[2603.20273] Efficient AI-Driven Multi-Section Whole Slide Image Analysis for Biochemical Recurrence Prediction in Prostate Cancer

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.20273 (cs) [Submitted on 17 Mar 2026] Title:Efficient AI-Driven Multi-Section Whole Slide Image Analysis for Biochemical Recurrence Prediction in Prostate Cancer Authors:Yesung Cho, Dongmyung Shin, Sujeong Hong, Jooyeon Lee, Seongmin Park, Geongyu Lee, Jongbae Park, Hong Koo Ha View a PDF of the paper titled Efficient AI-Driven Multi-Section Whole Slide Image Analysis for Biochemical Recurrence Prediction in Prostate Cancer, by Yesung Cho and 7 other authors View PDF Abstract:Prostate cancer is one of the most frequently diagnosed malignancies in men worldwide. However, precise prediction of biochemical recurrence (BCR) after radical prostatectomy remains challenging due to the multifocality of tumors distributed throughout the prostate gland. In this paper, we propose a novel AI framework that simultaneously processes a series of multi-section pathology slides to capture the comprehensive tumor landscape across the entire prostate gland. To develop this predictive AI model, we curated a large-scale dataset of 23,451 slides from 789 patients. The proposed framework demonstrated strong predictive performance for 1- and 2-year BCR prediction, substantially outperforming established clinical benchmarks. The AI-derived risk score was validated as the most potent independent prognostic factor in a multivariable Cox proportional hazards analysis, surpassing conventional clinical markers such as pre-operative P...