Beyond TNM staging: validation of a CT-derived nutritional-inflammatory composite model (RFMmSIS) for prognostication in resectable gastric cancer

Prognostic models for resectable gastric cancer rarely integrate adipose tissue and immune-inflammatory parameters. This study evaluated the predictive value of preoperative relative fat mass (RFM) and the modified systemic inflammation score (mSIS), and explored their potential synergy with nanomedicine-based theranostics. We retrospectively analyzed 518 patients who underwent radical gastrectomy across three tertiary centers in China (overall enrollment period: 2010–2019). Preoperative CT-derived waist circumference at L3 was used to calculate RFM, with the optimal cutoff determined by ROC analysis using the Youden index. Univariate and multivariate Cox regressions identified independent prognostic factors and constructed the nutritional-inflammatory composite parameter RFMmSIS. Predictive performance was assessed using AUC, AIC, BIC, NRI, IDI, and DCA. Postoperative follow-up was conducted every 3 months during the first 2 years, every 6 months during years 3 through 5, and annually thereafter. Patients were randomly allocated to training ( n = 362) and validation ( n = 156) cohorts. RFM outperformed BMI for survival prediction (AUC: 0.584 vs. 0.503, P < 0.05) and was associated with worse 3-year overall survival (OS) (64.1% vs. 80.0%, P = 0.014) and disease-free survival (DFS) (57.7% vs. 78.4%, P = 0.005). Multivariate analysis identified RFM (HR: 2.355) and mSIS grade 2 (HR: 1.657) as independent prognostic factors. The composite RFMmSIS stratified patients into high-, intermediate-, and low-risk groups, with higher scores associated with increased severe complications (Clavien-Dindo ≥IIIa: 4.7% vs. 4.2% vs. 15.2%, P = 0.005 in training cohort) and poorer 3-year OS/DFS (high-risk: 44.3%/41.0%). RFMmSIS demonstrated superior predictive accuracy for OS and DFS compared with operative extent and pN stage (all P < 0.05). Preoperative RFMmSIS is a clinically feasible predictor of short-term complications and long-term survival in gastric cancer, offering enhanced prognostic stratification beyond traditional parameters and providing a rationale for integrating nutritional-inflammatory risk assessment with nanomedicine-based theranostic strategies.