可解释的多式核磁共振扫描 (MRI) 放射学用于预测鼻癌中新辅助化疗反应
Tian-Cheng Lin1, Kai-Jie Wang1, Wen-Zhu Li1
1Department of Radiology, Hainan Affliated Hospital of Hainan Medical University (Hainan General Hospital), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, P.R. China.
BMC medical imaging
|November 26, 2025
概括
多模式MRI放射学,特别是动态对比增强MRI,有效预测鼻癌 (NPC) 治疗反应. 深度学习模型显示出卓越的性能,有助于临床决策.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 鼻癌 (NPC) 需要有效预测新辅助化疗 (NAC) 反应.
- 多模态MRI放射学为治疗疗效的非侵入性评估提供了潜力.
研究的目的:
- 评估一种多式核磁共振磁力共振模型,用于预测NPC中NAC反应.
- 在这个预测任务中比较机器学习和深度学习模型的性能.
主要方法:
- 从370名NPC患者的T1WI,PDWI,CE-T1WI和DCE-MRI序列中提取了放射性特征.
- 开发了机器学习和深度学习模型,并使用ROC曲线进行比较.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 具有DCE-MRI特征的深度学习模型实现了AUC为0.885,具有79.6%的灵敏度和83.7%的特异性.
- SHAP分析强调了DCE-MRI特征的显著贡献,特别是从后期增强阶段.
结论:
- 多模式MRI放射学,特别是DCE-MRI,准确地预测了NPC中的NAC反应.
- 开发的模型,可以通过SHAP解释,显示在NPC管理中临床应用的希望.
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