Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19.

BACKGROUND: The outbreak of COVID-19 poses a major and urgent threat to global public health. CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. This study aimed to retrospectively analyze abnormal lung changes following initial computed tomography (CT) among patients with coronavirus disease 2019 (COVID-19) in Yunnan, and to evaluate the effectiveness of a chest CT-based model for the diagnosis of COVID-19. METHODS: One hundred and nine patients with COVID-19 pneumonia confirmed with the positive new coronavirus nucleic acid antibody who exhibited abnormal findings on initial CT were retrospectively analyzed. Thereafter, changes in the number, distribution, shape, and density of the lesions were observed. Further, the epidemiological, clinical, and CT imaging findings (+/-) were correlated. Following univariate and multivariate logistic regression analysis, receiver operating characteristic (ROC) curves were generated for significant factors, and models were established to evaluate the diagnostic ability of CT for COVID-19. RESULTS: Our results showed significant differences between patients with COVID-19 in epidemiological history (first, second, and third generation), clinical type (moderate, severe, and critical), and abnormal CT imaging characteristics (+/-) (P<0.05). Moreover, significant differences in abnormal CT imaging characteristics, including region, extent, and focus, were observed between the first generation and the other generations (P<0.05). For the diagnosis of COVID-19, the areas under the ROC curves for logistic regression models 1, 2, and 3 were 0.8016 (95% CI: 0.6759-0.9274), 0.9132 (95% CI: 0.8571-0.9693), and 0.9758 (95% CI: 0.9466-1), respectively. CONCLUSIONS: The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19.

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.

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.

Dynamic changes in chest CT findings of patients with coronavirus disease 2019 (COVID-19) in different disease stages: a multicenter study.

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; fibrous stripes and subpleural lines increased from stage 1 and peaked in stage 4. Partial consolidation and consolidation were more common in severe patients than in moderate patients over the disease course (P<0.05). Critical patients showed significant partial consolidation and consolidation; The CT value, lesion volume and lesion volume percentage significantly decreased from stages 1-2 to stage 4 (all P<0.05). CONCLUSIONS: The dynamic changes in lung HRCT images are clinically related to the disease course of COVID-19.