Leveraging Representation Learning for Bi-parametric Prostate MRI to Disambiguate PI-RADS 3 and Improve Biopsy Decision Strategies.

ABSTRACT:

Objectives

Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies.

Materials and methods

This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to

SUBMITTER: Umapathy L 

PROVIDER: S-EPMC12336996 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature