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免疫治疗在驱动基因负的非小细胞肺癌中疗效的预测建模

Xipeng Tao¹, Yufang Chen², Yumo Zhong³

¹Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Army Medical University, Chongqing, China.

Translational lung cancer research

|March 11, 2026

概括

一个新的机器学习模型准确地预测了免疫治疗在非小细胞肺癌 (NSCLC) 的有效性. 确定免疫疗法没有持久益处 (NDB) 的患者在添加放射治疗后表现得更好.

科学领域:

在瘤学瘤学.
免疫治疗是一种免疫疗法.
机器学习机器学习

背景情况:

免疫疗法与化疗相结合,为驱动基因负的非小细胞肺癌 (NSCLC) 提供了好处.
预测免疫疗法反应仍然具有挑战性,需要多维方法.
放射治疗可以提高免疫反应不佳的患者的生存率.

研究的目的:

开发一种在NSCLC中免疫治疗有效性的预测模型.
为了确定免疫疗法没有持久益处的患者.
为了比较接受免疫疗法,有或没有放射治疗的患者的生存结果.

主要方法:

利用机器学习算法来构建NDB的预测模型.
采用后勤回归来选预测变量.
使用ROC-AUC,PR-AUC,校准曲线和DCA评估模型性能.

主要成果:

一个LightGBM模型显示出高的预测准确性 (ROC-AUCtrain=0.858,ROC-AUCall=0.852).
瘤阶段,TMB和Ki-67是预后的独立预测因素.
接受组合治疗 (放射治疗+免疫治疗) 的患者的NDB率显著降低 (29.6% vs 64.0%),PFS改善 (9.6 vs 5.6 个月).

结论:

开发了一种强大的免疫疗法在NSCLC中的有效性预测模型.
该模型准确地识别了可能因免疫治疗而患有NDB的患者.
对于已确定的NDB患者,将放射治疗与一线治疗相结合,可以改善治疗效果和生存率.

关键词:

非小细胞肺癌 (NSCLC) 是一种非小细胞肺癌.免疫疗法免疫疗法预测模型预测模型辐射疗法辐射疗法

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