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Zhonghua Jie He He Hu Xi Za Zhi ; 43(4): 321-326, 2020 Apr 12.
Article in Chinese | MEDLINE | ID: mdl-32125131

ABSTRACT

Objective: To investigate the imaging findings of 2019 novel coronavirus pneumonia (COVID-19). Methods: From January 20 to February 5, 2020, a total of 130 patients diagnosed with COVID-19 from seven hospitals in China were collected. The imaging data were reviewed and analyzed in detail. Results: (1) Distribution: the lesion detected in the lung unilaterally in 14 cases (10.7%) and bilaterally in 116 cases (89.3%). According to the distribution in the lobes of the lung, all cases could be classified into subpleural distribution (102 cases, 78.4%), centrilobular distribution (99 cases, 76.1%) and diffused distribution (8 cases, 6.1%). (2) Number of lesions: single lesion 9 cases (6.9%); multiple lesions 113 cases (86.9%), diffuse lesions 8 cases (6.1%). (3) Imaging density: 70 cases (53.8%) of ground-glass opacity (GGO), 60 cases (46.2%) of GGO+consolidation. (4) Accompanying signs: 100 cases (76.9%) with vascular thickening, 98 cases (75.3%) with "pleural parallel sign" ; " intralobular septal thickening" in 100 cases (76.9%); "halo sign" in 13 cases (10%); "reversed-halo sign" in 6 cases (4.6%); pleural effusion in 3 cases (2.3%), and pneumatocele in 2 cases (1.5%); no case with pulmonary cavity. Among 35 patients that underwent follow-up CT, 21 patients (60%) improved while 14 (40%) exacerbated. Conclusions: COVID-19 imaging characteristic mainly has subpleural, centrilobular and diffused distribution. The first two distributions can overlap or progress to diffused distribution. In the later period, it was mainly manifested as organizing pneumonia and fibrosis. The most valuable characteristic is the pleural parallel sign.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Betacoronavirus , COVID-19 , China , Coronavirus Infections/pathology , Humans , Lung/pathology , Pandemics , Pneumonia, Viral/pathology , SARS-CoV-2
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