ObjectiveTo explore the correlation between the texture features of gastric cancer plain CT images and the expression of HER2.MethodsA retrospective collection the datas of 62 patients with gastric cancer who underwent CT scans of the upper abdomen and (or) the whole abdomen from January 2017 to January 2021 in Leshan City People’s Hospital. The treatment method was surgery. The HER2 expression of gastric cancer tissue was detected after the operation. There were 45 male patients and 17 female patients. Lauren classification: 18 cases of intestinal type, 30 cases of diffuse type, and 14 cases of mixed type. Fifty-two cases were HER2 expression negative [age: (63.54±10.32) years], and 10 cases were HER2 expression positive [age: (61.70±11.70) years]. The MaZda module in the MaZda 4.6 version software was used to perform the image normalization, interest area delineation, texture feature extraction, and texture feature selection on the CT plain scan image, and perform texture feature discrimination and misjudgment rate analysis in the B11 module.ResultsThere was no correlation between HER2 expression and age, gender of patients and Lauren classification of tumors (P>0.05). The analysis methods of nonlinear discriminant analysis (NDA)/artificial neural network (ANN), linear discriminant analysis (LDA)/1-nearest-neighbor (1-NN), principal component analysis (PCA)/1-NN, and raw-data analysis (RDA)/1-NN can better correspond to the CT plain scan texture feature parameters of gastric cancer and the expression level of HER2.ConclusionsTexture analysis based on CT plain images has the potential to non-invasively detect the HER2 expression in gastric cancer. The best comprehensive performance texture discrimination method is NDA/ANN and LDA/1-NN.