一个人工智能模型用于核分类的清细胞细胞癌使用整个幻灯片图像:一个回顾性,多中心的诊断研究
Qingyuan Zheng1,2, Li Wei3, Yang Zhou4,5
1Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
International journal of surgery (London, England)
|May 13, 2025
概括
一个名为RIGDAS的AI系统准确地评级清细胞细胞癌 (ccRCC) ISUP核等级,提高病理学家的准确性并减少审查时间,以便更好地管理患者.
科学领域:
- 尿路病理学 尿路病理学
- 人工智能在医学中的应用
- 数字病理学数字病理学
背景情况:
- 国际泌尿病学学会 (ISUP) 核分级的准确病理评估对于管理清细胞细胞癌 (ccRCC) 至关重要.
- 现有的分级方法可能是主观的,耗时的.
- 在ccRCC病理学中需要有效和准确的诊断工具.
研究的目的:
- 开发和评估基于人工智能 (AI) 的系统,即细胞癌ISUP分级诊断系统 (RIGDAS),用于高效和高精度的ccRCCISUP分级.
- 评估RIGDAS在现实环境中的临床应用价值.
主要方法:
- 一项多中心的回顾性诊断研究包括来自中国三个医院和两个公共队列的1807名ccRCC患者.
- 病理幻灯片被数字化为整张幻灯片图像,用于AI模型开发和验证.
- 主要终点是接收器操作特征曲线 (AUC) 下的区域;人工智能辅助的性能和审查时间也被评估.
主要成果:
- 在训练和验证数据集中,RIGDAS实现了高诊断性能,AUC范围从0.943到0.980.
- 人工智能系统的准确性 (0.930) 超过了初级病理学家,并与高级病理学家相美.
- RIGDAS显著提高了初级病理学家对高级病理学家的准确性,并将所有病理学家的幻灯片审查时间减少了20.5-45.1%.
结论:
- 在ccRCC ISUP核分级中,RIGDAS系统显示出临床应用的巨大潜力.
- 它提高了诊断准确度,降低了误诊率,并优化了病理幻灯片审查效率.
- 人工智能辅助分级提供了一个有价值的工具,以支持病理学家在ccRCC管理.
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