Integrating 18F-FDG PET/CT radiomics and body composition for enhanced prognostic assessment in patients with esophageal cancer

Abstract Background This study aimed to develop a predictive model utilizing radiomics and body composition features derived from 18F-FDG PET/CT scans to forecast progression-free survival (PFS) and overall survival (OS) outcomes in patients with esophageal squamous cell carcinoma (ESCC). Methods We analyzed data from 91 patients who underwent baseline 18F-FDG PET/CT imaging. Radiomic features extracted from PET and CT images and subsequent radiomics scores (Rad-scores) were calculated. Body composition metrics were also quantified, including muscle and fat distribution at the L3 level from CT scans. Multiparametric survival models were constructed using Cox regression analysis, and their performance was assessed using the area under the time-dependent receiver operating characteristic (ROC) curve (AUC) and concordance index (C-index). Results Multivariate analysis identified Rad-scorePFS (P = 0.003), sarcopenia (P