Conventional core needle biopsy for prostate cancer diagnosis can lead to diagnostic uncertainty and complications, prompting exploration of alternative risk assessment approaches that use clinical and imaging features. Our aim was to evaluate the effectiveness of machine learning (ML) models in reducing unnecessary biopsies.
We conducted a retrospective analysis of data for 1884 patients across two academic centers who underwent prostate magnetic resonance imaging and biopsy between 2016 and 2020 or 2004 and 2011. Twelve ML models were assessed for prediction of clinically significant prostate cancer (csPCa; Gleason grade group ≥2) using combinations of clinical features, including patient age, prostate-specific antigen level and density, Prostate Imaging-Reporting and Data System/Likert score, lesion volume, and gland volume. The models were trained and validated using a tenfold split for intrasite, intersite, and combined-site data sets. Model effectiveness was evaluated using the area under the receiver operating characteristic curve and decision curve analysis.
The best-performing ML model would reduce the number of biopsies by 13.07% at a false-negative rate of 1.91%. Performance was consistent across sites, although the study is limited by the small number of centers and the absence of specific clinical data.
ML-enhanced clinical models provide an effective and generalizable approach for prediction of csPCa using standard clinical data. These models allow personalized risk assessment and follow-up, support clinical decision-making, and improve workflow efficiency.
Models that are enhanced by machine learning can predict the severity of prostate cancer and help doctors in tailoring treatments for individual patients. This approach can simplify health care decisions and improve clinical efficiency.
European urology oncology. 2025 Feb 08 [Epub ahead of print]
Fuyao Chen, Roxana Esmaili, Ghazal Khajir, Tal Zeevi, Moritz Gross, Michael Leapman, Preston Sprenkle, Amy C Justice, Sandeep Arora, Jeffrey C Weinreb, Michael Spektor, Steffan Huber, Peter A Humphrey, Angelique Levi, Lawrence H Staib, Rajesh Venkataraman, Darryl T Martin, John A Onofrey
Department of Biomedical Engineering, Yale University New Haven CT USA; Medical Scientist Training Program, Yale School of Medicine New Haven CT USA., Department of Radiology and Biomedical Imaging, Yale University New Haven CT USA; Charité Center for Diagnostic and Interventional Radiology, Charité-Universitätsmedizin Berlin Berlin Germany., Department of Urology, Yale University New Haven CT USA., Department of Biomedical Engineering, Yale University New Haven CT USA., VA Connecticut Healthcare West Haven CT USA; Department of Internal Medicine, Yale University New Haven CT USA., Department of Radiology and Biomedical Imaging, Yale University New Haven CT USA., Department of Pathology, Yale University New Haven CT USA., Department of Biomedical Engineering, Yale University New Haven CT USA; Department of Radiology and Biomedical Imaging, Yale University New Haven CT USA; Department of Electrical Engineering, Yale University New Haven CT USA., Eigen Health, Grass Valley CA USA., Department of Biomedical Engineering, Yale University New Haven CT USA; Department of Radiology and Biomedical Imaging, Yale University New Haven CT USA; Department of Urology, Yale University New Haven CT USA. Electronic address: .