AI Article Synopsis
- The study investigates how body composition, age, and tumor staging affect the prognosis of gastric cancer, using machine learning techniques to create prediction models.
- It analyzed data from 1,132 gastric cancer patients, revealing significant factors impacting recurrence-free survival (RFS) and overall survival (OS), including age, tumor staging, and muscle characteristics.
- The results showed distinct prognostic subgroups based on these factors, suggesting that combining these analytical methods can improve treatment planning and clinical decisions for gastric cancer patients.
Article Abstract
Background: Research 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.
Methods: This study included 1,132 gastric cancer patients, with preoperative body composition and clinical parameters recorded, analyzed using Cox regression and machine learning models.
Results: The 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.
Conclusion: Cox 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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| http://dx.doi.org/10.1097/CEJ.0000000000000917 | DOI Listing |
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