高精度的表皮生长因子受体突变预测通过遗传病学深度学习
Dan Zhao1, Yanli Zhao1, Sen He2
1Department of Pathology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
BMC pulmonary medicine
|July 5, 2023
概括
这项研究开发了一个深度学习模型,使用H&E染色的幻灯片来预测非小细胞肺癌中的表皮生长因子受体 (EGFR) 突变. 该模型显示了具有成本效益的预先查的潜力,有助于为具有有限组织样本的患者做出治疗决定.
科学领域:
- 在瘤学瘤学.
- 病理学 病理学 病理学
- 人工智能的人工智能
背景情况:
- 对表皮生长因子受体 (EGFR) 突变的准确检测对于指导非小细胞肺癌 (NSCLC) 氨酸激酶抑制剂治疗至关重要.
- 传统的EGFR突变检测依赖于组织样本,这些样本通常很难获得,可能会延迟或阻止治疗.
- 对于EGFR突变状态的预测,需要使用非侵入性或较少侵入性的方法.
研究的目的:
- 开发一个高精度的深度学习模型,用于预测EGFR突变状态,仅使用例行用血素和素 (H&E) 染色的幻灯片.
- 评估该模型在分类EGFR突变状态方面的表现及其作为预选工具的潜力.
主要方法:
- 基于ResNet-50的卷积神经网络被训练在226个H&E染色NSCLC幻灯片 (88个具有EGFR突变) 上.
- 该模型在100个独立的H&E染色NSCLC幻灯片 (50个EGFR突变) 上进行了测试.
- 进行了EGFR突变状态的幻灯片级分类.
主要成果:
- 该模型在EGFR突变预测方面获得了76%的灵敏度和74%的特异性 (AUC 0.82).
- 双值方法允许33%的患者被分类为100%的灵敏度和87.5%的特异性.
- 结合腺癌亚型信息,使得37.3%的腺癌患者的敏感度提高到100%.
结论:
- 使用H&E幻灯片的深度学习模型可以作为一种快速,具有成本效益的NSCLCEGFR突变预选工具.
- 这种方法可以补充分子检测方法,特别是对于组织可用性有限的患者.
- 该模型有可能通过促进及时的突变状态评估来扩大NSCLC患者的治疗机会.
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