Abstract
Introduction: To investigate the Computed Tomography (CT) imaging characteristics and dynamic changes of COVID-19 pneumonia at different stages.
Methods: Forty-six patients infected with COVID-19 who had chest CT scans were enrolled, and CT scans were performed 4-6 times with an interval of 2-5 days.
Results: At the early stage (n=25), ground glass opacity was presented in 11 patients (11/25 or 44.0 %) and ground glass opacity mixed with consolidation in 13 (13/25 or 52.0 %) in the lung CT images. At the progressive stage (n=38), ground glass opacity was presented in only one patient (1/38 or 2.6 %) and ground glass opacity mixed with consolidation in 33 (33/38 or 86.8 %). In the early improvement stage (n=38), the imaging presentation was ground glass opacity alone in three patients (3/38 or 7.9 %) and ground glass opacity mixed with consolidation in 34 (34/38 or 89.5 %). In the late improvement (absorption) stage (n=33), the primary imaging presentation was ground glass presentation in eight patients (8/33 or 24.2 %) and ground glass opacity mixed with consolidation in 23 (23/33 or 69.7 %). The lesion reached the peak at 4-16 days after disease onset, and 26 (26/38 or 68.4 %) patients reached the disease peak within ten days. Starting from 6 to 20 days after onset, the disease began to be improved, with 30 (30/38 or 78.9 %) patients being improved within 15 days.
Conclusion: COVID-19 pneumonia will progress to the peak stage at a mediate time of seven days and enter the improvement stage at twelve days. Computed tomography imaging of the pulmonary lesion has a common pattern from disease onset to improvement and recovery and provides important information for evaluation of the disease course and treatment effect.
Keywords: COVID-19, SARS-CoV-2 virus, radiography, viral infection, imaging, dynamic changes.
Graphical Abstract
[http://dx.doi.org/10.1016/S0140-6736(20)30183-5] [PMID: 31986264]
[http://dx.doi.org/10.1002/jmv.25708] [PMID: 32056235]
[PMID: 32048818]
[http://dx.doi.org/10.1001/jama.2020.1585] [PMID: 32031570]
[http://dx.doi.org/10.1007/s00330-020-07300-y] [PMID: 32964337]
[http://dx.doi.org/10.1007/s11547-020-01295-8] [PMID: 33006087]
[http://dx.doi.org/10.1007/s00330-020-06713-z] [PMID: 32025790]
[http://dx.doi.org/10.1148/radiol.2020200230] [PMID: 32017661]
[http://dx.doi.org/10.1148/radiol.2020200370]
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