比例危险模型
回归分析
肺癌
回归
癌症
生存分析
肿瘤科
人工智能
医学
统计
计算机科学
机器学习
内科学
数学
作者
Sebastian Germer,Christiane Rudolph,Louisa Labohm,Alexander Katalinic,Natalie Rath,Katharina Rausch,Bernd Holleczek,Heinz Handels
摘要
Introduction: Survival analysis based on cancer registry data is of paramount importance for monitoring the effectiveness of health care. As new methods arise, the compendium of statistical tools applicable to cancer registry data grows. In recent years, machine learning approaches for survival analysis were developed. The aim of this study is to compare the model performance of the well established Cox regression and novel machine learning approaches.Material and Methods: The study is based on lung cancer data from the Schleswig-Holstein Cancer Registry.Four survival analysis models are compared: Cox Proportional Hazard Regression as the most commonly used statistical model, as well as Random Survival Forests and two neural network architectures based on the DeepSurv and TabNet approaches.The models are evaluated using the concordance index (C-I), the Brier score and the AUC-ROC score. In addition, to gain more insight in the decision process of the models, we identified the features that have an higher impact on patient survival using permutation feature importance scores and SHAP values.Results: Using a dataset including the UICC stadium, the best performing model is the Cox Proportional Hazard Regression (C-I: 0.698 [[EQUATION]] 0.005), while using a dataset which includes the TNM classification leads to the Random Survival Forest as best performing model (C-I: 0.703 [[EQUATION]] 0.004).The explainability metrics show that the models rely on the UICC stadium and the metastasis status in the first place, which corresponds to other studies.Discussion: The studied methods are highly relevant for epidemiological researchers to create more accurate survival models, which can help physicians make informed decisions about appropriate therapies and management of patients with lung cancer, ultimately improving survival and quality of life.
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