Abstract
BACKGROUND: This study aimed to develop a predictive model utilizing radiomics and body composition features derived from (18)F-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 (18)F-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-score(PFS) (P = 0.003), sarcopenia (P < 0.001), and visceral adipose tissue index (VATI) (P < 0.001) as independent predictors of PFS. For OS, Rad-score(OS) (P = 0.001), sarcopenia (P = 0.002), VATI (P = 0.037), stage (P = 0.042), and body mass index (BMI) (P = 0.008) were confirmed as independent prognostic factors. Integration of the Rad-score with clinical variables and body composition parameters enhanced predictive accuracy, yielding C-indices of 0.810 (95% CI: 0.737-0.884) for PFS and 0.806 (95% CI: 0.720-0.891) for OS. CONCLUSIONS: This study underscored the potential of combining Rad-score with clinical and body composition data to refine prognostic assessment in ESCC patients.