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在胃肠道癌症中免疫检查点抑制剂反应的基于机器学习的预测模型

Yufan Lv^1,2, Qingbin Wang³, Huiting Xu⁴

¹Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China.

Frontiers in medicine

|November 3, 2025

概括

这项研究开发了一个可解释的机器学习模型,使用XGBoost来预测胃肠道癌症的免疫治疗反应. 该模型准确地识别了化疗和瘤阶段等关键因素,有助于个性化治疗决策.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

胃肠道 (GI) 癌症的预后不好,免疫治疗反应的预测因素有限.
准确的免疫疗法反应预测模型对于有效的临床决策至关重要.

研究的目的:

开发和验证一种机器学习模型,用于预测肠道癌症中免疫治疗反应率.
确定影响治疗结果的关键临床和分子特征.

主要方法:

这是一项506名胃肠道癌症患者的多中心回顾性研究.
利用了14个特征 (人口,临床,生化,遗传) 作为八个机器学习算法的输入.
采用极端梯度提升 (XGBoost) 以其卓越的预测性能,通过AUC,准确性和灵敏度等指标进行验证.
沙普利添加式解释 (SHAP) 分析用于模型解释性.

主要成果:

XGBoost模型的AUC值为0.829,准确度为78.43%,灵敏度为86.67%.
化疗 (28%的贡献) 和瘤阶段是最重要的预测因素.
血红蛋白,BMI,年龄和较低的中性粒细胞与淋巴细胞比率 (NLR) 等因素也对预测产生了积极的影响.

结论:

可解释的XGBoost模型准确地预测肠道癌症的免疫治疗反应.
个性化预测模型,由化疗和瘤阶段等因素提供信息,可以增强临床决策.
该模型为 GI 瘤学中的个性化治疗策略和风险管理提供了精确的支持.

关键词:

的胃肠道恶性瘤.免疫检查点抑制剂免疫检查点抑制剂机器学习是机器学习.预测模型是一个预测模型.治疗治疗治疗治疗治疗治疗

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