ArticleBMC gastroenterology2026
A nutrition-focused model for predicting postoperative complications in locally advanced gastric cancer after neoadjuvant chemotherapy: a retrospective cohort study.
Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLocally advanced gastric cancer (LAGC) patients undergoing neoadjuvant chemotherapy (NAC) face a substantial risk of postoperative complications (POC), which may impair recovery and subsequent prognosis. This study aimed to develop a comprehensive predictive model incorporating multiple nutrition-related parameters, particularly CT-based body composition, to predict POC in this population.
methodsWe retrospectively analyzed 403 LAGC patients who underwent radical gastrectomy after NAC between January 2016 and December 2023. The study cohort was randomly divided into a training set and a validation set at a 7:3 ratio. The univariate analysis and subsequent least absolute shrinkage and selection operator (LASSO) regression were used to determine the significant nutrition-centered indicators for development of the predictive model. The performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve.
resultsThe incidence of POC was 31.9% (90/282) in the training cohort and 32.2% (39/121) in the validation cohort. Several independent predictors of POC were identified by univariate screening and subsequent LASSO regression analyses. Specifically, a higher nutritional risk score-2002 (NRS-2002), lower skeletal muscle index (SMI), lower pre-NAC body mass index (BMI), more advanced clinical stage, higher intramuscular fat index (IFI), higher subcutaneous fat density (SFD), higher visceral-to-subcutaneous fat ratio (VSR), and longer operation time were all associated with an increased risk of POC. Based on these parameters, we constructed a comprehensive nomogram model, which demonstrated robust and reliable predictive performance: the training set yielded an AUC of 0.861 (95% CI, 0.815–0.907), and sensitivity of 0.896; the validation set yielded an AUC of 0.744 (95% CI, 0.654–0.833), and sensitivity of 0.817. The decision curve analysis indicated that the model could provide benefits to NAC-LAGC patients within a specific threshold range.
conclusionsWe developed and validated a predictive model based on nutrition-focused predictors to predict POC in NAC-LAGC patients. The model enables accurate risk stratification and offers a practical tool to guide individualized nutritional interventions.
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