Gastric cancer is one of the most common malignant tumors with high mortality rate worldwide. In order to optimally treating gastric cacner patients and reduce cancer mortality, it requires to accurately predict Tumor, Node, Metastasis (TNM) staging of the tumor, which will determine whether the patients need neoadjuvant chemotherapy before surgery. However, subjectively reading CT images is difficult to predict TNM with large intra- and inter-reader variability. To address this challenge, we developed and tested a new CAD approach that uses radiomics features computed from the segmented tumor regions depicting on CT images to build a machine learning classifier to predict TNM and divide patients into two groups of whether need neoadjuvant chemotherapy. A CT image dataset involving 219 gastric cancer patients was retrospectively assembled and used in this study. In addition, 3 clinicopathological markers were also acquired and used. From an initial pool of 367 radiomics features, an optimal set of 11 features was selected. Then, 3 machine learning classifiers using (1) 11 CT image features, (2) 3 clinicopathological markers, and (3) fusion of both 11 CT image features and 3 clinicopathological markers, were trained and tested using a leave-one-case-out validation methods. Areas under ROC curves of three classifies are 0.74, 0.71, and 0.79, respectively. The results indicated that (1) radiomics image features computed from CT images carry higher discriminatory power to predict TNM than using clinicopathological markers acquired from surgically resected specimen and (2) fusion of CT image features and clinicopathological markers can further increase performance to predict TNM of gastric cancer patients.