Published online Nov 15, 2026. doi: 10.4251/wjgo.123321
Revised: June 25, 2026
Accepted: July 31, 2026
Published online: November 15, 2026
Processing time: 166 Days and 5.7 Hours
Gastric cancer remains a major global health burden. Many patients are diagnosed at an advanced stage and remain at high risk of recurrence despite curative-intent surgery. Although pathological tumor-node-metastasis (pTNM) staging is the cornerstone of prognostic assessment, patients within the same stage often show heterogeneous outcomes, suggesting that additional tumor biology-related information is needed to refine prognostic assessment. Dual-energy computed tomography (DECT)-derived iodine maps reflect tumor perfusion and vascularity, and radiomics can quantify subtle intratumoral heterogeneity. We hypothesized that iodine-map-based DECT radiomics, integrated with clinical factors, could improve individualized prognostic predictions in resectable advanced gastric adenocarcinoma.
To develop and validate DECT radiomics-based models for disease-free survival and overall survival prediction in resectable advanced gastric adenocarcinoma.
This retrospective study included 175 patients with advanced gastric adenocarcinoma who underwent radical gastrectomy and were divided into training (n = 122) and validation (n = 53) groups. Radiomic features were extracted from venous-phase-blended images and iodine maps. Prognostic models based on pTNM staging, clinical factors, radiomics signature (Rad-score), and combined clinical-radiomics features were developed and validated using Cox regression, Harrell’s C-index, receiver operating characteristic curves, calibration plots, and decision curve analysis.
The clinical-radiomics model, comprising the Rad-score, invasive morphology, elevated serum carcinoembryonic antigen level, and positive clinical N stage, demonstrated superior predictive accuracy compared with the pTNM model for disease-free survival (C-index: Training, 0.771 vs 0.686; validation, 0.712 vs 0.665) and overall survival (C-index: Training, 0.821 vs 0.695; validation, 0.714 vs 0.689). The clinical-radiomics model had a lower Akaike information criterion value than the pTNM model, indicating a better model fit. Subgroup analyses confirmed robust risk stratification of patients with clinically advanced disease.
The combined clinical-radiomics model integrating DECT radiomics with key clinical prognostic factors may improve preoperative risk stratification in resectable advanced gastric adenocarcinoma and help guide individualized treatment strategies.
Core Tip: This study developed a preoperative dual-energy computed tomography radiomics model for disease-free and overall survival prediction in resectable advanced gastric adenocarcinoma. By integrating radiomics features with clinical nodal status, tumor morphology, and carcinoembryonic antigen level, the model improved exploratory risk stratification over pathological tumor-node-metastasis staging.