Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples
Organizations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden · Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden · Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden · Department of Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden · Department of Medical Epidemiology and Biostatistics, SciLifeLab, Karolinska Institutet, Stockholm, Sweden · Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy · Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy · Division of Pathology, AOU Città Della Salute e Della Scienza di Torino, Turin, Italy · Department of Medical Sciences, University of Turin, Torino, Italy · Cancer Epidemiology Unit, University Hospital Città della Scienza e della Salute di Torino and CPO-Piemonte, Torino, Italy · Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
Abstract
Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preservation over prolonged time periods remains poorly understood. We evaluated GleasonAI, an end-to-end attention-based multiple instance learning model, on an independent validation cohort comprising 10,366 biopsy cores from 1,028 patients across 14 Swedish regions, using archival diagnostic specimens from the ProMort cohorts collected between 1998-2015. The model achieved an overall quadratic-weighted kappa of 0.86 for core-level ISUP grading, comparable to several experienced pathologists and consistent across geographic regions. Notably, performance remained stable across the 17-year collection period, demonstrating robustness to time-related variation in archival material, a property not consistently observed with foundation model-based approaches, with exploratory analysis demonstrating a significant prognostic gradient across AI-assigned grade groups for prostate cancer-specific mortality. These findings support the generalizability of the AI grading model and demonstrate the potential of pathology archives as a large-scale resource for AI development, validation, and retrospective prognostic research.