Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT - Chen, Ningxin; Li, Ruikun; Jiang, Mengmeng; Guo, Yixian; Chen, Jiejun; Sun, Dazhen; Wang, Lisheng; Yao, Xiuzhong (Frontiers in Medicine, 2022)
Purposes/Objectives: The aim of this study was to predict progression-free survival (PFS) in small cell lung cancer (SCLC) patients by radiomic signature from contrast-enhanced computed tomography (CT). Methods: A total of 186 cases with pathological confirmed small cell lung cancer was retrospectively assembled. Firstly, 1218 radiomic features were automatically extracted from tumor ROIs on lung window and mediastinal window respectively. Then, the prognostic and robust features were selected by machine learning methods, including 1) univariate analysis based on Cox proportional hazard (CPH) model, 2) redundancy removing using the variance inflation factor (VIF) and 3) multivariate importance analysis based on random survival forests (RSF). Finally, the PFS predictive models were established based on RSF, and their performances were evaluated using the concordance index (C-index) and the cumulative/dynamic AUC (C/D AUC). Results: 11 radiomic features (6 for mediastinal window and 5 for lung window) were finally selected, and the predictive model constructed from them achieved a C-index of 0.7531 and a mean C/D AUC of 0.8487 on the independent test set, better than the predictions by single clinical features (C-index = 0.6026, mean C/D AUC = 0.6312), and single radiomic features computed in lung window (C-index = 0.6951, mean C/D AUC = 0.7836) or mediastinal window (C-index = 0.7192, mean C/D AUC = 0.7964). Conclusion: The radiomic features computed from tumor ROIs on both lung window and mediastinal window can predict progression-free survival for SCLC patients by a high accuracy, which could be used as a useful tool to support personalized clinical decision for diagnosis and patient management of SCLC patients.