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预测肺癌查不遵守的机会:系统性审查和元分析

Yannan Lin¹, Ruiwen Ding¹, Drew Moghanaki²

¹Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.

Journal of the American College of Radiology : JACR

|December 3, 2025

概括

机器学习显示了预测肺癌查不遵守的前景. 需要使用大型国家数据库进行进一步的开发,以创建定制的干预措施并改善患者的治疗结果.

科学领域:

医疗信息学医疗信息学
公共卫生公共卫生
在瘤学瘤学.

背景情况:

对肺癌查 (LCS) 的遵守率低,限制了其在降低死亡率方面的有效性.
为设计有针对性的干预措施,对不遵守的个性化风险预测至关重要.
机器学习 (ML) 提供了预测LCS不坚持风险的潜力.

研究的目的:

系统地审查和对ML模型的文献进行元分析,以预测LCS不坚持风险.
评估目前的ML状态,以识别高风险的LCS不坚持的个体.

主要方法:

在PubMed,Embase和Web of Science的系统文献搜索 (2014年4月 - 2025年5月).
提取研究特征,不合规数据和预测模型性能.
预测模型性能的元分析 (接收器操作特征曲线下的区域).

主要成果:

包括9项研究 (2020-2025),样本大小各不相同 (168-28,294).
在三种种群体中,聚合交叉验证的AUC为0.80 (95% CI,0.64-0.90),具有很高的异质性.
探索使用国家数据库用于未来ML模型开发的可行性.

结论:

目前用于预测LCS不坚持的ML模型开发不足.
大型,多中心的国家数据库对于开发强大的预测模型至关重要.
需要投资来创建模型,以识别需要量身定制的坚持干预措施的患者.

关键词:

肺癌查肺癌查肺癌查这是一个元分析.没有遵守的不遵守.风险评估风险评估风险评估系统性审查系统性审查

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