Backgrounds/Objectives: Non-small cell lung cancer (NSCLC) exhibits substantial prognostic heterogeneity that is not fully captured by conventional anatomical staging, highlighting the need for individualized risk assessment. Radiomics enables non-invasive characterization of tumor phenotype, yet high dimensionality and inter-feature correlations often limit model stability and interpretability. Methods: To address these challenges, we developed a multimodal late-fusion framework integrating radiomic, clinical, and demographic information to predict patient-specific absolute risk in the Lung1 cohort (N = 398). Radomic features (N = 107) were extracted from primary tumor volumes and refined using a Group Lasso–penalized Cox model, preserving biological coherence and producing a parsimonious imaging signature. This signature was combined with clinical and demographic variables using five different late-fusion strategies: weighted averaging, Cox regression, logistic stacking, Random Survival Forests (RSF), and XGBoost. Model performance was evaluated using 5-fold cross-validation based on discrimination, calibration, and risk stratification metrics. Results: Using 5-fold cross validation, the radiomics-only model outperformed conventional clinical staging in patients’ risk prediction (C-index 0.5717 vs. 0.5350) and accuracy, demonstrating the prognostic value of imaging biomarkers. All fusion strategies improved risk prediction performance, with the Cox fusion model slightly better than other fusion methods with C-index of 0.58, time-dependent AUC of 0.60, and the distinct risk stratification with log-rank χ2 of 22.85. Conclusions: These findings suggest that multimodal late fusion may provide robust and interpretable risk estimates with potential clinical relevance, supporting personalized risk prediction for informed decision-making in NSCLC.
Helforoush et al. (Wed,) studied this question.