Li Wei, Zhu Hai, Dong Hai-Zheng, Qin Zheng-Kun, Huang Fu-Ling, Yu Zhu, Liu Shi-Yu, Wang Zhen, Chen Jun-Qiang
Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University.
Department of Pediatric Surgery, The First Affiliated Hospital of Guangxi Medical University.
Eur J Cancer Prev. 2025 May 1;34(3):267-275. doi: 10.1097/CEJ.0000000000000917. Epub 2024 Aug 13.
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.
This study included 1,132 gastric cancer patients, with preoperative body composition and clinical parameters recorded, analyzed using Cox regression and machine learning models.
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.
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.
关于胃癌的研究尚未探讨身体成分、年龄和肿瘤分期对胃癌预后的综合影响。为填补这一空白，我们使用机器学习方法来开发可靠的胃癌预测模型。
本研究纳入了1132例胃癌患者，记录了术前身体成分和临床参数，并使用Cox回归和机器学习模型进行分析。
多变量分析显示，几个因素与胃癌的无复发生存期（RFS）和总生存期（OS）相关。这些因素包括年龄（≥65岁）、肿瘤-淋巴结-转移（TNM）分期、低肌肉衰减（MA）、低骨骼肌指数（SMI）和低内脏与皮下脂肪组织面积比（VSR）。RFS的决策树分析确定了六个亚组，TNM分期I、II合并高MA亚组预后最有利，TNM分期III合并低MA亚组预后最差。对于OS，决策树分析确定了七个亚组，高MA合并TNM分期I、II的亚组预后最佳，低MA、TNM分期II、III、低SMI且年龄≥65岁的亚组预后最差。
Cox回归确定了与胃癌预后相关的关键因素，决策树分析确定了不同危险因素亚组的预后。我们的研究强调，联合使用这些方法可以加强胃癌的干预规划和临床决策。
