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

2026-03-17

AI Summary

Prostate cancer is inherently multifocal, complicating accurate prediction of biochemical recurrence (BCR) risk after radical prostatectomy. To address this challenge, this study proposes the first AI framework capable of jointly modeling multiple whole-slide pathology images across the entire prostate gland. By employing efficient patch- and slide-level subsampling strategies, the model comprehensively captures tumor heterogeneity while substantially reducing computational costs. Trained on 23,451 whole-slide images from 789 patients, the framework significantly outperforms standard clinical metrics in predicting 1-year and 2-year BCR. The derived AI-based risk score emerges as the strongest independent prognostic factor and demonstrates robust generalizability in external validation cohorts.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• prostate cancer
• biochemical recurrence
• multi-section whole slide image
• prognostic prediction
• tumor multifocality

Innovation

• multi-section whole slide image analysis
• biochemical recurrence prediction
• computational efficiency optimization
• prostate cancer prognosis
• AI-driven pathology