成人类型的扩散性质瘤的神经病理学家水平的综合分类,使用全幻灯片病理图像的深度学习
Weiwei Wang1, Yuanshen Zhao2,3, Lianghong Teng4
1Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Nature communications
|October 11, 2023
概括
这项研究引入了一种人工智能模型,用于使用全幻灯片图像对扩散性质瘤进行分类,从而简化诊断. 该模型准确识别瘤类型,等级和基因型,为临床使用提供了潜力.
科学领域:
- 神经瘤学神经瘤学
- 计算病理学计算病理学
- 医学中的人工智能
背景情况:
- 目前的质瘤诊断依赖于昂贵且耗时的组织学和分子分析.
- 从全幻灯片图像 (WSIs) 进行扩散性质瘤的自动分类对于有效的诊断至关重要.
研究的目的:
- 开发和验证一个集成的人工智能模型,用于从没有注释的WSIs直接对扩散性质瘤进行自动分类.
- 评估模型在分类瘤类型,等级和基因型方面的表现.
主要方法:
- 在大型队列 (n=1362) 上训练并在多个队列 (共1262个WSI) 上验证的综合诊断模型的开发.
- 该模型学习了来自WSIs的综合成像功能,包括病理形态和生物信息.
- 在不同分类任务中使用接收机操作员曲线 (AUC) 下的面积进行性能评估.
主要成果:
- 该模型实现了高性能,主要瘤类型分类的AUC值超过0.90.
- 在确定特定类型内的瘤等级方面观察到出色的表现.
- 该模型在区分具有相似组织学特征的瘤基因型方面表现出特别强大.
结论:
- 综合人工智能诊断模型为成年类型扩散质瘤的自动和公正分类提供了一个有希望的方法.
- 这项技术有可能简化临床工作流程并提高诊断准确性.
- 该模型能够整合WSIs的形态和生物线索,这代表了数字病理学的重大进步.
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