A Computationally Efficient Multi-Section Whole Slide Image Analysis for Capturing Prostate Cancer Multifocality in Biochemical Recurrence Prediction

The study introduces a computationally efficient multi‑section whole slide image analysis framework that captures the entire prostate tumor landscape to predict biochemical recurrence (BCR) after radical prostatectomy. Using a large dataset of 23,451 slides from 789 patients, the authors optimized patch and slide subsampling to reduce computational demands while achieving strong predictive performance for 1‑ and 2‑year BCR, comparable to or better than existing clinical benchmarks. The AI‑derived risk score was an independent prognostic factor, improved prognostic performance when combined with clinical variables, and showed preliminary transferability in an external cohort, with attention‑dispersion analysis confirming that the model leveraged information from multiple sections consistent with prostate cancer’s multifocal nature.

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