| Summary: | 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.
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