The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients
Open accessAbstract
Objective The objective of this systematic review was to evaluate the key imaging manifestations of COVID-19 on chest CT in adult patients by providing a comprehensive review of the published literature.Methods We performed a systematic literature search from the PubMed, Google Scholar, Embase, and WHO databases for studies mentioning the chest CT imaging findings of adult COVID-19 patients.Results A total of 45 studies comprising 4410 patients were included. Ground glass opacities (GGO), in isolation (50.2%) or coexisting with consolidations (44.2%), were the most common lesions. Distribution of GGOs was most commonly bilateral, peripheral/subpleural, and posterior with predilection for lower lobes. Common ancillary findings included pulmonary vascular enlargement (64%), intralobular septal thickening (60%), adjacent pleural thickening (41.7%), air bronchograms (41.2%), subpleural lines, crazy paving, bronchus distortion, bronchiectasis, and interlobular septal thickening. CT in early follow-up period generally showed an increase in size, number, and density of GGOs, with progression into mixed areas of GGOs plus consolidations and crazy paving, peaking at 10-11 days, before gradually resolving or persisting as patchy fibrosis. While younger adults more commonly had GGOs, extensive/multilobar involvement with consolidations was prevalent in the older population and those with severe disease.Conclusion This review describes the imaging features for diagnosis, stratification, and follow-up of COVID-19 patients. The most common CT manifestations are bilateral, peripheral/subpleural, posterior GGOs with or without consolidations with a lower lobe predominance. It is pertinent to be familiar with the various imaging findings to positively impact the management of these patients.Key points • Ground glass opacities (GGOs), whether isolated or coexisting with consolidations, in bilateral and subpleural distribution, are the most prevalent chest CT findings in adult COVID-19 patients. • Follow-up CT shows a progression of GGOs into a mixed pattern, reaching a peak at 10-11 days, before gradually resolving or persisting as patchy fibrosis. • Younger people tend to have more GGOs. Older or sicker people tend to have more extensive involvement with consolidations.
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“A systematic review of 45 studies of chest computed tomography images of patients with COVID-19 demonstrated pulmonary fibrosis in 17% of patients, which was attributed to impaired healing after viral infection of lung AT2 cells (53).”
From Lung epithelial and endothelial damage, loss of tissue repair, inhibition of fibrinolysis, and cellular senescence in fatal COVID-19 (2021) · cites this paper as [53]
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WITHDRAWN: Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19)
Zhu, Y., Liu, Y.-L., Li, Z., Kuang, J.-Y., Li, X., Yang, Y.-Y., & Feng, S. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.02.022 · Cited by 29
RetractedPreferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
Wang, G., Ma, R., Qiao, G., Wada, K., Aizawa, Y., & Satoh, T. (2015) · Integrative Medicine International · doi:10.1159/000368408 · Cited by 18,762
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Research works printed but not linked: 1
- Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020) Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing. Radiology. 12:200343
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Lemmers, D. H. L., Hilal, M. A., Bnà, C., Prezioso, C., Cavallo, E., Nencini, N., Crisci, S., Fusina, F., & Natalini, G. (2020) · ERJ Open Research · doi:10.1183/23120541.00385-2020 · Cited by 162
Metadata
- Authors
- Ojha, V., Mani, A., Pandey, N. N., Sharma, S., & Kumar, S.
- Affiliations
- All India Institute of Medical Sciences (IN)
- Sree Chitra Thirunal Institute for Medical Sciences and Technology (IN)
- Corresponding author
- Kumar, Sanjeev
- Venue
- European Radiology
- Published
- 2020-05-30
- Volume
- 30
- Issue
- 11
- Pages
- 6129-6138
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC7261039
- OpenAlex
- W3030243311
- Cited by
- 227
- References
- 66
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 16 · 2023: 35 · 2022: 50 · 2021: 87 · 2020: 28
- FWCI
- 2.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European 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 › Clinical Neurology › Long-Term Effects of COVID-19
- Keywords
- COVID-19 pandemic, SARS-CoV-2, chest CT findings, ground-glass opacities, lung consolidation, crazy-paving pattern, pulmonary fibrosis, disease severity, age differences, follow-up imaging, interlobular septal thickening
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adult, Female, Humans, Lung, Male, Middle Aged, Pneumonia, Viral, Tomography, X-Ray Computed, Coronavirus Infections, Disease Progression, Young Adult, Pandemics
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