Abstract
BACKGROUND: Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and poses a significant threat to human health. Helicobacter pylori (H. pylori)-negative early GC (HpN-EGC) often remains undetected because of its asymptomatic progression. AIM: To accurately and efficiently identify high-risk HpN-EGC individuals and guide clinical diagnosis and treatment, we developed a clinical prediction model for HpN-EGC. METHODS: This retrospective case-control study evaluated 593 confirmed H. pylori-negative cases at a hospital. Eligible patients were randomized into training (n = 416) and internal validation (n = 177) groups. Multivariate logistic regression analysis identified significant predictors, which were incorporated into the nomogram. Patients from a different hospital were included in the external validation group (n = 109). Subgroup analyses explored H. pylori eradication (> 1 year) in H. pylori-naive populations. RESULTS: Risk factors for HpN-EGC were advanced age [odds ratio (OR): 1.13], digestive comorbidities (OR: 17.55), and frequent consumption of smoked and hot foods (OR: 19.00; OR: 4.19). Regular legume and nut intake had protective effects (OR: 0.30; OR: 0.14). The nomogram showed excellent discrimination [training area under the curve (AUC) = 0.904; internal validation AUC = 0.865; external validation AUC = 0.794], stable calibration, and predictive accuracy, with a C-index of 0.904 (95% confidence interval: 0.876-0.931). Good model fit was supported by a non-significant Hosmer-Lemeshow test result (χ (2) = 7.57, P = 0.477). Subgroup analysis revealed that smoking and alcohol consumption specifically increased the risk in H. pylori-naive patients, whereas legume and nut consumption consistently reduced the risk across subgroups. CONCLUSION: The HpN-EGC risk prediction tool effectively identifies high-risk individuals based on age, digestive comorbidities, consumption of smoked and hot foods, and legume and nut intake.