Abstract

On 31 December 2019, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, Hubei province, China, and caused the outbreak of the Coronavirus Disease 2019 (COVID-19). To date, computed tomography (CT) findings have been recommended as major evidence for the clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the imaging characteristics and changes throughout the disease course in patients with COVID-19 in order to provide some help for clinicians. Typical CT findings included bilateral ground-glass opacity, pulmonary consolidation, and prominent distribution in the posterior and peripheral parts of the lungs. This review also provides a comparison between COVID-19 and other diseases that have similar CT findings. Since most patients with COVID-19 infection share typical imaging features, radiological examinations have an irreplaceable role in screening, diagnosis and monitoring treatment effects in clinical practice.

Details

Title
Diagnostic value and key features of computed tomography in Coronavirus Disease 2019
Author
Li, Bingjie 1 ; Li, Xin 1 ; Wang, Yaxuan 1 ; Han, Yikai 1 ; Wang, Yidi 1 ; Wang, Chen 1 ; Zhang, Guorui 2 ; Jin, Jianjun 2 ; Jia, Hongxia 2 ; Fan, Feifei 2 ; Wang, Ma 1 ; Liu, Hong 2 ; Zhou, Yue 3 

 Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China 
 Department of Respiratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China 
 Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China 
Pages
787-793
Publication year
2020
Publication date
Dec 2020
Publisher
Taylor & Francis Ltd.
e-ISSN
22221751
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2508726120
Copyright
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.