Background In this COVID-19 pandemic, the differential diagnosis of viral 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 and influenza pneumonia. Methods A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 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, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. Results The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). Conclusions CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

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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.

Background: To investigate the dynamic changes in high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) patients with different severities in different disease stages. Methods: We retrospectively collected the clinical and imaging data of 96 patients in Yunnan Province, China, who were diagnosed with COVID-19 between January 22 and March 15, 2020. Based on disease severity, the COVID-19 patients were classified into four types: mild (n=15), moderate (n=59), severe (n=19), and critical (n=3). Based on hospital stay and number of computed tomography (CT) scans, the clinical/ disease course was divided into four stages, including stage 1 (days 0-4), stage 2 (days 5-9), stage 3 (days 10-14), and stage 4 (days 15-19). The HRCT findings, CT value, and lesion volume were analyzed for each stage and compared among the four stages of COVID-19 patients. Results: CT findings were negative over the four stages for all mild COVID-19 patients. More lesions were found in the peripheral lung fields than in peripheral + central fields (P<0.05), and the number of negative patients in stage 4 were more than those in stages 1-3 (P<0.05). The left and right lower lobe were the most frequently affected lobes (P<0.05). In moderate patients, round ground glass opacities (GGOs) decreased from stage 1 to stage 4; partial consolidation peaked in stage 2 and then decreased in stages 3-4; Huang et al. Dynamic changes in chest CT of COVID-19 patients