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Introduction: When laparoscopic radical prostatectomy (LRP) was introduced as a novel treatment option for prostate cancer, it had to compete with the established open techniques. The short- and intermediate-term oncologic and functional outcomes were encouraging and comparable to those with retropubic radical prostatectomy. However, the long-term oncologic safety for LRP has yet to be fully elucidated. We evaluated the long-term oncologic outcomes of an initial series of patients who had undergone LRP., Patients and Methods: An initial unselected and consecutive series of 100 patients who had undergone LRP for clinically localized prostate cancer from 1999 to 2001 was identified. The pre-, intra-, and postoperative data were collected. Biochemical recurrence (BCR) was defined as a prostate-specific antigen (PSA) value of ≥ 0.2 ng/mL. The outcome measures were cancer control (CC), BCR-free survival (BCRFS), cancer-specific survival (CSS), and overall survival (OS)., Results: The mean patient age was 64 ± 7 years, and the mean preoperative PSA level was 9.6 ± 8.3 ng/mL. Of the 100 patients, 79 (79%) had stage pT2 and 15 (15%) had stage pT3 disease. Positive surgical margins were found in 25 patients (25%; 16.4% for pT2 and 40% for pT3). The median follow-up time was 126 months (range, 60-176 months). The 5-year CC rate was 82%. The estimated 10-year BCRFS was 83% and 80% for patients with stage pT2 and pT3 tumors, respectively. The median time to BCR was 52 months (range, 6-144 months). The estimated 10-year CSS and OS was 98% and 93%, respectively., Conclusion: Our long-term follow-up data from an initial unselected patient cohort have indicated that LRP offers excellent long-term oncologic control for patients with localized prostate cancer., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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.

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

Objectives: To develop a novel biopsy prostate cancer (PCa) prevention calculator (BioPrev-C) using data from a prospective cohort all undergoing mpMRI targeted and transperineal template saturation biopsy. Materials and methods: Data of all men who underwent prostate biopsy in our academic tertiary care center between 11/2016 and 10/2019 was prospectively collected. We developed a clinical prediction model for the detection of high-grade PCa (Gleason score ≥7) based on a multivariable logistic regression model incorporating age, PSA, prostate volume, digital rectal examination, family history, previous negative biopsy, 5-alpha-reductase inhibitor use and MRI PI-RADS score. BioPrev-C performancewas externally validated in another prospective Swiss cohort and compared with two other PCa risk-calculators (SWOP-RC and PBCG-RC). Results: Of 391 men in the development cohort, 157 (40.2%) were diagnosed with high-grade PCa. Validation of the BioPrev C revealed good discrimination with an area under the curve for high-grade PCa of 0.88 (95% Confidence Interval 0.82- 0.93), which was higher compared to the other two risk calculators (0.71 for PBCG and 0.84 for SWOP). The BioPrev-C revealed good calibration in the low-risk range (0 - 0.25) andmoderate overestimation in the intermediate risk range (0.25 - 0.75). The PBCG-RC showed good calibration and the SWOP-RC constant underestimation of high-grade PCa over the whole prediction range. Decision curve analyses revealed a clinical net benefit for the BioPrev-C at a clinical meaningful threshold probability range (≥4%), whereas PBCG and SWOP calculators only showed clinical net benefit above a 30% threshold probability. Conclusion: BiopPrev-C is a novel contemporary risk calculator for the prediction of high-grade PCa. External validation of the BioPrev-C revealed relevant clinical benefit, which was superior compared to other well-known risk calculators. The BioPrev-C has the potential to significantly and safely reduce the number of men who should undergo a prostate biopsy. [ABSTRACT FROM AUTHOR]

In a large, international cohort, men with indications for prostate biopsy have an increased risk of high-grade prostate cancer in the presence of a family history of prostate cancer, second-degree prostate cancer, and first-degree breast cancer, controlling for other risk factors. This risk did not vary based on prostate-specific antigen level, and having additional affected relatives conferred an increased risk of high-grade prostate cancer on biopsy. The risk of high-grade prostate cancer, given a family history of cancer, has been described in the general population, but not among men selected for prostate biopsy in an international cohort. To estimate the risk of high-grade prostate cancer on biopsy based on a family history of cancer. This is a multicenter study of men undergoing prostate biopsy from 2006 to 2019, including 12 sites in North America and Europe. All sites recorded first-degree prostate cancer family histories; four included more detailed data on the number of affected relatives, second-degree relatives with prostate cancer, and breast cancer family history. Multivariable logistic regressions evaluated odds of high-grade (Gleason grade group ≥2) prostate cancer. Separate models were fit for family history definitions, including first- and second-degree prostate cancer and breast cancer family histories. A first-degree prostate cancer family history was available for 15 799 men, with a more detailed family history for 4617 (median age 65 yr, both cohorts). Adjusted odds of high-grade prostate cancer were 1.77 times greater (95% confidence interval [CI] 1.57−2.00, p < 0.001, risk ratio [RR] = 1.40) with first-degree prostate cancer, 1.38 (95% CI 1.07−1.77, p = 0.011, RR = 1.22) for second-degree prostate cancer, and 1.30 (95% CI 1.01−1.67, p = 0.040, RR = 1.18) for first-degree breast cancer family histories. Interaction terms revealed that the effect of a family history did not differ based on prostate-specific antigen but differed based on age. This study is limited by missing data on race and prior negative biopsy. Men with indications for biopsy and a family history of prostate or breast cancer can be counseled that they have a moderately increased risk of high-grade prostate cancer, independent of other risk factors. In a large international series of men selected for prostate biopsy, finding a high-grade prostate cancer was more likely in men with a family history of prostate or breast cancer. [ABSTRACT FROM AUTHOR]

Introduction: To reduce unnecessary prostate biopsies while using novel tests judiciously, we created a tool to predict the probability of clinically significant prostate cancer (CSPC) vs. low-risk prostate cancer or negative biopsy (i.e., when intervention is likely not needed) among men undergoing initial or repeat biopsy.Methods: Separate models were created for men undergoing initial and repeat biopsy, identified from our institutional biopsy database and the placebo arm of the REDUCE trial, respectively, to predict the presence of CSPC (Gleason≥7 or>33% of cores involved). Predictors considered included age, race, body mass index, family history of prostate cancer, digital rectal examination, prostate volume, prostate-specific antigen (PSA), free-to-total PSA, presence of high-grade prostatic intraepithelial neoplasia or atypical small acinar proliferation on prior biopsy, number of prior biopsies, and number of cores previously taken. Multivariable logistic regression models that minimized the Akaike Information Criterion and maximized out-of-sample area under the receiver operating characteristics curve (AUC) were selected.Results: Of 7,963 biopsies (initial = 2,042; repeat = 5,921), 1,138 had CSPC (initial = 870 [42.6%]; repeat = 268 [4.5%]). Age, race, body mass index, family history, digital rectal examination, and PSA were included in the initial biopsy model (out-of-sample AUC = 0.74). Age, prostate volume, PSA, free-to-total PSA, prior high-grade prostatic intraepithelial neoplasia, and number of prior biopsies were included in the repeat biopsy model (out-of-sample AUC = 0.81).Conclusion: These prediction models may help guide clinicians in avoiding unnecessary initial and repeat biopsies in men unlikely to harbor CSPC. This tool may also allow for the more judicious use of novel tests only in patients in need of further risk stratification before deciding whether to biopsy. [ABSTRACT FROM AUTHOR]