-adapted Ultrasound-directed and Magnetic Resonance Imaging-directed Diagnostic Pathways for Suspected Prostate Cancer in Biopsy-naïve Men

BACKGROUND

European Association of Urology guidelines recommend a risk-adjusted biopsy strategy for early detection of prostate cancer in biopsy-naïve men. It remains unclear which strategy is most effective. Therefore, we evaluated two risk assessment pathways commonly used in clinical practice.

OBJECTIVE

To compare the diagnostic performance of a risk-based ultrasound (US)-directed pathway (Rotterdam Prostate Cancer Risk Calculator [RPCRC] #3; US volume assessment) and a magnetic resonance imaging (MRI)-directed pathway.

DESIGN, SETTING, AND PARTICIPANTS

This was a prospective multicenter study (MR-PROPER) with 1:1 allocation among 21 centers (US arm in 11 centers, MRI arm in ten). Biopsy-naïve men with suspicion of prostate cancer (age ≥50 yr, prostate-specific antigen 3.0-50 ng/ml, ± abnormal digital rectal examination) were included.

INTERVENTION

Biopsy-naïve men with elevated risk of prostate cancer, determined using RPCRC#3 in the US arm and Prostate Imaging Reporting and Data System scores of 3-5 in the MRI arm, underwent systematic biopsies (US arm) or targeted biopsies (MRI arm).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was the proportion of men with grade group (GG) ≥2 cancer. Secondary outcomes were the proportions of biopsies avoided and GG 1 cancers detected. Categorical (nonparametric) data were assessed using the Mann-Whitney U test and χ² tests.

RESULTS AND LIMITATIONS

A total of 1965 men were included in the intention-to-treat population (US arm n = 950, MRI arm n = 1015). The US and MRI pathways detected GG ≥2 cancers equally well (235/950, 25% vs 239/1015, 24%; difference 1.2%, 95% confidence interval [CI] -2.6% to 5.0%; p = 0.5). The US pathway detected more GG 1 cancers than the MRI pathway (121/950, 13% vs 84/1015, 8.3%; difference 4.5%, 95% CI 1.8-7.2%; p < 0.01). The US pathway avoided fewer biopsies than the MRI pathway (403/950, 42% vs 559/1015, 55%; difference -13%, 95% CI -17% to -8.3%; p < 0.01). Among men with elevated risk, more GG ≥2 cancers were detected in the MRI group than in the US group (52% vs 43%; difference 9.2%, 95% CI 3.0-15%; p < 0.01).

CONCLUSIONS

Risk-adapted US-directed and MRI-directed pathways detected GG ≥2 cancers equally well. The risk-adapted US-directed pathway performs well for prostate cancer diagnosis if prostate MRI capacity and expertise are not available. If prostate MRI availability is sufficient, risk assessment should preferably be performed using MRI, as this avoids more biopsies and detects fewer cases of GG 1 cancer.

PATIENT SUMMARY

Among men with suspected prostate cancer, relevant cancers were equally well detected by risk-based pathways using either ultrasound or magnetic resonance imaging (MRI) to guide biopsy of the prostate. If prostate MRI availability is sufficient, risk assessment should be performed with MRI to reduce unnecessary biopsies and detect fewer irrelevant cancers.

Reducing Biopsies and Magnetic Resonance Imaging Scans During the Diagnostic Pathway of Prostate Cancer: Applying the Rotterdam Prostate Cancer Risk Calculator to the PRECISION Trial Data

Background

Risk stratification in the diagnostic pathway of prostate cancer (PCa) can be used to reduce biopsies and magnetic resonance imaging (MRI) scans, while maintaining the detection of clinically significant PCa (csPCa). The use of highly discriminating and well-calibrated models will generate better clinical outcomes if context-dependent thresholds are used.

Objective

To retrospectively assess the effect of the upfront use of the Rotterdam Prostate Cancer Risk Calculator (RPCRC) developed in a screening cohort and the RPCRC-MRI developed in a clinical cohort while exploring the need to adapt thresholds in biopsy-naïve men in the PRECISION (Prostate Evaluation for Clinically Important Disease: Sampling Using Image Guidance or Not?) trial.

Design, setting, and participants

In the transrectal ultrasonography arm, we evaluated 188 men; in the MRI arm, we evaluated 206 (for the reduction of MRI scans) and 137 (for the reduction of targeted biopsies) men.

Outcome measurements and statistical analysis

Performance was assessed by discrimination, calibration, and clinical utility.

Results and limitations

The performance of the RPCRC was good. However, intercept adjustment was warranted. Net benefit was observed from a recalibrated probability of 32% for any PCa and 10% for csPCa. After recalibration and applying a threshold of 20% for any PCa or 10% for csPCa, 28% of all biopsies could have been reduced, missing five cases of csPCa. The uncalibrated RPCRC could reduce 35% of all MRI scans, with a threshold of 20% for any PCa or 4% for csPCa. In the MRI arm, performance was good without stressing recalibration. Net benefit was observed from a probability of 22% for any PCa and 7% for csPCa. With a threshold of 20% for any PCa or 4% for csPCa, 9% of all targeted biopsies could be reduced, missing one grade group 2 PCa.

Conclusions

The performance of the RPCRC and RPCRC-MRI in men included in the PRECISION trial was good, but recalibration and adaptation of the risk threshold of the RPCRC are indicated to reach optimal performance.

Patient summary

In this report, we show that risk stratification with the Rotterdam Prostate Cancer Risk Calculator has added value in reducing harm, but adjustment to reflect the characteristics of the patient cohort is indicated.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review

Background

Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients’ individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to better discrimination of patients in need of invasive diagnostic procedures and optimized allocation of health care resources

Objective

The goal of the research was to systematically review available literature on the performance of current prostate cancer risk calculators in healthy populations by comparing the relative impact of individual items on different cohorts and on the models’ overall performance.

Methods

We performed a systematic review of available prostate cancer risk calculators targeted at healthy populations. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese, or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, analyzed calculators, population for which it was calibrated, included risk factors, and the model’s overall accuracy.

Results

We included a total of 18 calculators from 53 different manuscripts. The most commonly analyzed ones were the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study on Prostate Cancer (ERSPC) risk calculators developed from North American and European cohorts, respectively. Both calculators provided high diagnostic ability of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations that reached a diagnostic ability as high as 0.938. The most commonly included risk factors in the calculators were age, prostate specific antigen levels, and digital rectal examination findings. Additional calculators included race and detailed personal and family history.

Conclusions

Both the PCPR and ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic ability. Furthermore, designing calculators from scratch considering each population’s sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which has been calibrated for its intended population and can be easily reproduced and implemented.

Trial Registration

PROSPERO CRD42021242110; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242110

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.

External validation of two MRI-based risk calculators in prostate cancer diagnosis

BACKGROUND

The diagnosis of (significant) prostate cancer ((s)PC) is impeded by overdiagnosis and unnecessary biopsy. Risk calculators (RC) have been developed to mitigate these issues. Contemporary RCs integrate clinical characteristics with mpMRI findings.

OBJECTIVE

To validate two of these models-the MRI-ERSPC-RC-3/4 and the risk model of van Leeuwen.

METHODS

265 men with clinical suspicion of PC were enrolled. Every patient received a prebiopsy mpMRI, which was reported according to PI-RADS v2.1, followed by MRI/TRUS fusion-biopsy. Cancers with ISUP grade ≥ 2 were classified as sPC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Statistical analysis was performed by comparing discrimination, calibration, and clinical utility RESULTS: There was no significant difference in discrimination between the RCs. The MRI-ERSPC-RC-3/4-RC showed a nearly ideal calibration-slope (0.94; 95% CI 0.68-1.20) than the van Leeuwen model (0.70; 95% CI 0.52-0.88). Within a threshold range up to 9% for a sPC, the MRI-ERSPC-RC-3/4-RC shows a greater net benefit than the van Leeuwen model. From 10 to 15%, the van Leeuwen model showed a higher net benefit compared to the MRI-ERSP-3/4-RC. For a risk threshold of 15%, the van Leeuwen model would avoid 24% vs. 14% compared to the MRI-ERSPC-RC-3/4 model; 6% vs. 5% sPC would be overlooked, respectively.

CONCLUSION

Both risk models supply accurate results and reduce the number of biopsies and basically no sPC were overlooked. The van Leeuwen model suggests a better balance between unnecessary biopsies and overlooked sPC at thresholds range of 10-15%. The MRI-ERSPC-RC-3/4 risk model provides better overall calibration.

Detection of Clinically Significant Prostate Cancer by Systematic TRUS-Biopsies in a Population-Based Setting Over a 20 Year Period

OBJECTIVE

To assess the performance of systematic TRUS-biopsies in a population-based setting to detect clinically significant PCa (csPCa) in combination with age, clinical tumor category (cT), and prostate-specific antigen (PSA) in men referred for the first biopsy.

METHODS

We identified all men referred for PCa work-up because of elevated PSA who underwent initial TRUS-biopsies in the nationwide Danish Prostate Cancer Registry (DaPCaR) between January 1st, 1995 and December 31st, 2016, in Denmark. Risk of histologic findings in initial TRUS-biopsies categorized as non-malignant, insignificant PCa, or significant PCa (csPCa). We defined csPCa as any biopsy containing Gleason score 3 + 4 or above as in the PRECISION trial. We assessed risk of csPCa with absolute risk, logistic regression model, and predicted risks.

RESULTS AND LIMITATIONS

After exclusions, our cohort included 39,886 men. The diagnostic hit rate for csPCa was 40.8 %. Men with PSA > 20 ng/mL and ≥cT2 harbor a risk >75% for finding csPCa in the first TRUS biopsy-set. Men with cT1 tumors and PSA < 20 ng/mL have a risk of non-malignant histology of at least 58%. Limitations include the high number of exclusions based on missing information.

CONCLUSION

The diagnostic accuracy of systematic TRUS-biopsies is high for men with palpable tumors and high PSA. Our data point to the fact that not all men need pre-biopsy MRI to find csPCa.

18F-choline PET/computed tomography and clinical parameters in the detection of significant prostate cancer in patients with increased prostate-specific antigen levels and previous negative biopsies

OBJECTIVES

The aim of this study is to assess the value of the F-choline PET/computed tomography (CT) in predicting significant prostate cancer (sPCa) in patients with persistently increased prostate-specific antigen (PSA) levels and previous negative biopsies. To study the possible predictive added value of F-choline PET/CT to clinical variables and biomarkers derived from PSA in detecting sPCa.

METHODS

We evaluated patients who underwent F-choline PET/CT because of ongoing suspicion of prostate cancer (PCa) due to elevated PSA levels (4-20 ng/mL) and at least one previous negative or no conclusive prostate biopsy for PCa. Age, PSA, free PSA, free/total PSA ratio, PSA velocity, PSA doubling time, PSA density and score risk were obtained. F-choline PET/CT was classified as negative/positive (PET-categorical). Additionally, we subclassified F-choline PET/CT according to the radiotracer uptake patterns (PET-pattern). The reference standard was the histological confirmation. Accuracy of PET/CT was evaluated. Univariate and multivariate logistic regression analyses were performed for metabolic and clinical variables.

RESULTS

A total of 78 patients were included in our study, 23 had PCa (15 with sPCa). The PET pattern showed the highest accuracy and was the most powerful predictor of sPCa. In this research, the prediction of sPCa was improved combining PET pattern and score risk.

CONCLUSION

F-choline PET/CT is a potential tool for predicting sPCa in patients with persistently increased PSA levels and previous negative biopsies, and also it could improve the performance of score risk in predicting sPCa.

The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study

Background: To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off points. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer and the nomogram prediction model may help early diagnose the high risk prostate cancer.

Clinically significant Prostate Cancer diagnosed using a urinary molecular biomarker-based risk score: two case reports

BACKGROUND

Identifying men for a repeat prostate biopsy is a conundrum to urologists. Risk calculators (RCs) such as the European Randomized Study of Screening for Prostate Cancer (ERSPC) RCs have been developed to predict the outcome of prostate biopsies and have been shown to improve diagnostic accuracy compared to PSA alone. However, it was recently shown that the outcome for high-grade prostate cancer (PCa) upon biopsy tended to be underestimated in men with previous negative biopsies using ERSPC RC model 4. For these men, an individualized approach combining the clinical information with the outcome of biomarker-related urine tests may help to make a more informed decision.

CASE PRESENTATION

Two men, aged 66 and 69 respectively when presented in the clinic, show the typical dilemma of urologist and patient for electing repeat prostate biopsy. Both men had normal DRE findings, did not have a family history of PCa, presented with serum PSA values between 3 and 10 ng/ml and the first biopsies were negative for disease. The ERSPC RC4 did not indicate a biopsy in these men. The urinary molecular biomarker-based test for HOXC6 and DLX1, combining biomarker-expression profiling with clinical risk factors, resulted in SelectMDx Risk scores for these men that were higher than the cut-off of the test. Based on this outcome, mpMRI was performed with an outcome of PI-RADS ≥4 in both men. Histopathological evaluation of TRUS-guided biopsies confirmed high-grade PCa.

CONCLUSIONS

The urinary molecular biomarker-based risk score played a pivotal role in the diagnosis of clinically significant PCa whereas ERSPC RC4 outcome would not have indicated further diagnostic follow-up in these two cases. The timely diagnosis was shown to be crucial for the curative treatment by radical retropubic prostatectomy and the potential life-years gained for these two vital males.

Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions

Background and purpose

Individualized risk-adapted algorithms in prostate cancer (PCa) diagnosis using predictive prebiopsy variables in addition to prostate-specific antigen value may result in a considerable reduction of unnecessary systematic biopsies. Multi-parametric magnetic resonance imaging (mpMRI) has emerged as a secondary prediction tool that can further improve the detection of clinically significant prostate cancer (csPCa). This review explores the performance of new MRI risk models for indicating a biopsy for prostate cancer diagnosis.

Results and considerations

The area under the receiver-operating characteristic curve for detecting csPCa varies between 0.64 and 0.91 in biopsy-naïve men, and between 0.78 and 0.93 in men with a previous negative biopsy. The utility of multivariate risk prediction tools including MRI suspicion scores as an extra input parameter has the potential to avoid a notable number of biopsies and detection of clinically insignificant PCa at a low price of missing some csPCa. The trade-off depends on the risk threshold that is chosen. In biopsy-naïve men a net benefit was obtained at a risk threshold of above 10% for csPCa in most MRI risk prediction models. All constructed MRI risk models used (referral) patient cohorts with high prevalence of csPCa. Using more representative cohorts from daily clinical screening, net benefit may attenuate at lower risk thresholds. Strengths and limitations of these models are discussed.

Future directions

To assess their wider applicability, in-depth analysis of mpMRI predictive qualities should be further investigated, in combination with required external validation of these models in a multicenter setting with large prospective datasets.

Diagnostic performance of 68Ga-PSMA PET/CT in the detection of prostate cancer prior to initial biopsy: comparison with cancer-predicting nomograms

PURPOSE

To assess the diagnostic performance of ⁶⁸Ga-PSMA PET/CT for detecting suspected prostate cancer (PCa) and to compare it with that of two cancer-predicting nomograms.

METHODS

We performed a retrospective analysis of 146 consecutive patients with suspected PCa based on symptoms or elevated total prostate-specific antigen (tPSA) levels who underwent ⁶⁸Ga-PSMA PET/CT and histopathologic examinations from April 2017 to April 2018 in a large tertiary care hospital in China. The ⁶⁸Ga-PSMA PET/CT results (PCa or benignancy) were evaluated by two experienced nuclear medicine specialists. The risk of positive PCa was evaluated using ERSPC and PCPT nomograms. The diagnostic performances of ⁶⁸Ga-PSMA PET/CT and that of the two nomograms were compared via receiver operating characteristic (ROC) curve analysis, decision curve analysis, and logistic regression.

RESULTS

A total of 58 patients with tPSA of 0.4-50 ng/ml were included in the final analysis; PCa diagnosis was confirmed in 37 patients and excluded in 21 patients. ROC analysis showed that the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ⁶⁸Ga-PSMA PET/CT were 91.67, 81.82, 89.19, and 85.71%, respectively, in per-patient analyses. ⁶⁸Ga-PSMA PET/CT exhibited a higher AUC (0.867) than those of ERSPC-RC3 (0.855) and PCPT-RC (0.770). The net benefit of ⁶⁸Ga-PSMA PET/CT was greatest for patients within threshold probabilities of 15-90%. Among the 58 patients, 11 (19%) biopsies suggested by ERSPC-RC3 were unnecessary and could have been avoided if judged by the ⁶⁸Ga-PSMA PET/CT results. Multivariate analysis revealed that the maximum standardised uptake value (SUV_max) and prostate volume were significant predictive factors for positive PCa results.

CONCLUSION

In suspected PCa patients with tPSA of 0.4-50 ng/ml, ⁶⁸Ga-PSMA PET/CT outperformed the nomograms in predicting cancer and reducing unnecessary biopsies. In addition, the risk of PCa was positively correlated with a higher SUV_max and lower prostate volume, which could help clinicians in making preliminary estimates of individual cancer risk, monitoring ⁶⁸Ga-PSMA PET/CT false-positive results and making biopsy decisions in daily medical practice.

Focal therapy for localized prostate cancer: is there a "middle ground" between active surveillance and definitive treatment?

In recent years, it has come a long way in the diagnosis, treatment, and follow-up of prostate cancer. Beside this, it was argued that definitive treatments could cause overtreatment, particularly in the very low, low, and favorable risk group. When alternative treatment and follow-up methods are being considered for this group of patients, active surveillance is seen as a good alternative for patients with very low and low-risk groups in this era. However, it has become necessary to find other alternatives for patients in the favorable risk group or patients who cannot adopt active follow-up. In the light of technological developments, the concept of focal therapy was introduced with the intensification of research to treat only the lesioned area instead of treating the entire organ for prostate lesions though there are not many publications about many of them yet. According to the initial results, it was understood that the results could be good if the appropriate focal therapy technique was applied to the appropriate patient. Thus, focal therapies have begun to find their "middle ground" place between definitive therapies and active follow-up.

Prediction of High-grade Prostate Cancer Following Multiparametric Magnetic Resonance Imaging: Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculators

BACKGROUND

The Rotterdam European Randomized Study of Screening for Prostate Cancer risk calculators (ERSPC-RCs) help to avoid unnecessary transrectal ultrasound-guided systematic biopsies (TRUS-Bx). Multivariable risk stratification could also avoid unnecessary biopsies following multiparametric magnetic resonance imaging (mpMRI).

OBJECTIVE

To construct MRI-ERSPC-RCs for the prediction of any- and high-grade (Gleason score ≥3 + 4) prostate cancer (PCa) in 12-core TRUS-Bx±MRI-targeted biopsy (MRI-TBx) by adding Prostate Imaging Reporting and Data System (PI-RADS) and age as parameters to the ERSPC-RC3 (biopsy-naïve men) and ERSPC-RC4 (previously biopsied men).

DESIGN, SETTING, AND PARTICIPANTS

A total of 961 men received mpMRI and 12-core TRUS-Bx±MRI-TBx (in case of PI-RADS ≥3) in five institutions. Data of 504 biopsy-naïve and 457 previously biopsied men were used to adjust the ERSPC-RC3 and ERSPC-RC4.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Logistic regression models were constructed. The areas under the curve (AUCs) of the original ERSPC-RCs and MRI-ERSPC-RCs (including PI-RADS and age) for any- and high-grade PCa were compared. Decision curve analysis was performed to assess the clinical utility of the MRI-ERSPC-RCs.

RESULTS AND LIMITATIONS

MRI-ERSPC-RC3 had a significantly higher AUC for high-grade PCa compared with the ERSPC-RC3: 0.84 (95% confidence interval [CI] 0.81-0.88) versus 0.76 (95% CI 0.71-0.80, p<0.01). Similarly, MRI-ERSPC-RC4 had a higher AUC for high-grade PCa compared with the ERSPC-RC4: 0.85 (95% CI 0.81-0.89) versus 0.74 (95% CI 0.69-0.79, p<0.01). Unlike for the MRI-ERSPC-RC3, decision curve analysis showed clear net benefit of the MRI-ERSPC-RC4 at a high-grade PCa risk threshold of ≥5%. Using a ≥10% high-grade PCa risk threshold to biopsy for the MRI-ERSPC-RC4, 36% biopsies are saved, missing low- and high-grade PCa, respectively, in 15% and 4% of men who are not biopsied.

CONCLUSIONS

We adjusted the ERSPC-RCs for the prediction of any- and high-grade PCa in 12-core TRUS-Bx±MRI-TBx. Although the ability of the MRI-ERSPC-RC3 for biopsy-naïve men to avoid biopsies remains questionable, application of the MRI-ERSPC-RC4 in previously biopsied men in our cohort would have avoided 36% of biopsies, missing high-grade PCa in 4% of men who would not have received a biopsy.

PATIENT SUMMARY

We have constructed magnetic resonance imaging-based Rotterdam European Randomized study of Screening for Prostate Cancer (MRI-ERSPC) risk calculators for prostate cancer prediction in transrectal ultrasound-guided biopsy and MRI-targeted biopsy by incorporating age and Prostate Imaging Reporting and Data System score into the original ERSPC risk calculators. The MRI-ERSPC risk calculator for previously biopsied men could be used to avoid one-third of biopsies following MRI.

Multivariable risk-based patient selection for prostate biopsy in a primary health care setting: referral rate and biopsy results from a urology outpatient clinic

Background

According to their guidelines, Dutch general practitioners (GPs) refer men with prostate-specific antigen (PSA) level ≥3.0 ng/mL to the urologist for risk-based patient selection for prostate biopsy using the Rotterdam Prostate Cancer Risk Calculator (RPCRC). Use of the RPCRC in primary care could optimize the diagnostic pathway even further by reducing unnecessary referrals. To investigate this, we calculated the risk and assessed the rate of men referred to the urologist with PSA level ≥3.0 ng/mL by implementing the RPCRC in a primary health care setting.

Methods

In January 2014, an exploratory study was initiated in collaboration with the primary health care facility of the GP laboratory in Rotterdam. GPs were given the possibility to refer men with a suspicion of prostate cancer (PCa) or a screening wish to this primary care facility (STAR-SHL) where further assessment was performed by specially trained personnel. Risk-based advice on referral to the urologist was given to the GP on the basis of the RPCRC results. If requested, advice on the treatment of lower urinary tract symptoms (LUTS) was provided. All men signed informed consent.

Results

Between January 2014 and September 2017, a total of 243 men, median age 64 [interquartile range (IQR), 57-70] years were referred for a consultation at the primary care facility. Of the 108 men with PSA level ≥3.0 ng/mL and a referral related to PCa, GPs were advised to refer 58 men to the urologist (54%). Of the men with available follow-up (FU) data [n=187, median FU, 16 (IQR, 9-25) months] 54 men were considered high-risk (i.e., had an elevated risk of PCa as calculated by the RPCRC). Of these men, 51 (94%) were actually referred to secondary care by their GP, and so far 38 men underwent biopsy. PCa was detected in 30 men [47% had Gleason score (GS) ≥3+4 PCa], translating to an overall positive predictive value (PPV) of 79%. Within the available FU time, 2 out of 38 (5%) men with PSA level ≥3.0 ng/mL which were considered low-risk have been diagnosed with GS 3+3 PCa.

Conclusions

Risk-stratification with the RPCRC in a primary health care setting could prevent almost half of referrals of men with PSA level ≥3.0 ng/mL to the urologist. In more than three-quarters of men referred for prostate biopsy, the suspicion of PCa was confirmed and almost half of men had clinically significant PCa (GS ≥3+4 PCa). These data show a huge potential for multivariable risk-stratification in primary care.

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

BACKGROUND

The survival rate for men with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa) without invasive cribriform (CR) and intraductal carcinoma (IDC) is similar to that for ISUP grade 1. If updated into the European Randomized Study of Screening for Prostate Cancer (ERSPC Rotterdam) risk calculator number 3 (RC3), this may further improve upfront selection of men who need a biopsy.

OBJECTIVE

To improve the number of possible biopsies avoided, while limiting undiagnosed clinically important PCa by applying the updated RC3 for risk-based patient selection.

DESIGN, SETTING, AND PARTICIPANTS

The RC3 is based on the first screening round of the ERSPC Rotterdam, which involved 3616 men. In 2015, histopathologic slides for PCa cases (n=885) were re-evaluated. Low-risk (LR) PCa was defined as ISUP grade 1 or 2 without CR/IDC. High-risk (HR) PCa was defined as ISUP grade 2 with CR/IDC and PCa with ISUP grade≥3.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We updated the RC3 using multinomial logistic regression analysis, including data on age, PSA, digital rectal examination, and prostate volume, for predicting LR and HR PCa. Predictive accuracy was quantified using receiver operating characteristic analysis and decision curve analysis.

RESULTS AND LIMITATIONS

Men without PCa could effectively be distinguished from men with LR PCa and HR PCa (area under the curve 0.70, 95% confidence interval [CI] 0.68-0.72 and 0.92, 95% CI 0.90-0.94). At a 1% risk threshold, the updated calculator would lead to a 34% reduction in unnecessary biopsies, while only 2% of HR PCa cases would be undiagnosed.

CONCLUSIONS

A relatively simple risk stratification tool augmented with a highly sensitive contemporary pathologic biopsy classification would result in a considerable decrease in unnecessary prostate biopsies and overdiagnosis of potentially indolent disease.

PATIENT SUMMARY

We improved a well-known prostate risk calculator with a new pathology classification system that better reflects disease burden. This new risk calculator allows individualized prediction of the chance of having (potentially aggressive) biopsy-detectable prostate cancer and can guide shared decision-making when considering prostate biopsy.