首个中东人群前列腺癌AI诊断验证，性能媲美病理医生。
Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohort
- 用中东地区339份活检样本测试AI模型，覆盖三种扫描仪。
- AI与病理医生分级一致性达0.801，跨扫描仪一致性超0.90。
- 低成本便携扫描仪可用，适合资源有限实验室推广。
人工智能正提升癌症诊断效率与准确率，但现有病理AI系统评估多集中于欧美大型中心。为推动全球病理AI应用，亟需在代表性不足的人群中开展验证研究，而这些地区可能正是最需要AI支持的区域。本研究首次基于中东地区（伊拉克库尔德斯坦）的外部验证队列，评估了基于AI的前列腺癌诊断与格里森分级表现。研究收集并数字化了2013-2024年间185例患者的339份前列腺活检样本。评估了一个专用端到端AI模型及两个基础模型在与病理医生的一致性，以及在三种扫描仪（佳能、徕卡、格鲁尼姆）上数字化样本的稳定性。结果显示，AI与病理医生之间的分级一致性（加权科恩κ=0.801）与病理医生间一致性（0.799）相当（p=0.9824）。所有AI模型在不同扫描仪间均表现出高一致性（加权科恩κ > 0.90），包括低成便携式扫描仪。结果表明，该AI模型在前列腺组织病理学评估中达到病理医生水平。紧凑型扫描仪可为非数字化环境提供验证路径，并实现小样本量实验室的低成本AI部署。这是首个公开的中东数字病理数据集，有助于推动全球公平的病理AI研究。
原文摘要 · Abstract (English)
Background: Artificial intelligence (AI) is improving the efficiency and accuracy of cancer diagnostics. The performance of pathology AI systems has been almost exclusively evaluated on European and US cohorts from large centers. For global AI adoption in pathology, validation studies on currently under-represented populations - where the potential gains from AI support may also be greatest - are needed. We present the first study with an external validation cohort from the Middle East, focusing on AI-based diagnosis and Gleason grading of prostate cancer. Methods: We collected and digitised 339 prostate biopsy specimens from the Kurdistan region, Iraq, representing a consecutive series of 185 patients spanning the period 2013-2024. We evaluated a task-specific end-to-end AI model and two foundation models in terms of their concordance with pathologists and consistency across samples digitised on three scanner models (Hamamatsu, Leica, and Grundium). Findings: Grading concordance between AI and pathologists was similar to pathologist-pathologist concordance with Cohen's quadratically weighted kappa 0.801 vs. 0.799 (p=0.9824). Cross-scanner concordance was high (quadratically weighted kappa > 0.90) for all AI models and scanner pairs, including low-cost compact scanner. Interpretation: AI models demonstrated pathologist-level performance in prostate histopathology assessment. Compact scanners can provide a route for validation studies in non-digitalised settings and enable cost-effective adoption of AI in laboratories with limited sample volumes. This first openly available digital pathology dataset from the Middle East supports further research into globally equitable AI pathology. Funding: SciLifeLab and Wallenberg Data Driven Life Science Program, Instrumentarium Science Foundation, Karolinska Institutet Research Foundation.
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