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.

Comparison of initial high-resolution computed tomography (HRCT) features of coronavirus disease 2019 (COVID-19) pneumonia and other viral pneumonias.

BACKGROUND: Multicenter retrospective comparison of the first high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) and other viral pneumonias. METHODS: We retrospectively collected clinical and imaging data from 262 cases of confirmed viral pneumonia in 20 hospitals in Yunnan Province, China, from March 1, 2015 to March 15, 2020. According to the virus responsible for the pneumonia, the pneumonias were divided into non-COVID-19 (141 cases) and COVID-19 (121 cases). The non-COVID-19 pneumonias comprised cytomegalovirus (CMV) (31 cases), influenza A virus (82 cases), and influenza B virus (20 cases). The differences in the basic clinical characteristics, lesion distribution, location and imaging signs among the four viral pneumonias were analyzed and compared. RESULTS: Fever and cough were the most common clinical symptoms of the four viral pneumonias. Compared with the COVID-19 patients, the non-COVID-19 patients had higher proportions of fatigue, sore throat, expectorant and chest tightness (all P<0.000). In addition, in the CMV pneumonia patients, the proportions of acquired immunodeficiency syndrome (AIDS) and leukopenia were high (all PP<0.000). Comparison of the imaging findings of the four viral pneumonias showed that the pulmonary lesions of COVID-19 were more likely to occur in the peripheral and lower lobes of both lungs, whereas those of CMV pneumonia were diffusely distributed. Compared with the non-COVID-19 pneumonias, COVID-19 pneumonia was more likely to present as ground-glass opacity, intralobular interstitial thickening, vascular thickening and halo sign (all PP<0.05). In addition, in the early stage of COVID-19, extensive consolidation, fibrous stripes, subpleural lines, crazy-paving pattern, tree-in-bud, mediastinal lymphadenectasis, pleural thickening and pleural effusion were rare (all PP<0.05). CONCLUSIONS: The HRCT findings of COVID-19 pneumonia and other viral pneumonias overlapped significantly, but many important differential imaging features could still be observed.