Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. However, it remains underreported and subject to significant interobserver variability among pathologists.
We aimed to develop and validate an artificial intelligence (AI)-based system to improve cribriform pattern detection.
We created a deep learning model using an EfficientNetV2-S encoder with multiple instance learning for end-to-end whole-slide classification. The model was trained on 640 digitised prostate core needle biopsies from 430 patients, collected across three cohorts. It was validated internally (261 slides from 171 patients) and externally (266 slides, 104 patients from three independent cohorts). Internal validation cohorts included laboratories or scanners from the development set, while external cohorts used completely independent instruments and laboratories. Annotations were provided by three expert uropathologists with known high concordance.
We assessed model performance using the area under the receiver operating characteristic curve (AUC) and Cohen's κ with 95% confidence intervals calculated through bootstrapping. Additionally, we conducted an inter-rater analysis and compared the model's performance against nine expert uropathologists on 88 slides from the internal validation cohort.
The model showed strong internal validation performance (AUC: 0.97, 95% CI: 0.95, 0.99; Cohen's κ: 0.81, 95% CI: 0.72, 0.89) and robust external validation (AUC: 0.90, 95% CI: 0.86, 0.93; Cohen's κ: 0.55, 95% CI: 0.45, 0.64). In our inter-rater analysis, the model achieved the highest average agreement (Cohen's κ: 0.66, 95% CI: 0.57, 0.74), outperforming all nine pathologists whose Cohen's κ ranged from 0.35 to 0.62. Limitations include the retrospective design and that the cross-scanner reproducibility and inter-rater analyses were conducted exclusively on internal validation data, potentially overestimating performance in these analyses.
Our AI model demonstrates pathologist-level performance for cribriform morphology detection in prostate cancer. This approach could enhance diagnostic reliability, standardise reporting, and improve treatment decisions for patients with prostate cancer.
European urology open science. 2026 Apr 07*** epublish ***
Kelvin Szolnoky, Anders Blilie, Nita Mulliqi, Toyonori Tsuzuki, Hemamali Samaratunga, Matteo Titus, Xiaoyi Ji, Sol Erika Boman, Einar Gudlaugsson, Svein Reidar Kjosavik, José Asenjo, Marcello Gambacorta, Paolo Libretti, Marcin Braun, Radzisław Kordek, Roman Łowicki, Brett Delahunt, Kenneth A Iczkowski, Theo van der Kwast, Geert J L H van Leenders, Katia R M Leite, Chin-Chen Pan, Emiel Adrianus Maria Janssen, Martin Eklund, Lars Egevad, Kimmo Kartasalo
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden., Department of Pathology, Stavanger University Hospital, Stavanger, Norway., Department of Surgical Pathology, School of Medicine, Aichi Medical University, Nagoya, Japan., Aquesta Uropathology and University of Queensland, Brisbane, Queensland, Australia., The General Practice and Care Coordination Research Group, Stavanger University Hospital, Stavanger, Norway., Department of Pathology, SYNLAB, Madrid, Spain., Department of Pathology, SYNLAB, Brescia, Italy., Department of Pathology, Chair of Oncology, Medical University of Lodz, Lodz, Poland., 1st Department of Urology, Medical University of Lodz, Lodz, Poland., Malaghan Institute of Medical Research, Wellington, New Zealand., Department of Pathology and Laboratory Medicine, University of California - Davis Health, Sacramento, CA, USA., Laboratory Medicine Program and Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, ON, Canada., Department of Pathology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands., Department of Urology, Laboratory of Medical Research, University of São Paulo Medical School, São Paulo, Brazil., Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan., Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden., Department of Medical Epidemiology and Biostatistics, SciLifeLab, Karolinska Institutet, Stockholm, Sweden.