Deep Learning From Routine Histology Improves Risk Stratification for Biochemical Recurrence in Prostate Cancer

TL;DR

This study introduces a deep learning model that analyzes routine prostate cancer histology slides to accurately predict individual risk of biochemical recurrence, outperforming traditional clinical scores and revealing new prognostic tissue patterns.

Contribution

The paper presents a novel deep learning biomarker trained on histopathology images that enhances risk prediction for prostate cancer recurrence beyond existing models.

Findings

Deep learning model improves BCR risk prediction across cohorts.

Integration with clinical scores enhances prognostic accuracy.

Model uncovers subtle histomorphological patterns linked to recurrence.

Abstract

Accurate prediction of biochemical recurrence (BCR) after radical prostatectomy is critical for guiding adjuvant treatment and surveillance decisions in prostate cancer. However, existing clinicopathological risk models reduce complex morphology to relatively coarse descriptors, leaving substantial prognostic information embedded in routine histopathology underexplored. We present a deep learning-based biomarker that predicts continuous, patient-specific risk of BCR directly from H&E-stained whole-slide prostatectomy specimens. Trained end-to-end on time-to-event outcomes and evaluated across four independent international cohorts, our model demonstrates robust generalization across institutions and patient populations. When integrated with the CAPRA-S clinical risk score, the deep learning risk score consistently improved discrimination for BCR, increasing concordance indices from 0.725-0.772 to 0.749-0.788 across cohorts. To support clinical interpretability, outcome-grounded analyses revealed subtle histomorphological patterns associated with recurrence risk that are not captured by conventional clinicopathological risk scores. This multicohort study demonstrates that deep learning applied to routine prostate histopathology can deliver reproducible and clinically generalizable biomarkers that augment postoperative risk stratification, with potential to support personalized management of prostate cancer in real-world clinical settings.