Radiomic Analysis of Abdominal CT Plain Scans for Identifying Aortic Syndrome in Emergency Patients

Background: This study aimed to assess the capability of radiomics based on abdominal CT scans in identifying aortic syndrome (AS) among patients in the emergency department. Methods: Emergency patients who underwent both plain and contrast-enhanced abdominal CT scans from August 2012 to October 2020 were retrospectively enrolled. These patients were classified based on the presence of abdominal AS. The dataset was randomly split into training, test, and external validation sets in a 3:1:1 ratio. Radiomic features were extracted from manually segmented regions of the abdominal aorta in the CT images. These features were used to create a radiomic model. The radiomic model was integrated with relevant clinical factors using multivariate logistic regression to develop a clinical-radiomics model. The diagnostic performance of the models was assessed through receiver operating characteristic (ROC) analysis. Results: A total of 188 patients were included in the study. Ten of 1794 radiomic features were selected for constructing the radiomic model. The SHapley Additive exPlanations (SHAP) analysis identified the wavelet-LHL_GLDM_SDLGLE feature as a significant contributor to the predictive accuracy of the radiomic model. The area under the curves (AUCs) for the clinical, radiomic, and clinical-radiomics models in the external validation set were 0.763, 0.891, and 0.860, respectively. The AUCs of the radiomic and clinical-radiomics models were significantly higher than that of the clinical model ( P < 0.05). However, no statistically significant difference was observed between the AUCs of the radiomic model and the clinical-radiomics model ( P > 0.05). Conclusions: The study demonstrated that an abdominal CT plain scan-based radiomic model can effectively be utilized for preliminary diagnosis of abdominal AS in emergency patients.