使用节整体机器学习模型评估肺癌查资格:一项开发和验证研究
Thomas Callender1, Fergus Imrie2, Bogdan Cebere3
1Department of Respiratory Medicine, University College London, London, United Kingdom.
PLoS medicine
|October 3, 2023
概括
新的整体机器学习模型仅使用三个变量预测常吸烟者的肺癌风险. 这些节的模型简化了个性化肺癌查的资格,改善了当前的标准.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 确定符合基于风险的肺癌查资格是具有挑战性的.
- 集成机器学习为开发简单但有效的预测模型提供了一条道路.
- 个性化查方法对于广泛采用至关重要.
研究的目的:
- 开发和验证肺癌查资格的整体机器学习模型.
- 创建节的模型,以更少的预测器保持高性能.
主要方法:
- 利用英国生物库 (n=216,714) 和美国国家肺部查试验 (NLST,n=26,616) 的数据进行模型开发.
- 在美国前列腺,肺部,结肠直肠和卵巢 (PLCO) 查试验中外部验证的模型 (n=49,593).
- 开发模型以预测5年肺癌诊断和死亡风险,使用年龄,吸烟持续时间和包装年.
主要成果:
- 预测肺癌死亡率 (UCL-D) 和发病率 (UCL-I) 的模型仅使用了3个变量.
- 与现有模型相比,实现了可比或优越的歧视,校准和净收益.
- 在PLCO试验中,外部验证显示UCL-D AUC为0.803和UCL-I AUC为0.787.
- 模型在类似的特异性下显示出比USPSTF-2021标准更高的灵敏度.
结论:
- 节集团机器学习模型有效预测常吸烟者的肺癌风险.
- 这些模型为实施基于风险的肺癌查提供了一种简化方法.
- 开发的模型可以提高肺癌查计划的个性化和采用.
相关概念视频
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