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在高风险人群中最大化肺癌查利用ML开发的风险预测算法:肺Flag的丹麦追溯验证

Margrethe Bang Henriksen¹, Ole Hilberg², Christian Juul³

¹Department of Oncology, Vejle University Hospital, Vejle, Denmark; Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.

Clinical lung cancer

|July 1, 2025

概括

LungFlag人工智能模型对肺癌 (LC) 查有前景,在识别高风险个体,特别是慢性阻塞性肺病 (COPD) 患者方面表现优于现有的方法. 需要进一步的现实研究来确认其临床价值.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

早期肺癌 (LC) 检测对于治疗成功至关重要,但面临查挑战.
人工智能 (AI) 使用常规临床数据提供创新的风险分层.
肺Flag模型利用人工智能进行肺癌风险评估.

研究的目的:

为了验证LungFlag AI模型在丹麦高风险人群中的性能.
评估LungFlag在肺癌查方面的潜力.
将LungFlag与PLCOm2012风险预测模型进行比较.

主要方法:

从两个丹麦南部人口 (2013-2021) 的数据进行了回顾性分析.
包括LC快速治疗患者和慢性阻塞性肺病 (COPD) 门诊患者.
使用实验室结果,并发症,BMI和吸烟史进行 LungFlag 和 PLCOm2012 的比较;用 SHAP 值和年龄进行分析.

主要成果:

在A群中,LungFlag在PLCOm2012上表现优于PLCOm2012 (AUC:0.63对比0.60).
LungFlag在B群体中显示出稍微更高的灵敏度,但存在很小的差异.
确定的主要预测因素是吸烟,年龄和COPD; LungFlag确定了年轻的高风险个人.

结论:

LungFlag显示出作为肺癌检测决策支持工具的潜力,特别是在COPD患者中.
该模型可能有助于识别针对肺癌查的个体.
需要进行前性研究来确认LungFlag的实际有效性和临床实用性.

关键词:

人工智能的人工智能是人工智能.血液样本分析血液样本分析机器学习是机器学习.预测模型的预测模型.风险分层是指风险的分层.

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