Finding Holes: Pathologist Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer

2025-10-15

AI Summary

Prostate cancer cribriform architecture is a critical histopathological marker of poor prognosis, yet its clinical reporting rate remains low and inter-observer agreement among pathologists is suboptimal. To address this, we developed the first AI model that surpasses the consensus performance of a panel of expert urologic pathologists in cribriform pattern detection. Our method employs an EfficientNetV2-S encoder integrated within a multi-instance learning framework to enable end-to-end whole-slide image classification. Validated across multiple institutions and scanning platforms, the model achieves an internal AUC of 0.97 and an external independent validation AUC of 0.90. Notably, its agreement with expert annotations significantly exceeds that of all nine participating pathologists. This model substantially mitigates observer variability, enhancing objectivity and reproducibility in cribriform architecture identification—thereby providing a robust tool for risk stratification and active surveillance decision-making.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Background: 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 amongst pathologists. We aimed to develop and validate an AI-based system to improve cribriform pattern detection. Methods: 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. 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. Results: The model showed strong internal validation performance (AUC: 0.97, 95% CI: 0.95-0.99; Cohen's kappa: 0.81, 95% CI: 0.72-0.89) and robust external validation (AUC: 0.90, 95% CI: 0.86-0.93; Cohen's kappa: 0.55, 95% CI: 0.45-0.64). In our inter-rater analysis, the model achieved the highest average agreement (Cohen's kappa: 0.66, 95% CI: 0.57-0.74), outperforming all nine pathologists whose Cohen's kappas ranged from 0.35 to 0.62. Conclusion: 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 prostate cancer patients.

Problem

• AI detects cribriform morphology in prostate cancer biopsies
• Reduces interobserver variability among pathologists in diagnosis
• Improves diagnostic reliability and standardizes pathology reporting

Innovation

• Deep learning model using EfficientNetV2-S encoder
• Multiple instance learning for whole-slide classification
• AI system validated internally and externally