AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples

• URL: http://arxiv.org/abs/2601.21022v1
• Date: Wed, 28 Jan 2026 20:33:15 GMT
• Title: AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples
• Authors: Andrea Camilloni, Chiara Micoli, Nita Mulliqi, Erik Everett Palm, Thorgerdur Palsdottir, Kelvin Szolnoky, Xiaoyi Ji, Sol Erika Boman, Andrea Discacciati, Henrik Grönberg, Lars Egevad, Tobias Nordström, Kimmo Kartasalo, Martin Eklund,
• Abstract summary: Biochemical recurrence (BCR) after radical prostatectomy is a surrogate marker for aggressive prostate cancer.<n>We trained an AI-based model on diagnostic prostate biopsy slides to predict patient-specific risk of BCR.
• Score: 0.012216782333971603
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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