Abstract
Accurate preoperative prognosis prediction is crucial for gastric cancer (GC) treatment planning, yet existing models overlook body composition integration. This study demonstrates the potential of integrating multimodal data, including skeletal muscle (SM), adipose tissue (AT), and primary tumor computed tomography images, to improve prognostic stratification in GC patients using an entire cohort of 1862 patients. By leveraging a Vision Transformer-based deep learning approach, we developed and validated a SM-AT-Tumor-Clinical (SMAT-TC) integrated score to predict recurrence-free survival (RFS) in GC patients. The SMAT-TC score achieved a C-index of 0.966 (95% CI: 0.937-0.990), 0.890 (95% CI: 0.866-0.915), and 0.855 (95% CI: 0.829-0.881) in the training, internal validation, and external validation cohorts, respectively, outperforming the Clinical, SM, AT, Tumor, Tumor-Clinical (TC), and SM-Tumor-Clinical (SM-TC) models. The net reclassification improvement and integrated discrimination improvement confirmed the incremental value of body composition. The SMAT-TC score was an independent risk factor for recurrence. The SMAT-TC model could stratify patients into high-, medium-, and low-risk groups with distinct 3- (99.6% vs. 67.0% vs. 10.9%) and 5-year RFS rates (98.8% vs. 61.7% vs. 2.4%). Collectively, the SMAT-TC score may serve as a novel imaging biomarker for GC patients, enhancing risk stratification and guiding individualized treatment strategies.