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

TL;DR

This paper introduces an AI framework for analyzing multiple pathology slides to accurately predict biochemical recurrence in prostate cancer patients, outperforming traditional clinical markers and reducing computational costs.

Contribution

The study presents a novel multi-section slide analysis AI model with large-scale data, demonstrating superior predictive performance and cost-effective strategies for prostate cancer prognosis.

Findings

Strong prediction of 1- and 2-year BCR risk

AI-derived risk score outperforms clinical markers

Cost reduction via patch and slide sampling strategies

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 PSA and Gleason score. Furthermore, we demonstrated that integrating patch and slide sub-sampling strategies significantly reduces computational cost during both training and inference without compromising predictive performance, and generalizability of AI was confirmed through external validation. Collectively, these results highlight the clinical feasibility and prognostic value of the proposed AI-based multi-section slide analysis as a scalable tool for post-operative management in prostate cancer.