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dc.contributor.authorSun, Zhonghua
dc.contributor.authorZhang, N.
dc.contributor.authorLi, Y.
dc.contributor.authorXu, X.
dc.date.accessioned2020-05-11T01:23:02Z
dc.date.available2020-05-11T01:23:02Z
dc.date.issued2020
dc.identifier.citationSun, Z. and Zhang, N. and Li, Y. and Xu, X. 2020. A systematic review of chest imaging findings in COVID-19. Quantitative Imaging in Medicine and Surgery. 10 (5): pp. 1058-1079.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/79085
dc.description.abstract

Chest computed tomography (CT) is frequently used in diagnosing coronavirus disease 2019 (COVID-19) for detecting abnormal changes in the lungs and monitoring disease progression during the treatment process. Furthermore, CT imaging appearances are correlated with patients presenting with different clinical scenarios, such as early versus advanced stages, asymptomatic versus symptomatic patients, and severe versus nonsevere situations. However, its role as a screening and diagnostic tool in COVID-19 remains to be clarified. This article provides a systematic review and meta-analysis of the current literature on chest CT imaging findings with the aim of highlighting the contribution and judicious use of CT in the diagnosis of COVID-19. A search of PubMed/Medline, Web of Science, ScienceDirect, Google Scholar and Scopus was performed to identify studies reporting chest imaging findings in COVID-19. Chest imaging abnormalities associated with COVID-19 were extracted from the eligible studies and diagnostic value of CT in detecting these abnormal changes was compared between studies consisting of both COVID-19 and non-COVID-19 patients. A random-effects model was used to perform meta-analysis for calculation of pooled mean values and 95% confidence intervals (95% CI) of abnormal imaging findings. Fifty-five studies met the selection criteria and were included in the analysis. Pulmonary lesions more often involved bilateral lungs (78%, 95% CI: 45–100%) and were more likely to have a peripheral (65.35%, 95% CI: 25.93–100%) and peripheral plus central distribution (31.12%, 95% CI: 1.96–74.07%), but less likely to have a central distribution (3.57%, 95% CI: 0.99–9.80%). Ground glass opacities (GGO) (58.05%, 95% CI: 16.67–100%), consolidation (44.18%, 95% CI: 1.61–71.46%) and GGO plus consolidation (52.99%, 95% CI: 19.05–76.79%) were the most common findings reported in 94.5% (52/55) of the studies, followed by air bronchogram (42.50%, 95% CI: 7.78–80.39%), linear opacities (41.29%, 95% CI: 7.44–65.06%), crazypaving pattern (23.57%, 95% CI: 3.13–91.67%) and interlobular septal thickening (22.91%, 95% CI: 0.90– 80.49%). CT has low specificity in differentiating pneumonia-related lung changes due to significant overlap between COVID-19 and non-COVID-19 patients with no significant differences in most of the imaging findings between these two groups (P>0.05). Furthermore, normal CT (13.31%, 95% CI: 0.74–38.36%) was reported in 26 (47.3%) studies. Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia. Further, there is a relatively high percentage of normal CT scans. Use of CT as a first-line diagnostic or screening tool in COVID-19 is not recommended.

dc.publisherAME Publishing Company
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject1103 - Clinical Sciences
dc.titleA systematic review of chest imaging findings in COVID-19
dc.typeJournal Article
dcterms.source.volume10
dcterms.source.number5
dcterms.source.startPage1058
dcterms.source.endPage1079
dcterms.source.issn2223-4292
dcterms.source.titleQuantitative Imaging in Medicine and Surgery
dc.date.updated2020-05-11T01:22:10Z
curtin.departmentSchool of Molecular and Life Sciences (MLS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidSun, Zhonghua [0000-0002-7538-4761]
curtin.contributor.researcheridSun, Zhonghua [B-3125-2010]
curtin.contributor.scopusauthoridSun, Zhonghua [12544503300]


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