AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples
This work addresses imprecise prognostication for prostate cancer patients, offering a potentially useful tool for clinical decision-making, though it is incremental in nature.
The study tackled the problem of predicting biochemical recurrence in prostate cancer patients by training an AI model on biopsy slides, achieving time-dependent AUCs of 0.64 to 0.70 across external cohorts and showing incremental improvement over existing prognostic tools.
Biochemical recurrence (BCR) after radical prostatectomy (RP) is a surrogate marker for aggressive prostate cancer with adverse outcomes, yet current prognostic tools remain imprecise. We trained an AI-based model on diagnostic prostate biopsy slides from the STHLM3 cohort (n = 676) to predict patient-specific risk of BCR, using foundation models and attention-based multiple instance learning. Generalizability was assessed across three external RP cohorts: LEOPARD (n = 508), CHIMERA (n = 95), and TCGA-PRAD (n = 379). The image-based approach achieved 5-year time-dependent AUCs of 0.64, 0.70, and 0.70, respectively. Integrating clinical variables added complementary prognostic value and enabled statistically significant risk stratification. Compared with guideline-based CAPRA-S, AI incrementally improved postoperative prognostication. These findings suggest biopsy-trained histopathology AI can generalize across specimen types to support preoperative and postoperative decision making, but the added value of AI-based multimodal approaches over simpler predictive models should be critically scrutinized in further studies.