自研AI系统可精准检测前列腺癌并优化病理流程，提升诊断效率与质量。
Development and prospective validation of a prostate cancer detection, grading, and workflow optimization system at an academic medical center
- 基于本地数据训练专用模型，实现癌症检测与分级自动化。
- 检测准确率98.5%（AUC），分级一致性达0.869（Cohen's kappa）。
- 适合高通量病理中心使用，助力诊断质控与资源优化。
人工智能可协助医疗系统在保持诊断质量的同时应对日益增长的病理科服务需求，缩短报告周期并降低成本。本研究评估了机构自研的前列腺癌检测、分级及工作流优化系统性能，并与商业方案对比。2021年8月至2023年3月间，共扫描1,147例患者、21,396张前列腺活检切片。开发了用于癌症检测、分级及可疑病例免疫组化（IHC）筛查的模型。在前瞻性收集的真实患者数据集上，比较了任务特定模型与通用基础模型的表现。同时评估了专为提升小病灶敏感性和低分辨率模式识别而设计的模型效果。结果显示，癌症检测与病理科医生标注高度一致（曲线下面积98.5%，敏感性95.0%，特异性97.8%），ISUP分级一致性为0.869（Cohen's kappa），≥Grade Group 3分类的曲线下面积为97.5%，敏感性94.9%，特异性96.6%。筛查模型能正确判断55%的需进行IHC的活检标本，错误率仅为1.4%。任务特定模型与基础模型在癌症检测上无显著差异，但前者体积更小、速度更快。拥有高扫描量和报告提取能力的学术医疗中心可自主开发高精度计算病理模型，用于质量控制、资源调配和流程优化，以应对未来前列腺癌诊断挑战。
原文摘要 · Abstract (English)
Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commercial alternatives. From August 2021 to March 2023, we scanned 21,396 slides from 1,147 patients receiving prostate biopsy. We developed models for cancer detection, grading, and screening of equivocal cases for IHC ordering. We compared the performance of task-specific prostate models with general-purpose foundation models in a prospectively collected dataset that reflects our patient population. We also evaluated the contributions of a bespoke model designed to improve sensitivity to small cancer foci and perception of low-resolution patterns. We found high concordance with pathologist ground-truth in detection (area under curve 98.5%, sensitivity 95.0%, and specificity 97.8%), ISUP grading (Cohen's kappa 0.869), grade group 3 or higher classification (area under curve 97.5%, sensitivity 94.9%, specificity 96.6%). Screening models could correctly classify 55% of biopsy blocks where immunohistochemistry was ordered with a 1.4% error rate. No statistically significant differences were observed between task-specific and foundation models in cancer detection, although the task-specific model is significantly smaller and faster. Institutions like academic medical centers that have high scanning volumes and report abstraction capabilities can develop highly accurate computational pathology models for internal use. These models have the potential to aid in quality control role and to improve resource allocation and workflow in the pathology lab to help meet future challenges in prostate cancer diagnosis.
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