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肺癌患者的生存分析:考克斯回归和机器学习模型的比较
Sebastian Germer1, Christiane Rudolph2, Louisa Labohm3
1German Research Center for Artificial Intelligence (DFKI), Ratzeburger Allee 160, 23562 Lübeck, Germany.
International journal of medical informatics
|August 29, 2024
概括
这项研究将考克斯回归和机器学习用于肺癌生存率分析进行了比较. 随机生存森林使用TNM分期表现最好,突出了准确分期对于改善患者结果的重要性.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 对癌症注册数据的生存分析对于医疗监测至关重要.
- 机器学习方法正在越来越多地被开发用于生存分析.
- 将已建立的统计模型与新型机器学习方法进行比较至关重要.
研究的目的:
- 为了比较考克斯回归和机器学习模型的性能,用于肺癌生存率分析.
- 在以前未使用的癌症注册数据集上评估模型性能.
- 评估不同生存分析模型的可解释性.
主要方法:
- 利用了来自施莱斯维格-霍尔斯坦癌症登记处的肺癌数据.
- 我们比较了Cox比例危险回归 (CoxPH),随机生存森林 (RSF),DeepSurv和TabNet.
- 使用一致性指数 (C-I),布里尔分数和AUC-ROC评估模型;使用SHAP值来确定特征的重要性.
主要成果:
- 考克斯PH的最佳表现是使用国际癌症控制联盟 (UICC) 的分期 (C-I:0.698±0.005).
- 随机生存森林 (RSF) 在瘤,结节,转移 (TNM) 阶段表现最好 (C-I:0.703±0.004).
- 可解释性指标表明依赖于联合UICC阶段和转移状态.
结论:
- 研究的生存分析方法对流行病学研究具有高度相关性.
- 准确的生存模型可以帮助医生为肺癌患者做出明智的治疗决策.
- 改进决策有潜力提高患者的生存率和生活质量.
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