Deep learning-derived CT body composition enhances survival risk stratification beyond the TNM system in locally...

Survival outcomes in locally advanced gastric cancer remain heterogeneous despite standard treatment and outcome classifications. Visceral adiposity has increasingly emerged as a prognostic factor, yet its mechanistic and clinical utilization remains underexplored.

This retrospective cohort study evaluated 227 American Joint Committee on Cancer 8th edition stage III gastric cancer patients undergoing curative gastrectomy (2007-2022) at a tertiary referral center. A deep learning-enabled UNet++ model quantified computed tomography-based body composition (CTBC) metrics, validated against manual segmentation. Subsets of patients underwent plasma metabolomic ( n = 86) and tumor immune-metabolic profiling ( n = 40) using mass spectrometry, immunohistochemistry, and 35-color flow cytometry. Median follow-up was 33 months.

Automated CTBC analysis showed excellent concordance with manual segmentation ( r2  > 0.85). Low subcutaneous adipose tissue (SAT) index and high visceral-to-subcutaneous adipose tissue (VAT/SAT) ratio independently predicted worse disease-free and overall survival (hazard ratio: 1.4-1.6). Incorporating CTBC metrics significantly improved survival models (likelihood ratio P < 0.03; ΔAIC > 4). A high VAT/SAT ratio correlated with increased plasma acylcarnitines and decreased phosphatidylcholines, indicating impaired mitochondrial fatty acid oxidation and altered lipid and membrane remodeling. Tumors from high VAT/SAT patients showed upregulated CPT1, downregulated CPT2/CACT, increased IDO1/AHR expression, and elevated immunosuppressive CD4 + EMRA and regulatory T cell infiltration (all P < 0.05).

Deep learning-derived CTBC metrics, especially VAT/SAT ratio, enhance prognostic stratification beyond TNM staging in locally advanced gastric cancer. This ratio captures a systemic and tumor-level immunometabolic phenotype marked by mitochondrial dysfunction and immune suppression. Our findings highlight VAT/SAT as a noninvasive, clinically actionable biomarker to guide personalized therapy and risk-adapted algorithm in gastric cancer management.