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CT imaging features of 4,121 patients with COVID-19: A meta-analysis




Objective: We systematically reviewed the CT imaging features of COVID-19 in order to provide reference for clinical practice.

Methods: Our article comprehensively searched PubMed, FMRS, EMbase, CNKI,WanFang databases and VIP databases to collect literatures about the CT imaging.



This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jmv.25910.



features of COVID-19 from 1 January 2020 to 16 March 2020. Three reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, and then, this meta-analysis was performed by using Stata12.0 software. Results: 34 retrospective studies involving a total of 4 121 COVID-19 patients were included. The results of meta-analysis showed that most patients presented bilateral lung involvement (73.8%, 95%CI: 65.9%-81.1%) or multilobar involvement (67.3%,95%CI: 54.8%-78.7 %) and just a little patients showed normal CT findings (8.4%). We found that the most common changes in lesion density was ground-glass opacities (68.1%,95%CI: 56.9%-78.2%). Other changes in density included air bronchogram sign(44.7%), crazy-paving pattern (35.6%) and consolidation (32.0%). Patchy (40.3%), spider web sign (39.5%), cord-like (36.8%) and nodular (20.5%) were common lesion shapes in COVID-19 patients. Pleural thickening (27.1%) was found in some patients. Lymphadenopathy(5.4%) and pleural effusion (5.3%) were rare. Conclusion: The lung lesions of patients with COVID-19 were mostly bilateral lungs or multilobar involved. The most common chest CT findings were patchy and ground-glass opacities. Some patients had air bronchogram, spider web sign and cord-like. Lymphadenopathy and pleural effusion were rare.

Key words: Coronavirus disease 2019; Pneumonia; Computed tomography; Imaging features; Meta-analysis; Systematical review


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