Comparative Performance of Machine Learning Models in Reducing Unnecessary Targeted Prostate Biopsies.

ABSTRACT:

Background and objective

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.

Methods

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, Pr

SUBMITTER: Chen F 

PROVIDER: S-EPMC12332026 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature