Prediction model of lymph node metastasis for early gastric cancer: a better choice than computed tomography

Abstract

Abstract Objective: The purpose of this study was to establish and validate a nomogram for predicting lymph node metastasis in early gastric cancer and to compare it with the predictive power of computed tomography (CT). Methods: Patients with early gastric cancer (2016-2021) from the First Affiliated Hospital of Nanchang University were included in the study. A nomogram was constructed according to stepwise regression analysis and logistic regression analysis. Results: In the validation cohort, the incidence of lymph node metastasis was 15.67%. Multivariate logistic regression revealed that 7 variables are associated with lymph node metastasis in early gastric cancer. According to stepwise regression analysis, 5 variables were screened to construct a nomogram, including T stage, total bilirubin (TB), Lauren typing, γ-glutamyl transpeptidase (γ-GT), vascular invasion. the AUCs of the ROC for the nomograms in the training cohort and the validation cohort is 0.795 (95% CI: 0.754–0.837) and 0.729 (95% CI: 0.655–0.803), respectively, higher than the AUCs of the CT in the training cohort and the validation cohort. Conclusion: The constructed nomogram has good performance and discrimination, which is better than CT, and successfully visualizes risk factors associated with LN metastasis in early gastric cancer.