Development and validation of a predictive model for the pathological upgrading of gastric low-grade intraepithelial neoplasia

BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia (LGIN) presents challenges in developing diagnostic and treatment protocols. AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment. METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People's Liberation Army General Hospital between January 2008 and December 2023. A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability. RESULTS A total of 171 patients were included in this study: 93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN. The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800, respectively, while the least absolute shrinkage and selection operator (LASSO) regression model showed sensitivity and specificity values of 0.842 and 0.840, respectively. The area under the curve (AUC) for the logistic model was 0.896, slightly lower than the AUC of 0.904 for the LASSO model. Internal validation with 30% of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model. The LASSO model provided greater utility in clinical decision-making. CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.