Combining radiomics and deep learning to predict overall survival in non-small cell lung cancer patients(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第11期
- Page:
- 1462-1468
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Combining radiomics and deep learning to predict overall survival in non-small cell lung cancer patients
- Author(s):
- LIU Yongxin1; 2; WANG Qiusheng3; JIANG Huayong2; LU Na2; CHEN Diandian2; YU Yanjun2; GAO Yanxiang2; ZHANG Huijuan2; DENG Minmin2; SUN Yinglun1; ZHANG Fuli2
- 1. School of Radiology, Shandong First Medical University (Shandong Academy of Medical Sciences), Taian 271000, China 2. Department of Radiation Oncology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China 3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
- Keywords:
- Keywords: non-small cell lung cancer computed tomography radiomics deep learning overall survival
- PACS:
- R318；R734.2
- DOI:
- DOI:10.3969/j.issn.1005-202X.2025.11.009
- Abstract:
- 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.
Last Update: 2025-12-01