Objective：To develop and validate a nomogram model based on preoperative contrast -enhanced CT radiomic features combined with clinical factors for predicting the lymph node ratio（LNR）in gastric cancer patients，aiming to provide a basis for individualized treatment decision - making. Methods：Clinical data and preoperative enhanced CT images of 380 patients who underwent radical gastrectomy were retrospectively collected and divided into a training set（n=266）and a validation set（n=114）in a 7∶3 ratio. Radiomic features were extracted from arterial and venous phase images using the PyRadiomics platform. After feature selection，three machine learning algorithms，namely support vector machine（SVM），random forest（RF），and logistic regression（LR）， were employed to build prediction models. Model performance was evaluated using the area under the receiver operating characteristic （ROC）curve（AUC）and decision curve analysis（DCA）. Significant predictors were incorporated into multivariate logistic regression to construct a nomogram. Results：The RFmodel demonstrated the best predictive performance，with AUC values of 0.733 and 0.778 in the training set and validation set，respectively. Multivariate analysis identified sex，clinical N stage（cN），and radiomic score（Radscore）as independent predictors of LNR（all P ＜ 0.05）. The nomogram incorporating these factors showed excellent predictive efficacy in the validation set，with an AUC of 0.821，and DCA indicated favorable clinical net benefit. Conclusion：A nomogram integrating radiomics and clinical factors was successfully developed and validated for the preoperative prediction of LNR in gastric cancer patients，which can effectively predict the LNR status of gastric cancer patients preoperatively，helping to identify high - risk patients and guide individualized treatment strategies.