Combining radiomics and deep learning to predict overall survival in non-small cell lung cancer patients
10.3969/j.issn.1005-202X.2025.11.009
- VernacularTitle:融合影像组学与深度学习预测非小细胞肺癌患者的总生存期
- Author:
Yongxin LIU
1
;
Qiusheng WANG
;
Huayong JIANG
;
Na LU
;
Diandian CHEN
;
Yanjun YU
;
Yanxiang GAO
;
Huijuan ZHANG
;
Minmin DENG
;
Yinglun SUN
;
Fuli ZHANG
Author Information
1. 山东第一医科大学(山东省医学科学院)放射学院,山东 泰安 271000;解放军总医院第七医学中心放射治疗科,北京 100700
- Publication Type:Journal Article
- Keywords:
non-small cell lung cancer;
computed tomography;
radiomics;
deep learning;
overall survival
- From:
Chinese Journal of Medical Physics
2025;42(11):1462-1468
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.
