Prostate Biopsy Collaborative Group Research Articles

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

ObjectiveTo 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 methodsRetrospectively, 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. Results250 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). ConclusionIn 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.

Comparación entre las calculadoras de riesgo del European Randomized Study for Screening of Prostate Cancer (ERSPC) y Prostate Biopsy Collaborative Group (PBCG): predicción del riesgo de cáncer de próstata clínicamente significativo en una cohorte de pacientes de Argentina

ObjectiveTo 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 methodsRetrospectively, 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. Results250 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). ConclusionIn 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.

Clinical analysis of optimized neural network risk models to predict clinically significant prostate cancer and avoid unnecessary prostate biopsies.

5023 Background: Given the low specificity of the current standard of care diagnostic tests for prostate cancer (PCa), there is an unmet clinical need for higher specificity tests to counter overdiagnosis of grade group (GG) 1 PCa. The aim of the study was to create optimized neural network risk models using PSA, free PSA, and other useful clinical features, and to validate the accuracy of the risk models to predict GG ≥2 PCa using real-world samples and data. Methods: Men aged 40-75 years with PSA >=3ng/mL and a biopsy referral were recruited into cohorts from sites in Canada (Kipnes Urology Centre (KUC), Edmonton, AB, and Prostate Cancer Centre (PCC), Calgary, AB) and the United States (Johns Hopkins University (JHU), Baltimore, ML and UCLA, Los Angeles, CA). Risk models to predict all GGs or GG ≥2 PCa were fit using data from KUC and JHU (train cohort n = 1037) while fixed models were validated on PCC (n = 401) and UCLA (n = 945). Prediction models were created using neural networks to generate the patient’s risk score. To compare the risk model test with PSA, the high-grade cancer detection sensitivity was fixed, and the number of biopsies needed to achieve that sensitivity was evaluated. Threshold values for the training cohort were determined using at least 95% sensitivity and maximum specificity. Threshold values for other clinical features and risk calculator outputs were set to match the test sensitivity when possible. Results: The optimized neural network risk models test had the highest area under the curve (AUC 0.81) for predicting GG ≥2 PCa on the validation cohorts compared to four other risk calculators; Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC) with free PSA (0.78, p-value<0.001), Prostate Biopsy Collaborative Group risk calculator (PBCG, 0.73, p-value<0.0001), European Randomized study of Screening for Prostate Cancer risk calculator 3 (ERSPC-3, 0.72, p-value<0.0001), and PCPTRC with no free PSA (0.71, p-value<0.0001) and PSA (0.66, p-value<0.0001). At a threshold of 18.6%, this test provided 94% sensitivity, 37% specificity, 49% positive predictive value, and 90% NPV for predicting GG ≥2 PCa. If a biopsy was performed only when the tests’ risk score prediction was ≥17.8% for GG ≥2 PCa, 36-44% of unnecessary prostate biopsies could be avoided while missing 4-12% of patients with GG ≥2 prostate cancer. Conclusions: This neural network risk model is a tool that can be used to inform the physician of a patient’s risk of having GG ≥2 PCa on prostate biopsy. Using this novel test in the clinic is expected to significantly reduce the number and burden of unnecessary prostate biopsies. [Table: see text]

MP40-19 WEST-AFRICAN ANCESTRY CAN AID PREDICTION OF HIGH-GRADE PROSTATE CANCER IN BLACK-TAILORED RISK CALCULATOR

You have accessJournal of UrologyCME1 Apr 2023MP40-19 WEST-AFRICAN ANCESTRY CAN AID PREDICTION OF HIGH-GRADE PROSTATE CANCER IN BLACK-TAILORED RISK CALCULATOR Neil Mistry, Zequn Sun, Norma Marshall, Rick Kittles, Edward Schaeffer, Peter Gann, and Adam Murphy Neil MistryNeil Mistry More articles by this author , Zequn SunZequn Sun More articles by this author , Norma MarshallNorma Marshall More articles by this author , Rick KittlesRick Kittles More articles by this author , Edward SchaefferEdward Schaeffer More articles by this author , Peter GannPeter Gann More articles by this author , and Adam MurphyAdam Murphy More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003278.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The Kaiser Permanent Prostate Cancer (KPPC), Prostate Cancer Prevention Trial (PCPT) and Prostate Biopsy Collaborative Group (PBCG) risk calculators (RCs) improve the detection of high-grade, Gleason Grade Group ≥2 prostate cancer (HGPCa) but result in excessive biopsies in Black men. Genetic West African Ancestry (WA) is associated with increased risk of HGPCa, but its impact on RC accuracy is unknown. We aimed to validate a tailored RC to improve accuracy and assess if WA can augment accuracy. METHODS: We recruited men undergoing prostate biopsy from 5 Chicago clinics from 2009-2014. We split the sample into development (75%) and validation (25%) cohorts. A second cohort from the same sites from 2016-2019 provided external validation. Exclusion criteria were non-Black race and PSA >50 ng/ml. We created 2 binary logistic regressions to predict HGPCa using clinical variables alone and including WA. Model accuracy was assessed using area under curve (AUC). RESULTS: 393 Black men were included for development/internal validation, and 354 for external validation. Table 1 shows model performance and comparisons to prior RCs. CONCLUSIONS: A tailored RC improves HGPCa prediction in Blacks compared to popular RCs. Including genetic ancestry as a risk factor alongside clinical variables may help alleviate PCa disparities faced by Black men. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e553 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Neil Mistry More articles by this author Zequn Sun More articles by this author Norma Marshall More articles by this author Rick Kittles More articles by this author Edward Schaeffer More articles by this author Peter Gann More articles by this author Adam Murphy More articles by this author Expand All Advertisement PDF downloadLoading ...

Active Data Science for Improving Clinical Risk Prediction

Clinical risk prediction models are commonly developed in a post-hoc and passive fashion, capitalizing on convenient data from completed clinical trials or retrospective cohorts. Impacts of the models often end at their publication rather than with the patients. The field of clinical risk prediction is rapidly improving in a progressively more transparent data science era. Based on collective experience over the past decade by the Prostate Biopsy Collaborative Group (PBCG), this paper proposes the following four data science-driven strategies for improving clinical risk prediction to the benefit of clinical practice and research. The first proposed strategy is to actively design prospective data collection, monitoring, analysis and validation of risk tools following the same standards as for clinical trials in order to elevate the quality of training data. The second suggestion is to make risk tools and model formulas available online. User-friendly risk tools will bring quantitative information to patients and their clinicians for improved knowledge-based decision-making. As past experience testifies, online tools expedite independent validation, providing helpful information as to whether the tools are generalizable to new populations. The third proposal is to dynamically update and localize risk tools to adapt to changing demographic and clinical landscapes. The fourth strategy is to accommodate systematic missing data patterns across cohorts in order to maximize the statistical power in model training, as well as to accommodate missing information on the end-user side too, in order to maximize utility for the public.

Prostate-specific Antigen Density Cutoff of 0.15 ng/ml/cc to Propose Prostate Biopsies to Patients with Negative Magnetic Resonance Imaging: Efficient Threshold or Legacy of the Past?

BackgroundA prostate-specific antigen density (PSAd) cutoff of 0.15 ng/ml/cc is a commonly recommended threshold to identify patients with negative prostate magnetic resonance imaging (MRI) who should proceed to a prostate biopsy. We were unable to find any study that explicitly examined the properties of this threshold compared with others. ObjectiveTo investigate whether the 0.15 cutoff is justified for selecting patients at risk of harboring high-grade cancer (Gleason score ≥3 + 4) despite negative MRI. Design, setting, and participantsA cohort of 8974 prostate biopsies provided by the Prostate Biopsy Collaborative Group (PBCG) was included in the study. Outcome measurements and statistical analysisLocally weighted scatterplot smoothing was used to investigate whether there was a change in the risk of high-grade cancer around this value. We examined whether the use of this cutoff in patients with negative MRI corresponds to a reasonable threshold probability for a biopsy (defined as a 10% risk of high-grade disease). To do so, we applied the negative likelihood ratio of MRI, calculated from eight studies on prostate MRI, to the risk curve derived from the PBCG. Results and limitationsThere was no discontinuity in the risk of high-grade prostate cancer at a PSAd cutoff of 0.15. This cutoff corresponded to a probability of high-grade disease ranging from 2.6% to 10%, depending on MRI accuracy. Using 10% as threshold probability, the corresponding PSAd cutoff varied between 0.15 and 0.38, with the threshold increasing for greater MRI accuracy. Possible limitations include difference between studies on MRI and the use of ultrasound to measure prostate volume. ConclusionsThe 0.15 cutoff to recommend prostate biopsies in patients with negative MRI is justified only under an extreme scenario of poor MRI properties. We recommend a value of at least ≥0.20. Our results suggest the need for future studies to look at how to best identify patients who need prostate biopsies despite negative MRI, likely by using individualized risk prediction. Patient summaryIn this study, we investigated whether the commonly used prostate-specific antigen density cutoff of 0.15 is justified to identify patients with negative magnetic resonance imaging (MRI) who should proceed to a prostate biopsy. We found that this cutoff is appropriate only in case of very poor MRI quality, and a higher cutoff (≥0.20) should be used for the average MRI.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.MethodsTen North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.ResultsAmong 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.ConclusionDevelopers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators.

To develop and validate a prostate cancer (PCa) risk calculator (RC) incorporating multiparametric magnetic resonance imaging (mpMRI) and to compare its performance with that of the Prostate Biopsy Collaborative Group (PBCG) RC. Men without a PCa diagnosis receiving mpMRI before biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015-2020) were included. Data from a separate institution were used for external validation. The primary outcome was diagnosis of no cancer, grade group (GG)1 PCa, and clinically significant (cs)PCa (≥GG2). Binary logistic regression was used to explore standard clinical and mpMRI variables (prostate volume, Prostate Imaging-Reporting Data System [PI-RADS] version 2.0 lesions) with the final PLUM RC, based on a multinomial logistic regression model. Receiver-operating characteristic curve, calibration curves, and decision-curve analysis were evaluated in the training and validation cohorts. A total of 1010 patients were included for development (N = 674 training [47.8% PCa, 30.9% csPCa], N = 336 internal validation) and 371 for external validation. The PLUM RC outperformed the PBCG RC in the training (area under the curve [AUC] 85.9% vs 66.0%; P < 0.001), internal validation (AUC 88.2% vs 67.8%; P < 0.001) and external validation (AUC 83.9% vs 69.4%; P < 0.001) cohorts for csPCa detection. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a threshold probability of 15%, the PLUM RC vs the PBCG RC could avoid 13.8 vs 2.7 biopsies per 100 patients without missing any csPCa. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for the PBCG RC. The PLUM RC compared favourably with the Stanford Prostate Cancer Calculator (SPCC; AUC 84.1% vs 81.1%; P = 0.002) and the MRI-European Randomized Study of Screening for Prostate Cancer (ERSPC) RC (AUC 84.5% vs 82.6%; P = 0.05). The mpMRI-based PLUM RC significantly outperformed the PBCG RC and compared favourably with other mpMRI-based RCs. A large proportion of biopsies could be avoided using the PLUM RC in shared decision making while maintaining optimal detection of csPCa.

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

BackgroundExternal validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed. ObjectiveTo assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC). Design, setting, and participantsWe 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 analysisProbabilities 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 limitationsOut 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. ConclusionsAssessment 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 summaryPredicting 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.

Integrating Information from Existing Risk Prediction Models with No Model Details.

Consider the setting where (i) individual-level data are collected to build a regression model for the association between an event of interest and certain covariates, and (ii) some risk calculators predicting the risk of the event using less detailed covariates are available, possibly as algorithmic black boxes with little information available about how they were built. We propose a general empirical-likelihood-based framework to integrate the rich auxiliary information contained in the calculators into fitting the regression model, to make the estimation of regression parameters more efficient. Two methods are developed, one using working models to extract the calculator information and one making a direct use of calculator predictions without working models. Theoretical and numerical investigations show that the calculator information can substantially reduce the variance of regression parameter estimation. As an application, we study the dependence of the risk of high grade prostate cancer on both conventional risk factors and newly identified molecular biomarkers by integrating information from the Prostate Biopsy Collaborative Group (PBCG) risk calculator, which was built based on conventional risk factors alone.

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.

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 0.3-50ng/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.

P011 - External validation of the Rotterdam Prostate Cancer Risk Calculator and the Prostate Biopsy Collaborative Group Risk Calculator in a Swedish population-based screening cohort

P011 - External validation of the Rotterdam Prostate Cancer Risk Calculator and the Prostate Biopsy Collaborative Group Risk Calculator in a Swedish population-based screening cohort

Improved predictive performance of prostate biopsy collaborative group risk calculator when based on automated machine learning

BackgroundThe Prostate Biopsy Collaborative Group risk calculator (PBCG RC) has a moderate discriminatory capability. This study aimed to create automated machine learning (AutoML) PBCG RC for predicting the probability of any-grade and high-grade prostate cancer (PCa). MethodsThis retrospective, single-center study was carried out using the database with 832 patients who were subject to transrectal ultrasound-guided prostate biopsy with prostate-specific antigen (PSA) values from 2 to 50 ng/ml. Information about PBCG RC predictors was gathered for all patients. We used H2O, as an open-source platform for AutoML, where the set of 20 base learning algorithms were trained. The AutoML PBCG RC was compared in terms of discrimination, calibration, and clinical utility with the original PBCG RC. ResultsPCa was detected in 341 (41%) men, and 159 (19.1%) of them had high-grade PCa. Our AutoML models demonstrated better discriminative ability than the original PBCG RC for detection of PCa (area under the curve [AUC]: 0.703 vs 0.628; P = 0.023) and high-grade PCa (AUC: 0.990 vs 0.717; P < 0.001). The decision curve analyses showed that AutoML models performed better. For high-grade PCa the PSA was the most important feature. ConclusionsWe applied ensemble techniques to create a freely available online PCa risk tool based on PBCG RC predictors and AutoML algorithms. The AutoML models drastically improved original model performance and the predictions of high-grade PCa were nearly perfect. However, new models should be used with a reserve, because external validation has not been performed yet.

MP43-01 A PROSTATE BIOPSY RISK CALCULATOR BASED ON MAGNETIC RESONANCE IMAGING: DEVELOPMENT, VALIDATION, AND COMPARISON TO THE PBCG RISK CALCULATOR

You have accessJournal of UrologyProstate Cancer: Detection & Screening IV (MP43)1 Sep 2021MP43-01 A PROSTATE BIOPSY RISK CALCULATOR BASED ON MAGNETIC RESONANCE IMAGING: DEVELOPMENT, VALIDATION, AND COMPARISON TO THE PBCG RISK CALCULATOR Hiten D. Patel, Elizabeth L. Koehne, Steven M. Shea, Andrew Fang, Marielia Gerena, Alex Gorbonos, Marcus L. Quek, Robert C. Flanigan, Ari Goldberg, Soroush Rais-Bahrami, and Gopal N. Gupta Hiten D. PatelHiten D. Patel More articles by this author , Elizabeth L. KoehneElizabeth L. Koehne More articles by this author , Steven M. SheaSteven M. Shea More articles by this author , Andrew FangAndrew Fang More articles by this author , Marielia GerenaMarielia Gerena More articles by this author , Alex GorbonosAlex Gorbonos More articles by this author , Marcus L. QuekMarcus L. Quek More articles by this author , Robert C. FlaniganRobert C. Flanigan More articles by this author , Ari GoldbergAri Goldberg More articles by this author , Soroush Rais-BahramiSoroush Rais-Bahrami More articles by this author , and Gopal N. GuptaGopal N. Gupta More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002064.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Implementation of multiparametric MRI (mpMRI) prior to prostate biopsy has improved detection of clinically significant prostate cancer (csPCa) through use of mpMRI–transrectal ultrasound fusion-guided biopsies, but imaging findings are not considered in traditional prediction tools. Therefore, we aimed to develop, validate, and compare a PCa risk calculator (RC) incorporating mpMRI findings to the Prostate Biopsy Collaborative Group (PBCG) RC. METHODS: Patients without a diagnosis of PCa receiving mpMRI prior to prostate biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015-2020) were included for training and internal validation. Data from a separate institution served as external validation. Clinical variables included age, race, family history, digital rectal examination, prior negative biopsy, PSA, mpMRI prostate volume, and presence of any PI-RADS v2 mpMRI lesion. PSA density (PSAD) was based on mpMRI volume. A multinomial logistic regression model was constructed with outcomes of no cancer, clinically insignificant PCa (Gleason 3+3), and csPCa (Gleason ≥3+4). Receiver operating characteristics and calibration curves were evaluated. Decision curve analysis (DCA) was conducted. RESULTS: A total of 1,010 patients were separated into training (N=674) and validation (N=336) cohorts with a separate population for external validation (N=371). In the training cohort, 322 (47.8%) were diagnosed with PCa and 208 (30.9%) represented csPCa. The PLUM model included standard clinical variables with addition of log-transformed PSAD, mpMRI volume, and PI-RADS score. The multinomial PLUM RC outperformed the PBCG RC in the training (AUC: 0.859 vs. 0.660,p<0.001), internal validation (AUC: 0.882 vs. 0.678,p<0.001), and external validation (AUC: 0.839 vs. 0.694,p<0.001) samples for detection of csPCa. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for PBCG. On DCA at a threshold probability of 15%, PLUM was equivalent to a strategy of avoiding 13.8 biopsies per 100 without missing any csPCa compared to 2.7 biopsies per 100 for PBCG. CONCLUSIONS: Addition of mpMRI findings to standard clinical variables significantly improved the ability of the PLUM RC to detect PCa and csPCa compared to the PBCG RC. A large proportion of biopsies could be avoided using the PLUM RC in shared decision-making to inform the decision for prostate biopsy. Source of Funding: Efforts to support data extraction and maintenance of The Prospective Loyola University mpMRI Prostate Biopsy Cohort database is supported by funding from Siemens Medical Solutions USA, Inc. This work was funded in part by Junior Faculty Development Grant (ACS-IRG 001-53) and by Developmental funds from the UAB Comprehensive Cancer Center Support Grant (NCI P30 CA 013148) granted to Soroush Rais-Bahrami © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e781-e781 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hiten D. Patel More articles by this author Elizabeth L. Koehne More articles by this author Steven M. Shea More articles by this author Andrew Fang More articles by this author Marielia Gerena More articles by this author Alex Gorbonos More articles by this author Marcus L. Quek More articles by this author Robert C. Flanigan More articles by this author Ari Goldberg More articles by this author Soroush Rais-Bahrami More articles by this author Gopal N. Gupta More articles by this author Expand All Advertisement Loading ...

MP30-13 DIAGNOSTIC PERFORMANCE OF SYSTEMATIC FREEHAND TRANSPERINEAL PROSTATE BIOPSY: COMPARISON OF OUTCOMES TO PBCG NOMOGRAM PREDICTIONS OBSERVED OUTCOMES TO PBCG NOMOGRAM PREDICTIONS

You have accessJournal of UrologyProstate Cancer: Detection & Screening III (MP30)1 Sep 2021MP30-13 DIAGNOSTIC PERFORMANCE OF SYSTEMATIC FREEHAND TRANSPERINEAL PROSTATE BIOPSY: COMPARISON OF OUTCOMES TO PBCG NOMOGRAM PREDICTIONS OBSERVED OUTCOMES TO PBCG NOMOGRAM PREDICTIONS Shaan Setia, Jamaal Jackson, Demetri Cendo, Michael Gorin, Matthew Allaway, and Srinivas Vourganti Shaan SetiaShaan Setia More articles by this author , Jamaal JacksonJamaal Jackson More articles by this author , Demetri CendoDemetri Cendo More articles by this author , Michael GorinMichael Gorin More articles by this author , Matthew AllawayMatthew Allaway More articles by this author , and Srinivas VourgantiSrinivas Vourganti More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002027.13AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We assessed the diagnostic performance of freehand systematic transperineal biopsy (fTPb) by using the Prostate Biopsy Collaborative Group (PBCG) nomogram as a comparator. METHODS: From 1/2012 to 12/2018, fTPb was performed on consecutive men with clinical suspicion of prostate cancer. Patients included in this study had no prior diagnosis of prostate cancer, PSA between 2.5 to 20 ng/ml, and underwent ≥12 core biopsies. Those men who underwent pre-biopsy MRI were considered separately from those without prebiopsy imaging. Biopsies were performed by a single urologist who developed the needle guidance device used in the procedure. Clinical and pathological data were collected retrospectively. We compared observed biopsy outcomes with those predicted by PBCG nomogram utilizing chi-square statistical analysis. RESULTS: Systematic fTPb (without pre-biopsy MRI) was performed in 301 men (median age 67, mean PSA 6.9 ng/mL). These men had a median of 20 biopsy cores. Clinically significant cancer (ISUP 2 or greater) was found in 33.9% of men. In men without pre-biopsy MRI, using PBCG Nomogram, we found no significant difference between the expected and observed number of clinically significant cancer (96 vs 102; p=0.09). An additional 73 men (median age 67, mean PSA 7.8 ng/ml) underwent pre-biopsy MRI imaging. The addition of MRI targets to systematic sampling resulted in a median of 25 cores. Clinically significant cancer was found in 49.3%. Using the PBCG Nomogram, in the men with pre-biopsy MRI we found clinically significant cancer in significantly more men than was expected by PBCG nomogram predictions (36 vs 20; p≤0.01). There were no biopsy-related infectious complications. CONCLUSIONS: The fTPb technique is a promising method to sample the prostate which provides comparable cancer detection to systematic TRUS biopsy. We found that pre-biopsy mpMRI resulted in greater than expected detection of clinically significant cancer when utilizing this technique. Source of Funding: none © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e504-e505 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Shaan Setia More articles by this author Jamaal Jackson More articles by this author Demetri Cendo More articles by this author Michael Gorin More articles by this author Matthew Allaway More articles by this author Srinivas Vourganti More articles by this author Expand All Advertisement Loading ...

MP30-14 5-ARI USAGE ASSOCIATED WITH MORE ADVANCED PROSTATE CANCER AT DIAGNOSIS

You have accessJournal of UrologyProstate Cancer: Detection & Screening III (MP30)1 Sep 2021MP30-14 5-ARI USAGE ASSOCIATED WITH MORE ADVANCED PROSTATE CANCER AT DIAGNOSIS Emily Roebuck, Wei Sha, Caroline Lu, Caroline Miller, Earle F Burgess, Claud M Grigg, Jason Zhu, Kris E Gaston, Stephen B Riggs, Justin T Matulay, Peter E Clark, and James T Kearns Emily RoebuckEmily Roebuck More articles by this author , Wei ShaWei Sha More articles by this author , Caroline LuCaroline Lu More articles by this author , Caroline MillerCaroline Miller More articles by this author , Earle F BurgessEarle F Burgess More articles by this author , Claud M GriggClaud M Grigg More articles by this author , Jason ZhuJason Zhu More articles by this author , Kris E GastonKris E Gaston More articles by this author , Stephen B RiggsStephen B Riggs More articles by this author , Justin T MatulayJustin T Matulay More articles by this author , Peter E ClarkPeter E Clark More articles by this author , and James T KearnsJames T Kearns More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002027.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer (PCa) risk is often modeled at referral based on Prostate Cancer Prevention Trial (PCPT) and Prostate Biopsy Collaborative Group (PBCG) risk calculators, which account for factors such as PSA, DRE, age, race, family history, and prior biopsy history. Prospective PCa chemoprevention trials noted an increased risk of high-grade prostate cancer among men using 5-ARIs, and subsequent retrospective data associates 5-ARI use with delayed diagnoses and worse cancer outcomes than non-users. The objective of this study was to assess clinical characteristics and treatment types between 5-ARI users versus non-users at prostate biopsy and PCa diagnosis. METHODS: Men undergoing prostate biopsy at our institution between January 2018–December 2021 were retrospectively reviewed for clinical and demographic data. Patients with previously diagnosed PCa were excluded. For categorical demographic or biopsy variables, Chi-square test was used to determine if there is a significant association between these variables and 5-ARI usage. For continuous demographic variables, quantile regression was used to compare the medians between 5-ARI users and non-users. False discovery rate (FDR) adjusted P value was used for multiple tests. RESULTS: Among 842 patients, 83 men were using 5-ARIs at time of biopsy (Table 1). Demographic characteristics including age, race, marital status, BMI, or family history were similar across the two cohorts. PSA was greater among 5-ARI users (p=.054). At PCa diagnosis, 5-ARI users tended to have higher clinical T stage (p<0.0001) and NCCN risk group stratification (p=.0119). Gleason Grade Group (p=0.372), prostate volume (p=.1838), DRE (p=.5849), and treatment type (p>.05 for all) were not statistically different between users and non-users. CONCLUSIONS: While not emphasized in common risk calculators, pre-diagnostic use of 5-ARI should be considered when deciding to biopsy. Our findings demonstrate 5-ARI usage associated with more advanced clinical T stage and higher NCCN risk though not Gleason Grade, suggesting that 5-ARI is a risk factor to consider at referral and highlighting the need to optimize diagnostic care for 5-ARI users. Source of Funding: No source of funding © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e505-e505 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Emily Roebuck More articles by this author Wei Sha More articles by this author Caroline Lu More articles by this author Caroline Miller More articles by this author Earle F Burgess More articles by this author Claud M Grigg More articles by this author Jason Zhu More articles by this author Kris E Gaston More articles by this author Stephen B Riggs More articles by this author Justin T Matulay More articles by this author Peter E Clark More articles by this author James T Kearns More articles by this author Expand All Advertisement Loading ...

Clash of the calculators: External validation of prostate cancer risk calculators in men undergoing mpMRI and transperineal biopsy.

ObjectiveTo compare the accuracy of the European Randomized Study of Screening for Prostate Cancer (ERSPC) RC, MRI‐ERSPC‐RC, and Prostate Biopsy Collaborative Group (PBCG) RC in patients undergoing transperineal prostate biopsy.Patients and methodsWe identified 392 patients who underwent mpMRI before transperineal prostate biopsy across multiple public and private institutions between January 2017 and August 2019. The estimated probabilities of detecting PCa and significant PCa were calculated using the MRI‐ERSPC‐RC, ERSPC‐RC, and PBCG‐RC. Receiver operating characteristic (ROC) curves for each calculator were generated and the area underneath the curve (AUC) was compared. Calibration and clinical utility were assessed with calibration plots and decision curve analysis, respectively.ResultsPCa was detected in 285 patients (72.7%) with significant PCa found in 200 patients (51.1%). ROC curve analysis found the MRI‐ERSPC‐RC outperformed the ERSPC‐RC and PBCG‐RC. For the prediction of PCa, the AUC was 0.756, 0.696, and 0.675 for the MRI‐ERSPC‐RC, ERSPC‐RC, and PBCG‐RC, respectively. The AUC for the prediction of significant PCa was 0.803, 0.745, and 0.746 for the MRI‐ERSPC‐RC, ERSPC‐RC, and PBCG‐RC, respectively.ConclusionsOur study validated the ERSPC‐RC, MRI‐ERSPC‐RC, and PBCG‐RC in a cohort undergoing transperineal prostate biopsy with the MRI‐ERSPC‐RC performing the best. These RCs may enable improved shared decision making and help to guide patient selection for biopsy.

Impact of 5-ALPHA reductase inhibitor use on prostate cancer risk at the time of urology referral.

226 Background: 5-alpha reductase inhibitors (5-ARIs) are commonly used medications for the treatment of lower urinary tract symptoms caused by benign prostatic hyperplasia. One of the consequences of 5-ARI use is a 50% drop in serum PSA without a concomitant reduction in prostate cancer (PCa) risk. Previous work has suggested that 5-ARI use is associated with worse PCa-specific outcomes. The objective of this study was to evaluate the impact of 5-ARI use on patients’ PCa risk at the time of referral from primary care to urology. Methods: This retrospective cohort study included all men ≥ 40 years who had a PSA resulted between 2018-2019 and were seen in an ambulatory setting. PSA testing was determined through laboratory data in the electronic health record (EHR). Clinical and demographic data were collected for all men. 5-ARI use was determined through orders in the EHR. Men were assigned PCa risk according to both the Prostate Biopsy Collaborative Group (PBCG) and Prostate Cancer Prevention Trial (PCPT) risk calculators. PSA values were doubled for 5-ARI users prior to calculating risk. Referral to urology for PCa risk was determined using the narrative reason for referral associated with the referral order. Results: Between 2018-2019, 91,368 men had a PSA test, including 2,939 5-ARI users, and 88,429 non-users. Uncorrected median PSA and the proportion of men referred to urology for PCa risk were similar between the two groups (p = 0.60 and p = 0.17, respectively). Of men referred to urology for PCa risk, 5-ARI users had similar uncorrected PSA to non-users (p=0.86) but higher risk for high grade PCa once PSA correction was performed, median (IQR) 48% (24%) vs 28% (18%) using the PBCG and 21% (17%) vs 10% (10%) using the PCPT (p &lt; 0.01 for both) (Table). Conclusions: Men taking 5-ARIs have significantly higher risk for high-grade PCa at time of referral to urology than non-users in this cohort. As the unadjusted PSA at referral to urology for PCa risk was the same between 5-ARI users and non-users, this indicates that the effect of 5-ARI use on serum PSA levels is not routinely accounted for when assessing PCa risk. Further study on interventions to account for 5-ARI use when screening for PCa are warranted. [Table: see text]

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

BackgroundEuropean Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa).ObjectivesTo 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 ParticipantsSingle-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI.Outcome Measurements and Statistical AnalysisPatients’ 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 LimitationThe 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.ConclusionsmpMRI 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