基于多参数MRI的机器学习模型,用于区分乳腺癌中的HER2表达水平
Yongxin Chen1,2, Weifeng Liu2, Wenjie Tang2
1The First School of Clinical Medicine, Jinan University, Guangzhou 510632, China.
Current oncology (Toronto, Ont.)
|January 27, 2026
概括
使用MRI特征的机器学习模型可以预测乳腺癌中的HER2表达. 这些模型显示出非侵入性分层患者为无病生存 (DFS) 的潜力.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- HER2表达是乳腺癌预后和治疗的关键因素.
- 准确的HER2评估对于个性化治疗至关重要.
- 目前用于HER2评估的方法可能是侵入性的或耗时的.
研究的目的:
- 开发和验证机器学习模型,使用传统的MRI特征来对侵入性乳腺癌中的HER2表达水平进行分类.
- 探索MRI衍生的HER2分类与无病生存率 (DFS) 之间的关联.
- 为了提高模型的可解释性,使用夏普利添加式解释.
主要方法:
- 从两个中心的678名患者使用BI-RADS基于MRI特征的随机森林模型的开发.
- 模型被训练来区分HER2-阳性与HER2-阴性以及HER2-低与HER2-零瘤.
- 使用SHapley添加式扩展分析了特征的重要性;使用Kaplan-Meier曲线和日志等级测试评估了DFS.
主要成果:
- 模型的AUC为0.75 (内部) 和0.73 (外部) 的HER2-阳性与HER2-阴性分类.
- 模型的AUC为0.73 (内部) 和0.72 (外部) HER2-低与HER2-零分类.
- 较高的模型分数与任务1中较短的DFS (p=0.037) 和任务2中较长的DFS (p=0.046) 相相关.
结论:
- 基于传统MRI特征的机器学习模型可以非侵入性地对侵入性乳腺癌中的HER2表达水平进行分类.
- 这些模型显示出基于HER2状态的患者预后分层的潜力.
- 这些发现表明,基于MRI的AI在乳腺癌管理的临床决策中可能发挥作用.
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