AI识别前列腺癌筛状结构，准确率媲美病理专家。
Finding Holes: Pathologist Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer
- 用深度学习模型分析全切片图像，自动检测筛状结构。
- 内部验证AUC达0.97，外部验证AUC为0.90，性能稳定。
- 比9位专家更一致，适合临床辅助诊断和标准化报告。
筛状结构是前列腺癌中预后较差的组织学特征，常导致主动监测被禁忌。然而该特征易被漏诊，且病理医生间存在显著差异。本研究开发并验证了一种基于AI的系统以提升筛状结构检测能力。采用EfficientNetV2-S编码器结合多实例学习，对640例来自430名患者的数字化穿刺活检样本进行训练，涵盖三个队列。在261张内部验证切片（171名患者）及266张外部验证切片（104名患者）上评估，后者来自完全独立的实验室与扫描仪。标注由三位高一致性专家泌尿病理学家提供。此外，在88张内部验证切片上对比模型与九位专家的表现。结果显示，模型内部验证表现优异（AUC: 0.97，95% CI: 0.95-0.99；Cohen's kappa: 0.81，95% CI: 0.72-0.89），外部验证也具鲁棒性（AUC: 0.90，95% CI: 0.86-0.93；Cohen's kappa: 0.55，95% CI: 0.45-0.64）。在多评者分析中，模型平均一致性最高（Cohen's kappa: 0.66，95% CI: 0.57-0.74），优于所有专家（kappa范围0.35至0.62）。结论表明，该AI模型在筛状结构检测上达到病理医生水平，有助于提升诊断可靠性、实现报告标准化，并优化治疗决策。
原文摘要 · Abstract (English)
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.
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