从多机构队列中对病理学家注释数据集的案例进行优先排序
Victor Garcia1, Emma Gardecki1, Stephanie Jou2
1U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, United States of America.
Journal of pathology informatics
|December 25, 2024
概括
我们开发了一种方法来创建人工智能模型的验证数据集,以评估乳腺癌中的瘤透淋巴细胞. 这种方法优先考虑代表性不足的患者群体,以实现更公平的AI开发.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 人工智能的人工智能
背景情况:
- 标准化验证数据集对于在癌症研究中比较人工智能和机器学习 (AI/ML) 模型至关重要.
- 流体瘤透淋巴细胞 (sTILs) 是三阴性乳腺癌 (TNBC) 的重要预后标志物.
- 开发强大的AI/ML模型来评估STILs需要高质量,多样化的验证数据.
研究的目的:
- 为AI/ML模型创建一个全面的验证数据集,用于评估TNBC中的sTILs.
- 实施一种新的病例优先级方法,以确保各种患者子组的代表性.
- 为了方便在不同研究小组中直接比较AI/ML模型的性能.
主要方法:
- 从两个学术医疗中心获得TNBC核心活检的整片图像 (WSI) 和临床元数据.
- 精选的兴趣区域 (ROI) 针对不同的组织形态和sTILs密度.
- 实施了对病例进行优先排序的层次排序方法,专注于代表性不足的临床因素.
主要成果:
- 从105名独特的TNBC患者中收集了122个玻璃幻灯片的数据.
- 通过案例优先设置,改善了stILs密度分布的斜率从0.60到0.46.
- 将STILs密度容器的度从1.20增加到1.24,增强了数据多样性.
结论:
- 开发的优先级方法有效地提高了在验证数据集中代表代表性不足的患者子组的比例.
- 这种方法对于创建一个强大而公平的数据集来培训和验证TNBC研究中的AI/ML模型至关重要.
- 描述的方法方便在数字病理学中创建AI/ML模型开发的关键研究.
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