CT-based Radiomics Combined with Signs: A Valuable Tool to help Physician Discriminate COVID-19 and Other Viral Pneumonia

Abstract Background: In this COVID-19 pandemic, the differential diagnosis of different viral types of pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 pneumonia and other viral pneumonia.Methods: A total of 181 patients with confirmed viral pneumonia (COVID-19: 89 cases, Non-COVID-19: 92 cases; training cohort: 126 cases; test cohort: 55 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model and the combined model were evaluated using an intra-cross validation cohort. Diagnostic performance of two models was assessed via receiver operating characteristic (ROC) analysis.Results: The combined models consisted of 3 significant CT signs and 14 selected features and demonstrated better discrimination performance between COVID-19 and Non-COVID-19 pneumonia than the single radiomics model. For the radiomics model along, the area under the ROC curve (AUC) were 0.904 (sensitivity, 85.5%; specificity, 84.4%; accuracy, 84.9%) in the training cohort and 0.866 (sensitivity, 77.8%; specificity, 78.6%; accuracy, 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity, 91.9%; specificity, 85.9%; accuracy, 88.9%), while that for the test cohort was 0.943 (sensitivity, 88.9%; specificity, 85.7%; accuracy, 87.3%).Conclusion: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and other viral pneumonia with satisfactory performance.