ArticleJournal of gastrointestinal oncology2025
Development and validation of a preoperative CT-based body composition nomogram for predicting recurrence-free survival after radical surgery in patients with gastric cancer.
Article in Journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative contributions of visceral adiposity and muscle-related parameters to postoperative complications after gastrectomy: a domain-based analysis.BMC cancer · 2026Article
- Development and validation of a prognostic prediction model for gastric cancer based on lipophagy-related genes.Translational cancer research · 2025Article
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Authors and funding
7 authors.
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Abstract
Background: Computed tomography (CT) body composition is associated with the prognosis of gastric cancer (GC), but few studies have investigated the prognostic value of CT body composition combined with preoperative clinical indicators in GC. This study aimed to develop and validate a nomogram model using preoperative CT-quantified body composition parameters and clinical indicators to predict recurrence-free survival (RFS) in patients undergoing radical resection for GC. Methods: We retrospectively analyzed patients with pathologically confirmed GC who underwent preoperative CT scans between October 2018 and May 2023. Multivariate Cox regression analysis was performed on the derivation cohort to identify preoperative predictors independently associated with RFS and to construct a nomogram model. The model was then validated in a separate test set. Results: A total of 450 patients were included, with 268 in the derivation set and 182 in the test set. Five variables, visceral adipose tissue (VAT) density, visceral obesity, sarcopenia, neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index (PNI), were identified as independent predictors of RFS. The preoperative nomogram model demonstrated superior predictive accuracy compared to pathological tumor staging at various time points. Calibration curves showed good agreement between the model's predictions and actual outcomes. Decision curve analysis (DCA) indicated significant clinical benefit. The model effectively stratified patients into low-risk and high-risk groups for recurrence. Conclusions: The preoperative nomogram model is a valuable tool for predicting RFS in patients undergoing radical resection for GC.
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