Multimodal Deep Learning Integrating Tumor Radiomics and Mediastinal Adiposity Improves Survival Prediction in Non‐Small Cell Lung Cancer: A Prognostic Modeling Study

ABSTRACT Background and Purpose Prognostic stratification in non‐small cell lung cancer (NSCLC) presents considerable challenges due to tumor heterogeneity. Emerging evidence has proposed that adipose tissue may play a prognostic role in oncological outcomes. This study investigates the integration of deep learning (DL)–derived computed tomography (CT) imaging biomarkers with mediastinal adiposity metrics to develop a multimodal prognostic model for postoperative survival prediction in NSCLC patients. Methods A retrospective cohort of 702 surgically resected NSCLC patients was analyzed. Tumor radiomic features were extracted using a DenseNet121 convolutional neural network architecture, while mediastinal fat area (MFA) was quantified through semiautomated segmentation using ImageJ software. A multimodal survival prediction model was developed through feature‐level fusion of DL‐extracted tumor characteristics and MFA measurements. Model performance was evaluated using Harrell's concordance index (C‐index) and receiver operating characteristic (ROC) analysis. Risk stratification was performed using an optimal threshold derived from training data, with subsequent Kaplan–Meier survival curve comparison between high‐ and low‐risk cohorts. Results The DL‐based tumor model achieved C‐indices of 0.787 (95% CI: 0.742–0.832) for disease‐free survival (DFS) and 0.810 (95% CI: 0.768–0.852) for overall survival (OS) in internal validation. Integration of MFA with DL‐derived tumor features yielded a multimodal model demonstrating enhanced predictive performance, with C‐indices of 0.823 (OS) and 0.803 (DFS). Kaplan–Meier analysis revealed significant survival divergence between risk‐stratified groups (log‐rank p < 0.05). Conclusion The multimodal fusion of DL‐extracted tumor radiomics and mediastinal adiposity metrics represents a significant advancement in postoperative survival prediction for NSCLC patients, demonstrating superior prognostic capability compared to unimodal approaches.