Development and validation of a nomogram incorporating dietary factors for predicting -negative early gastric cancer risk.

[BACKGROUND] Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and poses a significant threat to human health. ()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 -negative cases at a hospital. Eligible patients were randomized into training ( = 416) and internal validation ( = 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 ( = 109). Subgroup analyses explored eradication (> 1 year) in -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 ( = 7.57, = 0.477). Subgroup analysis revealed that smoking and alcohol consumption specifically increased the risk in -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.