Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

ABSTRACT:

Background

Biochemical recurrence (BCR) occurs in 20%-40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. However, current preoperative recurrence prediction mainly relies on the use of the Gleason grading system, which omits within-grade morphological patterns and subtle histopathological features, leaving a significant amount of prognostic potential unexplored.

Methods

We collected and selected a total of 1585 prostate biopsy images with tumor regions from 317 patients (5 Whole Slide Images per patient) to develop a deep learning system for predicting BCR of PCa before prostatectomy. The Inception_v3 neural network was employed to train

SUBMITTER: Cao L 

PROVIDER: S-EPMC11812243 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature