Improved DeepSurv model for survival analysis in lung cancer and exploration of influencing factors
10.3969/j.issn.1005-202X.2025.06.019
- VernacularTitle:基于改进DeepSurv模型的肺癌生存分析及其影响因素
- Author:
Qiyang ZHAO
1
;
Xu ZHAO
;
Ying ZHANG
;
Manman KUANG
;
Qun XI
Author Information
1. 甘肃中医药大学医学信息工程学院,甘肃 兰州 730000
- Publication Type:Journal Article
- Keywords:
lung cancer;
survival analysis;
deep learning;
improved DeepSurv model;
influencing factor
- From:
Chinese Journal of Medical Physics
2025;42(6):832-840
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the performance of an improved DeepSurv model for survival analysis in lung cancer patients,and investigate key factors affecting the prognosis of lung cancer.Methods The lung cancer data from the SEER database(2018-2021)was used in the study,and the DeepSurv model was optimized by incorporating a self-attention mechanism,a residual network,a LIME module and an entropy regularization term to enhance prediction accuracy and interpretability.Model performance was assessed using C-index and Brier score,and the improved model was utilized to evaluate the effects of various features on the prognosis of lung cancer.Results The improved DeepSurv model achieved a C-index of 0.852 and a Brier score of 0.139.Feature importance analysis identified age as the primary determinant of the survival of lung cancer patients.Conclusion The improved DeepSurv model outperforms both the Cox proportional hazards model and the original DeepSurv model in terms of accuracy,robustness and interpretability,offering a novel methodology for personalized medicine and survival analysis.
