基于机器学习的预后模型,用于预测肺癌患者的局部控制,这些患者接受体内立体辐射疗法:一项初步研究
Bao-Tian Huang1, Pei-Xian Lin2, Ying Wang3
1Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, No.7 Raoping Road, Shantou, China. hbt830910@126.com.
European journal of medical research
|February 1, 2026
概括
机器学习显示了预测肺癌患者在立体体辐射疗法 (SBRT) 后局部复发的前景. 拉索-科克斯模型实现了最佳性能,超过了传统方法.
科学领域:
- 在瘤学瘤学.
- 辐射疗法 辐射疗法
- 机器学习 机器学习
背景情况:
- 预测肺癌的立体体辐射疗法 (SBRT) 后的局部复发对于患者的治疗结果至关重要.
- 现有的模型可能无法完全捕捉到预测局部控制 (LC) 的复杂性.
研究的目的:
- 开发和比较机器学习 (ML) 模型,用于预测SBRT治疗的肺癌患者的局部控制 (LC).
- 在这种情况下,确定用于预后建模的最佳ML算法.
主要方法:
- 对158名接受SBRT治疗的肺癌患者的临床数据的回顾性分析.
- 六种ML方法 (Boruta,RSF,GBM,LASSO,CoxBoost,单变Cox回归) 的系统应用和比较用于变量选择.
- 使用多变量考克斯回归构建预后模型,并与基准模型进行比较 (临床阶段+治疗剂量).
- 使用C指数,AIC,时间依赖的AUC,ROC分析,校准图表和DCA进行性能评估,并进行内部交叉验证.
主要成果:
- 拉索-科克斯模型表现出卓越的性能,具有最高的C指数 (0.718) 和时间依赖的AUC.
- 拉索-科克斯模型的表现优于常规阶段加治疗剂量模型 (0.718对比0.634).
- 拉索-科克斯模型表现出中度的区分能力,良好的校准和临床实用性.
结论:
- 机器学习有可能预测肺癌SBRT后的局部控制.
- 开发的LASSO-Cox模型显示出有希望的结果,但在临床实施之前需要在独立队列中进行外部验证.
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