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 (≥65â 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 ≥65â 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.
ABSTRACT
Atrial fibrillation (AF) is closely related to abnormal cerebral blood flow. Inflammation and oxidative stress have always been important factors in the pathophysiology of AF. It remains unknown whether inflammation and oxidative stress are correlated to hippocampal perfusion in patients with AF.Sixty-three patients with AF with normal hippocampal blood perfusion (NHBP) were compared to 71 patients with AF with abnormal hippocampal blood perfusion (AHBP) using a case-control study design. The serum levels of inflammation and oxidative stress were measured. The hippocampal perfusion was detected. (1) The serum levels of high-sensitivity C-reactive protein (hs-CRP), interleukin 6 (IL-6), and oxidized low-density lipoprotein (ox-LDL) were statistically higher in the AHBP group than in the NHBP group. In the AHBP subgroup analysis, the serum levels of hs-CRP and IL-6 were statistically higher in patients with persistent AF than those with paroxysmal AF. (2) The relative cerebral blood volume (rCBV), mean transit time (MTT), and the time-to-peak (TTP) were statistically higher in the AHBP group than in the NHBP group. Moreover, cerebral blood flow (rCBF) was statistically lower in the AHBP group than in the NHBP group. (3) relative cerebral blood volume (rCBV), rCBF, MTT, and TTP were passively associated with serum hs-CRP and IL-6; rCBV, rCBF, and MTT were positively associated with ox-LDL. The serum levels of hs-CRP, IL-6, and ox-LDL were associated with AHBP in patients with AF after multivariate logistic regression analysis.Oxidative stress and inflammatory biomarkers were increased in patients with AF with AHBP, in which the serum levels of hs-CRP and IL-6 in the persistent AF group were statistically higher than those in the paroxysmal AF group. The serum levels of hs-CRP, IL-6, and ox-LDL were associated with AHBP in patients with AF.