Zhangsen Huang ¹ Songyao Chen ^1* Songcheng Yin ^1,2* Zhaowen Shi ^3* Liang Gu ^1* Liang Li ^1* Haofan Yin ¹ Zhijian Huang ¹ Bo Li ^1,2 Xin Chen ^3* Yilin Yang ^3* Zhengli Wang ^3* Hai Li ^3* Changhua Zhang ^1,2* Yulong He ^1,2*

• ¹ Digestive Medicine Center, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong Province, China
• ² Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
• ³ People's Hospital of Fengqing, Lincang, China

Background: Detection of gastric cancer (GC) at early stages is an effective strategy for decreasing mortality. This study aimed to construct a prediction nomogram based on a questionnaire to assess the risk of developing GC. Methods: Our study comprised a total of 4379 participants (2326 outpatient participants from outpatient at Fengqing People's Hospital were considered for model development and internal validation, and 2053 participants from outpatients at the endoscopy center at the Seventh Affiliated Hospital of Sun Yat-Sen University were considered for independent external validation) and gastric mucosa status was determined by endoscopy and biopsies. The eligible participants in development cohort from Fengqing people's Hospital were randomly separated into a training cohort (n=1629, 70.0%70%) and an internala validation cohort (n=697, 30.0%30%). The relevant features were selected by a least absolute shrinkage and selection operator (LASSO), and the ensuing features were evaluated through multivariate multivariable logistic regression analysis. Subsequently, the variables were selected to construct a prediction nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated by the C-index and calibration plot, respectively. Decision curve analysis (DCA) curves were used for the assessment of clinical benefit of the model. This model was developed to estimate the risk of developing neoplastic lesions according to the "transparent reporting of a multivariable prediction model for individual prognosis or diagnosis" (TRIPOD) statement. Results: Six variables, including age, sex, alcohol consumption, cigarette smoking, education level, and Hp infection status, were independent risk factors for the development of neoplastic lesions.Thus, these variables were incorporated into the final nomogram. The AUCC-index of the nomogram were 0.701, and 0.657 and 0.699 in the training, internal validation, and external validation cohorts, respectively. The calibration curve showed that the nomogram was in good agreement with the observed outcomes. Compared to treatment of all patients or none, our nomogram showed a notably higher clinical benefit. Conclusion: This nomogram proved to be a convenientpowerful, cost-effective tool to effectively predict an individual's risk of developing neoplastic lesions, and it can act as a prescreening tool before gastroscopy.