Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens.

ABSTRACT: Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. While deep learning-based image analysis (DLIA) shows promise in enhancing diagnostic accuracy, its application to radical prostatectomy (RP) specimens remains underexplored. In this study, we evaluated the clinical feasibility and prognostic value of a DLIA algorithm for Gleason grading and tumor quantification on whole RP specimens. Using 29,646 digitized H&E-stained slides from 992 patients who underwent RP, we compared the case-level algorithm results with pathologist assessments for the International Society of Urological Pathology grade groups (GG), tumor volumes (TV), and percent tumor volumes (PTV). We also evaluated their prognostic p

SUBMITTER: Kwak TY 

PROVIDER: S-EPMC11958791 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature