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在非小细胞肺癌中使用机器学习模型预测新辅助免疫疗法的疗效:系统性审查和元分析

Wenrui Liu¹, Zhenzhen Feng¹, Mingyao Zhang¹

¹Department of Respiratory Diseases, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou, China; Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-constructed by Henan Province & Education Ministry of P.R. China/Henan Key Laboratory of Chinese Medicine for Respiratory Diseases, Henan University of Chinese Medicine, Zhengzhou, China.

International journal of medical informatics

|February 20, 2026

概括

机器学习模型显示出预测非小细胞肺癌 (NSCLC) 治疗反应的前景. 然而,低质量和高偏差风险需要对这些预测模型进行谨慎的解释.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
放射学放射学是一门学科.

背景情况:

在可切除的非小细胞肺癌 (NSCLC) 中,新辅助免疫疗法反应非常可变.
机器学习 (ML) 提供了整合多模式数据以预测治疗结果的潜力.
缺乏对ML模型质量,偏差和临床适用性的系统评估.

研究的目的:

系统地评估ML模型中的方法质量和偏差风险,预测可切除NSCLC中的新辅助免疫疗法反应.
评估这些ML模型的诊断性能.
确定影响模型性能和适用性的因素.

主要方法:

在11个数据库中进行了系统的文献搜索.
使用probast+AI,IJMEDI和RQS检查清单进行数据提取和质量评估.
使用R软件对模型性能指标 (AUC,灵敏度,特异性) 的元分析.
基于预测因素,算法和结果的子组分析.

主要成果:

包含了使用44个ML模型的17项研究;89%的研究质量低,偏差高.
模型在样本大小,缺少数据处理和验证方面存在缺陷.
内部验证显示聚合AUC为0.786;外部验证AUC为0.760.
支持矢量机 (SVM) 和非辐射模型表现出卓越的性能.

结论:

ML模型有可能预测可切除的NSCLC中新辅助免疫疗法的疗效.
SVM和非放射学模型似乎是最有希望的.
方法上的局限性和偏差风险需要对临床翻译进行仔细的解释和未来的改进.

关键词:

有效性预测的预测.机器学习机器学习新辅助免疫疗法新辅助免疫疗法非小细胞肺癌是非小细胞肺癌.系统审查和元分析.

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