Based on the inclusion criteria, 2000 gastric cancer patients will be recruited in the analysis. And a model will be constructed based on deep learning.

Based on the inclusion criteria, 1000 gastric cancer patients will be recruited in this group to verify the sensitivity and specificity of the constructed model.

Based on the inclusion criteria, 300 gastric cancer patients from 5 other medical centers will be recruited in this group to verify the sensitivity and specificity of the constructed model.

All the participants were measured by the whole abdomen contrast-enhanced CT scan.

To measure the maximum diameter of tumor on preoperative enhanced abdominal CT of patients with gastric cancer.

To assess the growth pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including endophytic, exophytic and mixed.

To assess the enhancement pattern on preoperative enhanced abdominal CT of patients with gastric cancer, including homogeneous and heterogeneous.

To assess the enhancement degree on preoperative enhanced abdominal CT of patients with gastric cancer, including hypoenhancement, isoenhancement and hyperenhancement.

To obtain the nucleus size of postoperative H&E stained sections and slides of gastric cancer by deep learning.

To obtain the nucleus shape of postoperative H&E stained sections and slides of gastric cancer by deep learning.

To obtain the distribution of pixel intensity of postoperative H&E stained sections and slides of gastric cancer by deep learning.

To analyze the survival status of patients with gastric cancer, involving dead and alive.

To calculate the overall survival of patients with gastric cancer based on days to death and days to last follow-up.