Cancer Prevention Trial Risk Calculator Research Articles

Development and Validation of an 18-Gene Urine Test for High-Grade Prostate Cancer

Benefits of prostate cancer (PCa) screening with prostate-specific antigen (PSA) alone are largely offset by excess negative biopsies and overdetection of indolent cancers resulting from the poor specificity of PSA for high-grade PCa (ie, grade group [GG] 2 or greater). To develop a multiplex urinary panel for high-grade PCa and validate its external performance relative to current guideline-endorsed biomarkers. RNA sequencing analysis of 58 724 genes identified 54 markers of PCa, including 17 markers uniquely overexpressed by high-grade cancers. Gene expression and clinical factors were modeled in a new urinary test for high-grade PCa (MyProstateScore 2.0 [MPS2]). Optimal models were developed in parallel without prostate volume (MPS2) and with prostate volume (MPS2+). The locked models underwent blinded external validation in a prospective National Cancer Institute trial cohort. Data were collected from January 2008 to December 2020, and data were analyzed from November 2022 to November 2023. Protocolized blood and urine collection and transrectal ultrasound-guided systematic prostate biopsy. Multiple biomarker tests were assessed in the validation cohort, including serum PSA alone, the Prostate Cancer Prevention Trial risk calculator, and the Prostate Health Index (PHI) as well as derived multiplex 2-gene and 3-gene models, the original 2-gene MPS test, and the 18-gene MPS2 models. Under a testing approach with 95% sensitivity for PCa of GG 2 or greater, measures of diagnostic accuracy and clinical consequences of testing were calculated. Cancers of GG 3 or greater were assessed secondarily. Of 761 men included in the development cohort, the median (IQR) age was 63 (58-68) years, and the median (IQR) PSA level was 5.6 (4.6-7.2) ng/mL; of 743 men included in the validation cohort, the median (IQR) age was 62 (57-68) years, and the median (IQR) PSA level was 5.6 (4.1-8.0) ng/mL. In the validation cohort, 151 (20.3%) had high-grade PCa on biopsy. Area under the receiver operating characteristic curve values were 0.60 using PSA alone, 0.66 using the risk calculator, 0.77 using PHI, 0.76 using the derived multiplex 2-gene model, 0.72 using the derived multiplex 3-gene model, and 0.74 using the original MPS model compared with 0.81 using the MPS2 model and 0.82 using the MPS2+ model. At 95% sensitivity, the MPS2 model would have reduced unnecessary biopsies performed in the initial biopsy population (range for other tests, 15% to 30%; range for MPS2, 35% to 42%) and repeat biopsy population (range for other tests, 9% to 21%; range for MPS2, 46% to 51%). Across pertinent subgroups, the MPS2 models had negative predictive values of 95% to 99% for cancers of GG 2 or greater and of 99% for cancers of GG 3 or greater. In this study, a new 18-gene PCa test had higher diagnostic accuracy for high-grade PCa relative to existing biomarker tests. Clinically, use of this test would have meaningfully reduced unnecessary biopsies performed while maintaining highly sensitive detection of high-grade cancers. These data support use of this new PCa biomarker test in patients with elevated PSA levels to reduce the potential harms of PCa screening while preserving its long-term benefits.

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]

Feasibility and accuracy of prostate cancer risk calculators in prediction of prostate cancer, extraprostatic extension as well as the risk of lymph nodes metastasis.

The aim of this article was to evaluate the accuracy of European Randomized study of Screening for Prostate Cancer (ERSPC 4) and Prostate Cancer Prevention Trial (PCPT 2.0) risk calculator on predicting high-grade prostate cancer (HGPCa) and accuracy of Partin and Briganti nomograms on organ confined (OC) or extraprostatic cancer (EXP), seminal vesicles invasion (SVI) and risk of lymph nodes metastasis. A cohort of 269 men aged between 44-84 years, who underwent radical prostatectomy was retrospectively analysed. Based on estimated calculator risk, patients were divided into risk groups: low (LR), medium (MR) and high (HR). Results obtained with calculators were compared to post-surgical final pathology outcome. In ERPSC4, the average risk for HGPC was LR = 5%, MR = 21%, and HR = 64%. In PCPT 2.0, the average risk for HG was: LR - 8%, MR - 14%, and HR - 30%. In the final results, HGPC was observed in: LR = 29%, MR = 67%, and HR = 81%. In Partin, LNI was estimated to occur in: LR = 1%, MR = 2%, and HR = 7.5% and in Briganti: LR = 1.8%, MR = 11.4%, and HR = 44.2% while finally it was found in: LR = 1.3%, MR = 0%, and HR = 11.6%. ERPSC 4 and PCPT 2.0 corresponded well with each other as well as Partin and Briganti. ERPSC 4 was more accurate in predicting HGPC than PCPT 2.0. Partin was more accurate as for LNI than Briganti. In this study group a large underestimation was observed in reference to Gleason grade.

The effect of lowering cholesterol through diet on serum prostate-specific antigen levels: A secondary analysis of clinical trials.

Statins appear to lower serum prostate-specific antigen (PSA) and improve prostate cancer (PCa) outcomes through cholesterol-dependent and independent mechanisms. While dietary modifications have an established role in serum cholesterol reduction, whether diet-driven cholesterol reductions yield similar PCa benefits to that observed with statins is unclear. We aimed to study the effect of dietdriven cholesterol reduction on serum PSA and estimated-PCa risk. A total of 291 men from six published randomized controlled trials of dietary interventions were included. Men were aged ≥40 years, free of PCa, and had baseline PSA <10.0 ng/mL. Participants received one of four diets (high-fiber, low-glycemic index, low-glycemic load, or cholesterol-lowering) for 8-24 weeks. The primary outcome evaluated the association between change from baseline low-density lipoprotein cholesterol (LDL-C) and PSA. How cholesterol reduction modified PCa risk was estimated using the Prostate Cancer Prevention Trial (PCPT) risk calculator (limited to age ≥55 years, baseline PSA ≥1.0 ng/mL). Baseline PSA was 0.90 ng/mL (interquartile range [IQR] 0.55-1.60) and LDL-C was 90 mg/dL (IQR 69-125). In multivariate regression, PSA decreased 1.9% (95% confidence interval [CI] 0.55-3.2, p=0.005) per 10% reduction in LDL-C. This regression was greater in men with baseline PSA ≥2.0 ng/mL (-5.4%, 95% CI 2.2-8.6] per 10% LDL-C reduction, p-interaction=0.001). In men with estimable PCPT risk, statin-comparable LDL-C reductions (≥15%) reduced PSA by 12% (p<0.001) and estimated PCa risk by 6.5% (p=0.005). This is the first study to show that serum cholesterol reduction through dietary interventions significantly lowered serum PSA and estimated PCa risk. Whether cholesterol-lowering diets improve PCa outcomes warrants investigation.

Predicting high-grade prostate cancer at initial biopsy: clinical performance of the ExoDx (EPI) Prostate Intelliscore test in three independent prospective studies

BackgroundThe ability to discriminate indolent from clinically significant prostate cancer (PC) at the initial biopsy remains a challenge. The ExoDx Prostate (IntelliScore) (EPI) test is a noninvasive liquid biopsy that quantifies three RNA targets in urine exosomes. The EPI test stratifies patients for risk of high-grade prostate cancer (HGPC; ≥ Grade Group 2 [GG] PC) in men ≥ 50 years with equivocal prostate-specific antigen (PSA) (2–10 ng/mL). Here, we present a pooled meta-analysis from three independent prospective-validation studies in men presenting for initial biopsy decision.MethodsPooled data from two prospective multi-site validation studies and the control arm of a clinical utility study were analyzed. Performance was evaluated using the area under the receiver-operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity for discriminating ≥ GG2 from GG1 and benign pathology.ResultsThe combined cohort (n = 1212) of initial-biopsy subjects had a median age of 63 years and median PSA of 5.2 ng/mL. The EPI AUC (0.70) was superior to PSA (0.56), Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) (0.62), and The European Randomized Study of Screening for Prostate Cancer (ERSPC) (0.59), (all p-values <0.001) for discriminating GG2 from GG1 and benign histology. The validated cutoff of 15.6 would avoid 23% of all prostate biopsies and 30% of “unnecessary” (benign or Gleason 6/GG1) biopsies, with an NPV of 90%.ConclusionsEPI is a noninvasive, easy-to-use, urine exosome–RNA assay that has been validated across 3 independent prospective multicenter clinical trials with 1212 subjects. The test can discriminate high-grade (≥GG2) from low-grade (GG1) cancer and benign disease. EPI effectively guides the biopsy-decision process independent of PSA and other standard-of-care factors.

PD65-03 PROSTATE SPECIFIC ANTIGEN THRESHOLD OF 1.0 PREDICTS LONG-TERM PROSTATE CANCER RISK FROM A PROSPECTIVE COHORT ANALYSIS

You have accessJournal of UrologyProstate Cancer: Detection & Screening VII (PD65)1 Sep 2021PD65-03 PROSTATE SPECIFIC ANTIGEN THRESHOLD OF 1.0 PREDICTS LONG-TERM PROSTATE CANCER RISK FROM A PROSPECTIVE COHORT ANALYSIS Christine Ibilibor, Jonathan Gelfond, Martin Goros, Teresa Johnson-Pais, Robin Leach, Ian M. Thompson, and Michael Liss Christine IbiliborChristine Ibilibor More articles by this author , Jonathan GelfondJonathan Gelfond More articles by this author , Martin GorosMartin Goros More articles by this author , Teresa Johnson-PaisTeresa Johnson-Pais More articles by this author , Robin LeachRobin Leach More articles by this author , Ian M. ThompsonIan M. Thompson More articles by this author , and Michael LissMichael Liss More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002109.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We investigated baseline prostate specific antigen (PSA) testing related to the future risk of prostate cancer development to inform optimal screening timepoints. METHODS: The San Antonio Biomarkers of Risk (SABOR) cohort is a contemporary, multi-ethnic, multi-racial population of men with no prostate cancer diagnosis at the time of enrollment in 2000. We calculated the risk of developing high-grade prostate cancer by applying the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPT RC) at enrollment and annually for up to 15-years of follow up. We used baseline risk factors and PSA to estimate long-term PCPT risk of high-grade prostate cancer using a linear mixed effects model. We stratified the risk groups by baseline PSA level above or below 1 ng/ml. RESULTS: We analyzed data from 4,170 men with a median follow up of 5.6 years (interquartile range of 0.9 to 11.6 years). The proportion of patients whose baseline PSA was > 1.0ng/ml with a PCPT risk of high-grade cancer greater than 5% at 5-, 10-, and 15-years of follow-up was 42.8%, 63.8%, and 77.9%, respectively, compared to 2.5%, 10.8%, and 28.1% for men whose baseline PSA was < 1.0ng/ml. Notably, when PCPT risk of high-grade disease and follow up time were modelled, age and African American race had a greater impact on risk of high-grade disease than PSA. CONCLUSIONS: Men with a baseline PSA level < 1 ng/ml maintain a low risk for the development of high-grade prostate cancer over a 5-year period, arguing for less frequent PSA testing in this population. Source of Funding: This research was supported in part by funding from the National Cancer Institute (U01 CA096492 and P30 CA054174) and the Cancer Prevention and Research Institute of Texas CPRIT (RP170345). Christine Ibilibor has received research support through the University of Texas Health Science Center at San Antonio Cancer CPRIT Research Training Award (RP170345) © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1152-e1153 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Christine Ibilibor More articles by this author Jonathan Gelfond More articles by this author Martin Goros More articles by this author Teresa Johnson-Pais More articles by this author Robin Leach More articles by this author Ian M. Thompson More articles by this author Michael Liss More articles by this author Expand All Advertisement Loading ...

Improving prostate biopsy decision making in Mexican patients: Still a major public health concern

BackgroundProstate cancer screening has reduced its mortality in 21%. However, this has also led to an increased number of biopsies in order to establish the diagnosis, many of them unnecessary. Current screening guidelines prioritize use of prostatic magnetic resonance and new biomarkers to reduce unnecessary biopsies, however, their implementation in developing countries screening programs is mainly limited by its costs. ObjectiveWe aimed to evaluate Prostate Biopsy Risk Collaborative Group (PBCG) and Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) 2.0 predictions accuracy in Mexican patients in order to guide prostate biopsy decision making and reduce unnecessary biopsies. Materials and methodsWe retrospectively analyzed patients between 55 and 90 years old who underwent prostate biopsy in a high-volume center in Mexico between January 2017 and June 2020. Clinical utility of PBCG and PCPTRC 2.0 to predict high-grade prostate cancer (HGPCa) biopsy outcomes was evaluated using decision curve analysis and compared to actual biopsy decision making. Receiver operating characteristics area under the curve (AUC) was used to measure discrimination and external validation. ResultsFrom 687 patients eligible for prostate biopsy, 433 met selections criteria. One hundred and thirty-five (31.17%) patients were diagnosed with HGPCa, 63 (14.54%) with low-grade disease and 235 (54.27%) had a negative biopsy. PCPTRC 2.0 ≥15% threshold got a standardized net benefit (sNB) of 0.70, while PBCG ≥30% and ≥35% had a sNB of 0.27 and 0.15, respectively. Use of both models for guiding prostate biopsy decision resulted in no statistical difference for HGCPa detection rates, while achieved a significant difference in reducing total and unnecessary biopsies. However, this difference was lower (better) for PCPTRC 2.0, being statistically significative when compared against PBCG thresholds. Both models were well calibrated (AUC 0.79) and achieved external validation compared with international cohorts. ConclusionsOur study is the first to effectively validate both PCPTRC 2.0 and PBCG predictions for the Mexican population, proving that their use in daily practice improves biopsy decision making by accurately predicting HGPCa and limit unnecessary biopsies without representing additional costs to screening programs.

Comparison of Multiparametric Magnetic Resonance Imaging–Targeted Biopsy With Systematic Transrectal Ultrasonography Biopsy for Biopsy-Naive Men at Risk for Prostate Cancer

Magnetic resonance imaging (MRI) with targeted biopsy is an appealing alternative to systematic 12-core transrectal ultrasonography (TRUS) biopsy for prostate cancer diagnosis, but has yet to be widely adopted. To determine whether MRI with only targeted biopsy was noninferior to systematic TRUS biopsies in the detection of International Society of Urological Pathology grade group (GG) 2 or greater prostate cancer. This multicenter, prospective randomized clinical trial was conducted in 5 Canadian academic health sciences centers between January 2017 and November 2019, and data were analyzed between January and March 2020. Participants included biopsy-naive men with a clinical suspicion of prostate cancer who were advised to undergo a prostate biopsy. Clinical suspicion was defined as a 5% or greater chance of GG2 or greater prostate cancer using the Prostate Cancer Prevention Trial Risk Calculator, version 2. Additional criteria were serum prostate-specific antigen levels of 20 ng/mL or less (to convert to micrograms per liter, multiply by 1) and no contraindication to MRI. Magnetic resonance imaging-targeted biopsy (MRI-TB) only if a lesion with a Prostate Imaging Reporting and Data System (PI-RADS), v 2.0, score of 3 or greater was identified vs 12-core systematic TRUS biopsy. The proportion of men with a diagnosis of GG2 or greater cancer. Secondary outcomes included the proportion who received a diagnosis of GG1 prostate cancer; GG3 or greater cancer; no significant cancer but subsequent positive MRI results and/or GG2 or greater cancer detected on a repeated biopsy by 2 years; and adverse events. The intention-to-treat population comprised 453 patients (367 [81.0%] White, 19 [4.2%] African Canadian, 32 [7.1%] Asian, and 10 [2.2%] Hispanic) who were randomized to undergo TRUS biopsy (226 [49.9%]) or MRI-TB (227 [51.1%]), of which 421 (93.0%) were evaluable per protocol. A lesion with a PI-RADS score of 3 or greater was detected in 138 of 221 men (62.4%) who underwent MRI, with 26 (12.1%), 82 (38.1%), and 30 (14.0%) having maximum PI-RADS scores of 3, 4, and 5, respectively. Eighty-three of 221 men who underwent MRI-TB (37%) had a negative MRI result and avoided biopsy. Cancers GG2 and greater were identified in 67 of 225 men (30%) who underwent TRUS biopsy vs 79 of 227 (35%) allocated to MRI-TB (absolute difference, 5%, 97.5% 1-sided CI, -3.4% to ∞; noninferiority margin, -5%). Adverse events were less common in the MRI-TB arm. Grade group 1 cancer detection was reduced by more than half in the MRI arm (from 22% to 10%; risk difference, -11.6%; 95% CI, -18.2% to -4.9%). Magnetic resonance imaging followed by selected targeted biopsy is noninferior to initial systematic biopsy in men at risk for prostate cancer in detecting GG2 or greater cancers. ClinicalTrials.gov Identifier: NCT02936258.

Impact of the MyProstateScore (MPS) Test on the Clinical Decision to Undergo Prostate Biopsy: Results From a Contemporary Academic Practice

ObjectiveTo evaluate the association of the MyProstateScore (MPS) urine test on the decision to undergo biopsy in men referred for prostate biopsy in urology practice. MethodsMPS testing was offered as an alternative to immediate biopsy in men referred to the University of Michigan for prostate biopsy from October 2013 through October 2016. The primary endpoint was the decision to perform biopsy. The proportion of patients undergoing biopsy was compared to predicted risk scores from the Prostate Cancer Prevention Trial risk calculator (PCPTrc). Analyses were stratified by the use of multiparametric magnetic resonance imaging (mpMRI). The associations of PCPTrc, MPS, and mpMRI with the decision to undergo biopsy were explored in a multivariable logistic regression model. ResultsOf 248 patients, 134 (54%) proceeded to prostate biopsy. MPS was significantly higher in biopsied patients (median 29 vs14, P < .001). The use of biopsy was strongly associated with MPS, with biopsy rates of 26%, 38%, 58%, 90%, and 85% in the first through fifth quintiles, respectively (P < .001). MPS association with biopsy persisted upon stratification by mpMRI. On multivariable analysis, MPS was strongly associated with the decision to undergo biopsy when modeled as both a continuous (odds ratio [OR] 1.05, 95%; confidence interval [CI] 1.04-1.08; <.001) and binary (OR 7.76, 95%; CI 4.14-14.5; P < .001) variable. ConclusionMany patients (46%) undergoing clinical MPS testing as an alternative to immediate prostate biopsy were able to avoid biopsy. Increasing MPS was strongly associated with biopsy rates. These findings were robust to use of mpMRI.

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

BackgroundPredictive models that take race into account like the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPT RC) and the new Prostate Biopsy Collaborative Group (PBCG) RC have been developed to equitably mitigate the overdiagnosis of prostate specific antigen (PSA) screening. Few studies have compared the performance of both calculators across racial groups.MethodsFrom 1485 prospectively recruited participants, 954 men were identified undergoing initial prostate biopsy for abnormal PSA or digital rectal examination in five Chicago hospitals between 2009 and 2014. Discrimination, calibration, and frequency of avoided biopsies were calculated to assess the performance of both risk calculators.ResultsOf 954 participants, 463 (48.5%) were Black, 355 (37.2%) were White, and 136 (14.2%) identified as Other. Biopsy results were as follows: 310 (32.5%) exhibited no cancer, 323 (33.9%) indolent prostate cancer, and 321 (33.6%) clinically significant prostate cancer (csPCa). Differences in area under the curve (AUC)s for the detection of csPCa between PCPT and PBCG were not statistically different across all racial groups. PBCG did not improve calibration plots in Blacks and Others, as it showed higher levels of overprediction at most risk thresholds. PCPT led to an increased number of avoidable biopsies in minorities compared to PBCG at the 30% threshold (68% vs. 28% of all patients) with roughly similar rates of missed csPCa (23% vs. 20%).ConclusionSignificant improvements were noticed in PBCG’s calibrations and net benefits in Whites compared to PCPT. Since PBCG’s improvements in Blacks are disputable and potentially biases a greater number of low risk Black and Other men towards unnecessary biopsies, PCPT may lead to better biopsy decisions in racial minority groups. Further comparisons of commonly used risk calculators across racial groups is warranted to minimize excessive biopsies and overdiagnosis in ethnic minorities.

Variations in prostate biopsy recommendation and acceptance confound evaluation of risk factors for prostate cancer: Examining race and BMI

BackgroundProstate cancer is ubiquitous in older men; differential screening patterns and variations in biopsy recommendations and acceptance will affect which man is diagnosed and, therefore, evaluation of cancer risk factors. We describe a statistical method to reduce prostate cancer detection bias among African American (n = 3398) and Non-Hispanic White men (n = 22,673) who participated in the Selenium and Vitamin E Cancer Prevention trial (SELECT) and revisit a previously reported association between race, obesity and prostate cancer risk. MethodsFor men with screening values suggesting prostate cancer but in whom biopsy was not performed, the Prostate Cancer Prevention Trial Risk Calculator was used to estimate probability of prostate cancer. Associations of body mass index (BMI) and race with incident prostate cancer were compared for observed versus imputation-enhanced outcomes using incident density ratios. ResultsAccounting for differential biopsy assessment, the previously reported positive linear trend between BMI and prostate cancer in African American men was not observed; no BMI association was found among Non-Hispanic White men. ConclusionsDifferential disease classification among men who may be recommended to undergo and then consider whether to accept a prostate biopsy leads to inaccurate identification of prostate cancer risk factors. Imputing a man’s prostate cancer status reduces detection bias. Covariate adjustment does not address the problem of outcome misclassification. Cohorts evaluating incident prostate cancer should collect longitudinal screening and biopsy data to adjust for this potential bias.

Prostate cancer risk prediction models in Eastern Asian populations: current status, racial difference, and future directions.

Prostate cancer (PCa) risk calculators (RCs) with prostate-specific antigen (PSA) and other risk factors can greatly improve the accurate prediction of potential risk of PCa compared to PSA. The European Randomized Study of Screening for PCa Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) are developed on the Western population. However, the Western RCs showed limited diagnostic efficacy in the Eastern Asian population, mainly due to racial differences between the two populations. We aimed to review the application of Western RCs and Eastern Asian RCs in Eastern Asian cohorts and to identify the characteristics and efficacy of these RCs.

Magnetic Resonance Imaging-Defined Prostate-Specific Antigen Density Significantly Improves the Risk Prediction for Clinically Significant Prostate Cancer on Biopsy

ObjectiveTo evaluate the predictive value of magnetic resonance imaging (MRI)-defined prostate-specific antigen (PSA) density and MRI interpretation for the detection of clinically significant prostate cancer (PCa). MethodsWe retrospectively reviewed our institutional database of men who received prostate MRI prior to biopsy at our institution between September 2014 and December 2016, excluding those on active surveillance (n = 372). Logistic regression models to predict clinically significant PCa on biopsy were developed using (1) MRI-defined PSA density; (2) PSA alone; and (3) the Prostate Cancer Prevention Trial (PCPT) risk calculator. Additional logistic regression models were then generated by combining the previous clinical variables with MRI interpretation (ie, prostate imaging reporting and data system [PI-RADS] classification): (1) MRI-defined PSA density + MRI interpretation; (2) PSA + MRI interpretation; and (3) PCPT + MRI interpretation. Receiving operator characteristic curves for each of the 6 models were generated, and the area under the curves (AUC) were compared. ResultsMRI-defined PSA density (AUCPSAD = 0.77) significantly outperformed the PSA (AUCPSA = 0.66, P < .01) and PCPT models (AUCPCPT = 0.70, P < .01). When combined with MRI interpretation (ie, PI-RADS classification), the MRI-defined PSA density model (AUCPSAD + MRI = 0.80) significantly outperformed the PSA (AUCPSA + MRI = 0.75, P < .01) and PCPT models (AUCPCPT + MRI = 0.76, P = .01). ConclusionIn addition to the PI-RADS classification, prebiopsy multiparametric magnetic resonance imaging also provides an accurate assessment of prostate volume. By utilizing this additional data to determine MRI-defined PSA density, we find that risk discrimination for clinically significant PCa on biopsy can be significantly improved.

Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information.

We consider a situation where there is rich historical data available for the coefficients and their standard errors in an established regression model describing the association between a binary outcome variable Y and a set of predicting factors X, from a large study. We would like to utilize this summary information for improving estimation and prediction in an expanded model of interest, Y| X, B. The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We develop and evaluate several approaches for translating the external information into constraints on regression coefficients in a logistic regression model of Y| X, B. Borrowing from the measurement error literature we establish an approximate relationship between the regression coefficients in the models Pr(Y = 1| X , β), Pr(Y = 1| X, B, γ) and E(B| X, θ ) for a Gaussian distribution of B. For binary B we propose an alternate expression. The simulation results comparing these methods indicate that historical information on Pr(Y = 1| X , β) can improve the efficiency of estimation and enhance the predictive power in the regression model of interest Pr(Y = 1| X, B, γ). We illustrate our methodology by enhancing the High-grade Prostate Cancer Prevention Trial Risk Calculator, with two new biomarkers prostate cancer antigen 3 and TMPRSS2:ERG.

External validation of Chun, PCPT, ERSPC, Kawakami, and Karakiewicz nomograms in the prediction of prostate cancer: A single center cohort-study

ObjectivesThe aim of our study was to analyze the performance of 5 different risk calculators for prostate cancer diagnosis: Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC), European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSP-RC), Karakiewicz nomogram, Chun nomogram, and Kawakami Nomogram. MethodsFrom 2008 onwards, we consecutively enrolled, at a single institution in Italy, men undergoing 12-core transrectal ultrasound-guided prostate needle biopsy. Demographic, clinical, and pathological data were collected. The risk of prostate cancer (PCa) was calculated according to the PCPT-RC, ERSPC-RC, Karakiewicz, Kawakami, and Chun nomograms. Calibration and discrimination were assessed using calibration plots and receiver operator characteristic analysis. Additionally, decision curve analyses (DCA) were used to assess the net benefit associated with the adoption of each model. ResultsOverall, 1,100 patients were evaluated, 39% presented PCa and out of them 26% presented high-grade PCa (defined as Gleason ≥ 4 + 3). All the models showed good discrimination capacities for PCa on receiver operator characteristic analysis (area under the curve: 0.59–0.72) On calibration curves the ERSCP, the PCPT and the Chun nomogram underestimated the risk of PC while the Kawakami overestimated it. At DCA, the net benefit associated with the use of the models in the prediction of cancer was observed when the threshold probability was between 40% and 60%. ConclusionIn a cohort of Italian men undergoing prostate biopsy, the performance accuracy of these calculators for the prediction prostate cancer is suboptimal. According to our experience the use of these calculator in clinical practice should be encouraged. Although integration with new serum/urine markers or magnetic resonance imaging results is warranted.

A Contemporary Prostate Biopsy Risk Calculator Based on Multiple Heterogeneous Cohorts

BackgroundProstate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems. ObjectiveWe prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool. Design, setting, and participantsWe obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006–2017 at eight North American institutions for model-building and three European institutions for validation. Outcome measurements and statistical analysisWe used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts. Results and limitationsCross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2–76.8), a small improvement over the AUC of 72.3% (70.9–73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies. ConclusionsThe PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome. Patient summaryA contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.

Incorporation of Urinary Prostate Cancer Antigen 3 and TMPRSS2:ERG into Prostate Cancer Prevention Trial Risk Calculator

BackgroundThe Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) is a commonly used risk tool for predicting the outcome on biopsy based on the established risk factors. ObjectiveTo determine whether incorporation of the novel urinary markers prostate cancer antigen 3 (PCA3) and TMPRSS2:ERG (T2:ERG) into the PCPTRC improves its discrimination, accuracy, and clinical net benefit. Design, setting, and participantsSince PCA3 and T2:ERG were not measured as part of the PCPTRC, a Bayesian modeling approach was used to combine data where the markers were measured in a Michigan cohort with the PCPTRC as prior probabilities to form an updated PCPTRC. This update was compared to the existing PCPTRC on an independent Early Detection Research Network cohort in terms of discrimination, calibration, and decision curve analysis. Results and limitationsAmong the 1225 Michigan biopsies, 57.7%, 24.0%, and 18.3% were negative, with low- and high-grade (Gleason grade≥7) prostate cancer, respectively. Evaluated on the Early Detection Research Network validation set comprising 854 biopsies, areas under the curve (95% confidence interval) for predicting high-grade cancer in the 854 biopsies comprising the validation set were 70.0% (66.0–74.0%), 76.4% (72.8–80.0%), and 77.1% (73.6–80.6%) for the PCPTRC alone, with PCA3 added, and PCA3 and T2:ERG added, respectively. Net benefit was improved for the updated PCPTRC, while calibration was not. Limitations are that the updated PCPTRC is based on two different cohorts, the PCPT and Michigan, and that 20% of the validation set came from the Michigan center. More validation is required; hence, the updated risk tool is posted online. ConclusionsIncorporation of PCA3 into the PCPTRC improved validation on an independent cohort, whereas T2:ERG offered negligible utility in addition to PCA3. Patient summaryAfter passing external validation, prostate cancer antigen 3 has been added to the online Prostate Cancer Prevention Trial Risk Calculator for use by patients in deciding whether to proceed to biopsy. TMPRSS2:ERG did not improve prediction on the external validation set, but is included for further validation.

PROSTATE CANCER RISK CALCULATOR COMPARISON: IPCRC vs KPCRC vs PCPT-RC vs ERSPC-RC IN PREDICTING PROSTATE CANCER AT HASAN SADIKIN HOSPITAL

Objective: To compare the validated calculators of Indonesian Prostate Cancer Risk Calculator (IPCRC) vs Korean Prostate Cancer Risk Calculator (KPCRC) vs Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) vs European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC-RC) in predicting prostate cancer in our patients at Hasan Sadikin Hospital Bandung. Material &amp; methods: This study is a prospective study conducted from August 2014 – December 2015 at Urology Clinic at Hasan Sadikin Hospital that included all patients with BPH or suspected PCa who have been undergoing prostate biopsy. Variables such as age, PSA level, prostate volume (transabdominal or transrectal ultrasound) and digital rectal examination (DRE) finding were recorded. Risk calculation was generated using each risk calculator. Data were analyzed using Chi-square analysis, Kolmogorov-Smirnov, and finally receiver operating characterisitic (ROC) curve. All statistical analyses were performed with SPSS version 20. Results: There were 89 BPH and 43 PCa patients between August 2014 – December 2015. The mean age, PSA and prostate volume are 65.78 ± 8.33 years; 121.16 ± 375.76ng/ml; 55.86 ± 31.9ml respectively. Abnormal DRE was found in 27 PCas and 4 BPHs. Receiver operator curve analysis of IPCRC showed AUC 0.861 vs KPCRC (AUC=0.779) vs ERSPC-RC (AUC=0.745) vs PCPT-RC (AUC=0.794) vs PSA (AUC 0.794), sensitivity 81.4% and specificity 71.9% with cut-off point 19.67 for the IPCRC in predicting the prostate cancer in our center. Conclusion: IPCRC is better than KPCRC, PCPT-RC and ERSPC-RC in predicting prostate cancer in our center.

The Single-parameter, Structure-based IsoPSA Assay Demonstrates Improved Diagnostic Accuracy for Detection of Any Prostate Cancer and High-grade Prostate Cancer Compared to a Concentration-based Assay of Total Prostate-specific Antigen: A Preliminary Report

BackgroundIsoPSA is a serum-based assay that predicts prostate cancer (PCa) risk by partitioning isoforms of prostate-specific antigen (PSA) with an aqueous two-phase reagent. ObjectivesTo determine the diagnostic accuracy of IsoPSA in identifying the presence or absence of PCa and the presence of high-grade disease in a contemporary biopsy cohort. Design, setting, and participantsMulticenter prospective study of 261 men scheduled for prostate biopsy at five academic and community centers in the USA enrolled between August 2015 and December 2016. InterventionPerformance of the IsoPSA assay. Outcome measurements and statistical analysisDiscrimination power was evaluated using receiver operating characteristic (ROC) analysis. The outcome of the IsoPSA assay was transformed into risk probability using logistic regression. Decision curve analysis (DCA) was used to compare the net benefit of IsoPSA against other clinical protocols. Results and limitationsThe overall prevalence was 53% for any PCa and 34% for high-grade PCa. The area under the ROC curve was 0.79 for any cancer versus none and 0.81 for high-grade PCa versus low-grade PCa/benign histology. In this preliminary study, DCA revealed a superior net benefit of IsoPSA against no biopsy, all biopsy, and the modified Prostate Cancer Prevention Trial Risk Calculator 2.0. At a cutoff selected to recommend biopsy, IsoPSA demonstrated a 48% reduction in false-positive biopsies; at a cutoff selected to identity men at low risk of high-grade disease, there was a 45% reduction in the false-positive rate. ConclusionThe structure-based IsoPSA assay outperformed concentration-based PSA measurement, and provided a net benefit against other protocols. Once validated, clinical use of IsoPSA could significantly reduce unnecessary biopsies while identifying patients needing treatment. Patient summaryThe IsoPSA assay outperformed prostate-specific antigen in predicting the overall risk of prostate cancer and the risk of clinically significant cancer in a preliminary study. The IsoPSA assay could assist in determining the need for prostate biopsy for patients.

Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan

To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0. With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort. Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population. The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.