Machine learning-based prognostic modeling for locally advanced non-small cell lung cancer treated with immuno-radiotherapy.

Background: Patients with locally advanced non-small cell lung cancer (NSCLC) who undergo concurrent chemoradiotherapy (CCRT) followed by consolidation immunotherapy show heterogeneous survival outcomes. Accurate prognostic prediction remains a major challenge in clinical practice. This study aimed to develop machine learning models to enhance personalized outcome prediction and guide precision immuno-radiotherapy. Methods: A total of 219 patients with locally advanced NSCLC were retrospectively enrolled. All patients received standard CCRT followed by consolidation immunotherapy. Prognostic variables were first selected using least absolute shrinkage and selection operator (LASSO) regression. A multivariate Cox proportional hazards model and a random survival forest (RSF) model were then constructed in the training cohort and validated in the independent cohort. Results: LASSO regression identified four prognostic variables: Age, T stage, Stage, and Pathology. Multivariate Cox analysis confirmed Stage and Pathology as independent predictors of OS. The Cox model achieved a C-index of 0.62 and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.748 and 0.736 for 1-and 2-year OS in the validation cohort. The RSF model demonstrated higher predictive accuracy, with a C-index of 0.67 and AUC-ROC of 0.79 and 0.78 for 1-and 2-year OS, respectively. Variable importance analysis indicated that Stage and Pathology were the most influential factors. Based on RSF-derived risk scores, patients were stratified into high-and low-risk groups, and the high-risk group showed significantly poorer survival. Conclusion: The RSF model demonstrated improved performance compared to the conventional Cox model in predicting survival and stratifying risk among patients with locally advanced NSCLC undergoing CCRT and consolidation immunotherapy.