The Value of CT-based Radiomics Nomogram in Differential Diagnosis of Different Histological Types of Gastric Cancer

Purpose: To establish and verify a nomogram based on computed tomography (CT) radiomics analysis to predict the histological types of gastric cancer preoperatively for patients with surgical indications. Methods: : A sum of 143 patients with gastric cancer in Sir Run Run Shaw Hospital from January 2019 to December 2020 (differentiated type: 46 cases; undifferentiated type: 97 cases) were included into this retrospective study, and were randomly divided into training (n=99) and test cohort (n=44). The least absolute shrinkage and selection operator(LASSO) was used for feature selection while the multivariate Logistic regression method was used for radiomics model and nomogram building. The area under curve(AUC) was used for performance evaluation in this study. Results: : The radiomics model got AUCs of 0.755 (95%CI, 0.650-0.859) and 0.71 (95%CI,0.543-0.875) for histological prediction in the training and test cohorts, respectively. The radiomics nomogram based on radiomics features and Carbohydrate antigen 125 (CA125) achieved an AUC of 0.777 (95%CI:0.679-0.875) in the training cohort with 0.726 (95%CI:0.559-0.893) in the test cohort. The calibration curve of the radiomics nomogram also showed good results. The decision curve analysis(DCA) shows that the radiomics nomogram is clinically practical. Conclusion: The radiomics nomogram established and verified in this study showed good performance for the preoperative histological prediction of gastric cancer, which might contribute to the formulation of a better clinical treatment plan.