AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912857617072128 |
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| author | Camilloni, Andrea Micoli, Chiara Mulliqi, Nita Palm, Erik Everett Palsdottir, Thorgerdur Szolnoky, Kelvin Ji, Xiaoyi Boman, Sol Erika Discacciati, Andrea Grönberg, Henrik Egevad, Lars Nordström, Tobias Kartasalo, Kimmo Eklund, Martin |
| author_facet | Camilloni, Andrea Micoli, Chiara Mulliqi, Nita Palm, Erik Everett Palsdottir, Thorgerdur Szolnoky, Kelvin Ji, Xiaoyi Boman, Sol Erika Discacciati, Andrea Grönberg, Henrik Egevad, Lars Nordström, Tobias Kartasalo, Kimmo Eklund, Martin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_21022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples Camilloni, Andrea Micoli, Chiara Mulliqi, Nita Palm, Erik Everett Palsdottir, Thorgerdur Szolnoky, Kelvin Ji, Xiaoyi Boman, Sol Erika Discacciati, Andrea Grönberg, Henrik Egevad, Lars Nordström, Tobias Kartasalo, Kimmo Eklund, Martin Computer Vision and Pattern Recognition 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. |
| title | AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.21022 |