Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3 Tesla Multiparametric Prostate Magnetic Resonance Imaging

Background: Multiparametric magnetic resonance imaging (mpMRI) for prostate cancer detection without careful patient selection may lead to excessive resource utilization and costs. Objective: To develop and validate a clinical tool for predicting the presence of high-risk lesions on mpMRI. Design, setting, participants: Four tertiary care centers were included in this retrospective and prospective study (BiRCH Study Collaborative). Statistical models were generated using 1269 biopsy-naive, prior negative biopsy, and active surveillance patients who underwent mpMRI. Using age, prostate-specific antigen, and prostate volume, a support vector machine model was developed for predicting the probability of harboring Prostate Imaging Reporting and Data System 4 or 5 lesions. The accuracy of future predictions was then prospectively assessed in 214 consecutive patients. Outcome measurements and statistical analysis: Receiver operating characteristic, calibration, and decision curves were generated to assess model performance. Results and limitations: For biopsy-naive and prior negative biopsy patients (n = 811), the area under the curve (AUC) was 0.730 on internal validation. Excellent calibration and high net clinical benefit were observed. On prospective external validation at two separate institutions (n = 88 and n = 126), the machine learning model discriminated with AUCs of 0.740 and 0.744, respectively. The final model was developed on the Microsoft Azure Machine Learning platform (birch.azurewebsites.net). This model requires a prostate volume measurement as input. Conclusions: In patients who are naive to biopsy or those with a prior negative biopsy, BiRCH models can be used to select patients for mpMRI. Patient summary: In this multicenter study, we developed and prospectively validated a calculator that can be used to predict prostate magnetic resonance imaging (MRI) results using patient age, prostate-specific antigen, and prostate volume as input. This tool can aid health care professionals and patients to make an informed decision regarding whether to get an MRI. (C) 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.