ArticleEuropean journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)2025
Impact of body composition parameters, age, and tumor staging on gastric cancer prognosis.
Article in European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Decoding the association between CT-derived body composition metrics and prognosis in stage II rectal cancer.Insights into imaging · 2026Article
- Evolving Paradigms in Gastric Cancer Staging: From Conventional Imaging to Advanced MRI and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Subtotal versus total gastrectomy for mid- and distal gastric poorly cohesive carcinoma: a retrospective controlled study.Frontiers in oncology · 2025Article
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9 authors.
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Abstract
backgroundResearch studies on gastric cancer have not investigated the combined impact of body composition, age, and tumor staging on gastric cancer prognosis. To address this gap, we used machine learning methods to develop reliable prediction models for gastric cancer.
methodsThis study included 1,132 gastric cancer patients, with preoperative body composition and clinical parameters recorded, analyzed using Cox regression and machine learning models.
resultsThe multivariate analysis revealed that several factors were associated with recurrence-free survival (RFS) and overall survival (OS) in gastric cancer. These factors included age (≥65 years), tumor-node-metastasis (TNM) staging, low muscle attenuation (MA), low skeletal muscle index (SMI), and low visceral to subcutaneous adipose tissue area ratios (VSR). The decision tree analysis for RFS identified six subgroups, with the TNM staging I, II combined with high MA subgroup showing the most favorable prognosis and the TNM staging III combined with low MA subgroup exhibiting the poorest prognosis. For OS, the decision tree analysis identified seven subgroups, with the subgroup featuring high MA combined with TNM staging I, II showing the best prognosis and the subgroup with low MA, TNM staging II, III, low SMI, and age ≥65 years associated with the worst prognosis.
conclusionCox regression identified key factors associated with gastric cancer prognosis, and decision tree analysis determined prognoses across different risk factor subgroups. Our study highlights that the combined use of these methods can enhance intervention planning and clinical decision-making in gastric cancer.
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