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Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-analysis
Open accessAbstract
Purpose To date, considerable knowledge gaps remain regarding the chest CT imaging features of coronavirus disease 2019 (COVID-19). We performed a systematic review and meta-analysis of results from published studies to date to provide a summary of evidence on detection of COVID-19 by chest CT and the expected CT imaging manifestations.Methods Studies were identified by searching PubMed database for articles published between December 2019 and February 2020. Pooled CT positive rate of COVID-19 and pooled incidence of CT imaging findings were estimated using a random-effect model.Results A total of 13 studies met inclusion criteria. The pooled positive rate of the CT imaging was 89.76% and 90.35% when only including thin-section chest CT. Typical CT signs were ground glass opacities (83.31%), ground glass opacities with mixed consolidation (58.42%), adjacent pleura thickening (52.46%), interlobular septal thickening (48.46%), and air bronchograms (46.46%). Other CT signs included crazy paving pattern (14.81%), pleural effusion (5.88%), bronchiectasis (5.42%), pericardial effusion (4.55%), and lymphadenopathy (3.38%). The most anatomic distributions were bilateral lung infection (78.2%) and peripheral distribution (76.95%). The incidences were highest in the right lower lobe (87.21%), left lower lobe (81.41%), and bilateral lower lobes (65.22%). The right upper lobe (65.22%), right middle lobe (54.95%), and left upper lobe (69.43%) were also commonly involved. The incidence of bilateral upper lobes was 60.87%. A considerable proportion of patients had three or more lobes involved (70.81%).Conclusions The detection of COVID-19 chest CT imaging is very high among symptomatic individuals at high risk, especially using thin-section chest CT. The most common CT features in patients affected by COVID-19 included ground glass opacities and consolidation involving the bilateral lungs in a peripheral distribution.
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“The global pandemic caused by COVID-19 and the subsequent lockdown have been widely recognized as traumatic events that pose threats to psychological well-being.”
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Metadata
- Authors
- Bao, C., Liu, X., Zhang, H., Li, Y., & Liu, J.
- Affiliations
- Tianjin Medical University (CN)
- Corresponding author
- Li, Yiming
- Liu, Jun
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 17
- Issue
- 6
- Pages
- 701-709
- ISSN
- 1546-1440 (ISSN-L)
- PMC
- PMC7151282
- OpenAlex
- W3012931235
- Cited by
- 446
- References
- 34
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 36 · 2023: 53 · 2022: 95 · 2021: 143 · 2020: 97
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American College of Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Medical Imaging and Pathology Studies
- Keywords
- COVID-19 pandemic, SARS-CoV-2, chest CT, computed tomography findings, ground-glass opacities, consolidation, thin-section CT, lung lobe distribution, diagnostic accuracy, crazy-paving pattern, interlobular septal thickening, air bronchogram
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Female, Humans, Male, Pneumonia, Viral, Sensitivity and Specificity, Radiography, Thoracic, Tomography, X-Ray Computed, Coronavirus Infections, Pandemics
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