Abstract: Objective: The aim was to establish a computed tomography (CT)-based radiomic nomogram and evaluate its efficacy in preoperatively predicting the clinical tumor, lymph node, and metastasis (TNM) staging of gastric cancer. Methods: A total of 177 patients with gastric cancer, including 150 and 27 from the Sir Run Shaw and Nanxun People's Hospitals, respectively, were enrolled between January 2019 and December 2020. The cohort consisted of 120 men and 57 women, with a mean age of 66.43±11.03 years. A total of 104 and 46 of the 150 patients at Sir Run Run Shaw Hospital were randomly assigned to the training and internal validation groups, respectively. The 27 patients from Nanxun People’s Hospital were included in the external validation group. Radiomic features were extracted from the CT images of gastric cancer lesions; features were selected using the least absolute shrinkage and selection operator (LASSO) method. A fusion prediction model was established that incorporated radiomic features and clinical risk factors using multivariate logistic regression. The results were visualized as a radiomic nomogram. The diagnostic efficiency of the resulting nomogram was evaluated for the training, internal validation, and external validation groups based on the area under the curve (AUC). Results: The CA199 (P=0.04), portal-venous-phase CT values (P=0.005), and equilibrium-phase CT values (P=0.007) significantly differed between the training and other groups. Four radiomics features were selected from 850 candidates. A combined model was developed based on radiomics features, CA199, portal-venous-phase CT values, and equilibrium-phase CT values using multiple logistic regression. The radiomic nomogram was evaluated on the training, internal validation, and external validation groups, achieving AUC values of 0.813, 0.746, and 0.773, respectively. These results demonstrate the ability of the model to accurately discriminate the clinical TNM staging of gastric cancer. Conclusion: A radiomics nomogram for preoperatively predicting the clinical TNM staging in gastric cancer was established and validated.