使用SHAP和机器学习方法解释差异化甲状腺癌的风险分层.
Mallika Khwanmuang1, Watcharaporn Cholamjiak2, Pasa Sukson1
1School of Medicine, University of Phayao, Phayao 56000, Thailand.
Biomedicines
|December 30, 2025
概括
这项研究开发了一种可解释的机器学习模型,用于差异化甲状腺癌 (DTC) 复发风险分层. 该模型使用关键临床特征准确预测风险,减少对主观病理的依赖.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 差异化甲状腺癌 (DTC) 具有很高的复发率 (30%在10年内),尽管预后普遍有利.
- 目前的风险分层依赖于病理解释,容易观察观察者的变化和不完整的数据.
- 开发客观,数据驱动的风险评估工具对于有效的DTC管理至关重要.
研究的目的:
- 为DTC复发风险分层创建一个可解释的机器学习框架.
- 通过SHapley添加式扩展 (SHAP) 来识别DTC复发的关键临床预测因素.
- 为了提高临床透明度,并支持个性化的DTC管理.
主要方法:
- 分析了345名DTC患者的回顾性数据集.
- 评估了临床病理特征,使用ReliefF和mRMR进行特征选择.
- 一个可优化的神经网络分类器被训练并使用SHAP进行特征归属的评估.
主要成果:
- 将特征减少到6 (T,N,响应,年龄,M,Hx放射治疗) 改善了模型性能 (AUC=0.94,精度=92%).
- SHAP分析发现N和T是高风险分类的主要驱动因素.
- 该模型即使没有术后反应数据,也显示出强大的预测性能,使得术前风险估计成为可能.
结论:
- 一个可解释的神经网络模型有效地分层DTC复发风险,最大限度地减少对主观病理的依赖.
- SHAP分析提供了临床透明度,有助于个性化甲状腺癌的随访.
- 可解释的机器学习为DTC中客观风险评估提供了一个有希望的方法.
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