Abstract
Abstract
Objective
To develop and validate a machine learning radiomics model integrating automated three-dimensional body composition and tumor imaging features for predicting overall survival in resectable non–small cell lung cancer (NSCLC).
Materials and methods
This multicenter retrospective study included patients with resectable NSCLC treated between January 2013 and December 2017, who were assigned to training, internal, and external validation cohorts. A fully automated deep learning algorithm was developed for body composition segmentation. Radiomic features from tumor and body composition were extracted and integrated using extreme gradient boosting. Model performance was assessed using the concordance index (C-index) and time-dependent area under the curve (AUC), with interpretability evaluated by SHapley Additive exPlanations (SHAP). Kaplan–Meier analysis was performed for survival stratification.
Results
Among 1,038 patients (mean age, 61.8 ± 10.7 years; 58.66% male), 293 (28.2%) died over a median follow-up of 3.31 years. In the training cohort, both tumor score and body composition score were independently associated with overall survival (hazard ratio 2.72 and 2.03, respectively; all
p
p
0.80). SHAP analysis identified tumor score and body composition score as dominant predictors, stratifying patients into four phenotypes with distinct prognoses (all log-rank
p
Conclusion
Integrating automated three-dimensional body composition with tumor radiomics enhances survival prediction and provides incremental value for postoperative risk stratification in resectable NSCLC.
Key Points
Question
Standard staging for resectable non-small cell lung cancer lacks objective three-dimensional quantification of host body composition, thereby limiting individualized prognostic assessment.
Findings
Machine learning models integrating automated three-dimensional body composition and tumor radiomics significantly outperform conventional tumor imaging in overall survival prediction.
Relevance statement
Automated three-dimensional body composition radiomics integrated with tumor imaging improves survival prediction in resectable non-small cell lung cancer, enabling more precise postoperative risk stratification and supporting individualized follow-up and supportive care strategies.