Computed Tomography–Based Predictive Model for the Probability of Lymph Node Metastasis in Gastric Cancer: A Meta-analysis

Abstract

Objectives Whether or not a gastric cancer (GC) patient exhibits lymph node metastasis (LNM) is critical to accurately guiding their treatment and prognostic evaluation, necessitating the ability to reliably predict preoperative LNM status. The present meta-analysis sought to examine the diagnostic value of computed tomography (CT)–based predictive models as a tool to gauge the preoperative LNM status of patients with GC. Methods Relevant articles were identified in the PubMed, Web of Science, and Wanfang databases. These studies were used to conduct pooled analyses examining sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) values, and area under the curve values were computed for summary receiver operating characteristic curves. Results The final meta-analysis incorporated data from 15 studies, all of which were conducted in China, enrolling 3,817 patients with GC (LNM+: 1790; LNM−: 2027). The developed CT-based predictive model exhibited respective pooled sensitivity, specificity, PLR, and NLR values of 84% (95% confidence interval [CI], 0.79–0.87), 81% (95% CI, 0.76–0.85), 4.39 (95% CI, 3.40–5.67), and 0.20 (95% CI, 0.16–0.26). The identified results were not associated with significant potential for publication bias (P = 0.071). Similarly, CT-based analyses of LN status exhibited respective pooled sensitivity, specificity, PLR, and NLR values of 62% (95% CI, 0.53–0.70), 77% (95% CI, 0.72–0.81), 2.71 (95% CI, 2.20–3.33), and 0.49 (95% CI, 0.40–0.61), with no significant risk of publication bias (P = 0.984). Conclusions Overall, the present meta-analysis revealed that a CT-based predictive model may outperform CT-based analyses alone when assessing the preoperative LNM status of patients with GC, offering superior diagnostic utility.