key: cord-282416-5x3lyuuf
authors: Adams, Hugo J.A.; Kwee, Thomas C.; Yakar, Derya; Hope, Michael D.; Kwee, Robert M.
title: Chest CT imaging signature of COVID-19 infection: in pursuit of the scientific evidence
date: 2020-06-25
journal: Chest
DOI: 10.1016/j.chest.2020.06.025
sha: 
doc_id: 282416
cord_uid: 5x3lyuuf

Abstract Background Chest computed tomography (CT) may be used for the diagnosis of Corona virus disease 2019 (COVID-19), but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19. Methods A systematic literature search was performed for original studies on chest CT findings in patients with COVID-19. Methodological quality of studies was evaluated. Pooled prevalences of chest CT findings were calculated using a random effects model in case of between-study heterogeneity (predefined as I2≥50), otherwise a fixed effects model was used. Results Twenty-eight studies were included. Median number of COVID-19 patients per study was 124 (range 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range >76.3%-100%). 27/28 (96%) of studies had a retrospective design. Methodological quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (8 studies), disease progression bias (26 studies), observer variability bias (27 studies), and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for vascular thickening, and 72.2% for right lower lobe involvement. Pooled prevalences were 5.2% for pleural effusion, 5.1% for lymphadenopathy, 4.1% for airway secretions/tree-in-bud sign, 3.6% for central lesion distribution, 2.7% for pericardial effusion, and 0.7% for cavitation/cystic changes. Pooled prevalences of other CT findings ranged between 10.5%-63.2%. Conclusion Studies on chest CT findings in COVID-19 suffer from methodological quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that requires cautious interpretation, several chest CT findings appear to be suggestive of COVID-19, but normal chest CT findings do not exclude COVID-19, even not in symptomatic patients.

Chest computed tomography (CT) may be used for the diagnosis of Corona virus disease 2019 (COVID- 19) , but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19.

A systematic literature search was performed for original studies on chest CT findings in patients with COVID-19. Methodological quality of studies was evaluated. Pooled prevalences of chest CT findings were calculated using a random effects model in case of between-study heterogeneity (predefined as I 2 ≥50), otherwise a fixed effects model was used.

Twenty-eight studies were included. Median number of COVID-19 patients per study was 124 (range 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range >76.3%-100%). 27/28 (96%) of studies had a retrospective design. Methodological quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (8 studies), disease progression bias (26 studies), observer variability bias (27 studies) , and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for

Corona virus disease 2019 (COVID -19) has been designated a pandemic by the World Health Organization, continues to rapidly disseminate around the globe, and poses a major public health problem. 1 Many countries are using a combination of containment and mitigation activities to battle the spread of COVID-19, with the primary aim to delay major surges of patients and leveling the demand for hospital beds, while protecting the most vulnerable from infection. 1 Screening of patients with suspected COVID-19 is crucial for hospitals to keep those who are actually infected strictly isolated from other patients and healthcare workers without COVID-19.

Real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay of nasal and pharyngeal swab specimens is currently the gold standard for the diagnosis of COVID-19. 2 However, it generally takes several hours before the results of RT-PCR testing become available, and its sensitivity is insufficient to reliably exclude COVID-19 due to factors like sampling or laboratory errors. [3] [4] [5] [6] RT-PCR testing should therefore be repeated in those individuals with a persistent clinical suspicion of COVID-19. [3] [4] [5] [6] Altogether, RT-PCR testing is rather time-consuming and suboptimal for the rapid triaging of patients.

Meanwhile, several reports have indicated a possible role for chest computed tomography (CT) in the diagnosis of this disease. 3, [7] [8] [9] Chest CT may be used for the diagnosis of COVID-19 in several settings. First, healthcare institutions that adopt a strategy of containment may decide to use chest CT for the evaluation of patients in whom COVID-19 needs to be excluded, in addition to RT-PCR. Second, chest CT may have a potential role as a problem-solving diagnostic tool in patients in whom RT-PCR testing remains negative despite persistent clinical suspicion. Third, CT scans that are performed as part of standard clinical care, for other reasons than COVID-19 evaluation (e.g., oncological follow-up CT scans), may reveal lung abnormalities that can suggest the diagnosis of COVID-19, even in asymptomatic individuals. 3, [7] [8] [9] Given the diagnostic potential of chest CT, it is imperative for radiologists to have knowledge of the typical imaging characteristics of COVID-19.

Although several previous studies have described chest CT characteristics of COVID-19, these individual studies may suffer from low sample sizes and differences in study design and methodology. Of interest, the Fleischner Society recently published an expert opinion statement on the use of chest imaging (including radiography and CT) in patient management during the COVID-19 pandemic, with the intent to offer guidance to physicians on the use of thoracic imaging across a breadth of healthcare environments. 10 However, the Fleischner Society also acknowledged that the evidence base supporting the use of imaging across the scenarios presented was scant and that their advice may undergo refinement through rigorous scientific investigation. 10 A systematic review and meta-analysis is required to overcome the limitations of individual studies and to provide an up to date overview that can be used to optimize the diagnostic interpretation of chest CT for COVID-19.

The purpose of this study was therefore to systematically review and metaanalyze the chest CT imaging signature of COVID-19 infection.

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. 11 

Original studies which reported the prevalence of chest CT findings in patients with RT-PCR or gene sequencing proven COVID-19 were eligible for inclusion. Only studies which provided a detailed description of chest CT findings according to the glossary of terms for thoracic imaging of the Fleischner Society 12 were included.

Reviews, conference abstracts, editorials, case reports/series, and studies involving <50 patients were excluded. Studies that enrolled patients from the same hospital in the same inclusion period as another larger study, were excluded.

Using the aforementioned selection criteria, titles and abstracts of studies were reviewed. Full-text versions of potentially eligible articles were retrieved. Full-text articles were then scrutinized to definitively determine if the study was eligible for inclusion. Study selection was independently performed by two reviewers (H.J.A.A. and R.M.K). Any discrepancies were solved by consensus with a third reviewer (T.C.K).

Quality of included studies was assessed. Study quality aspects were adopted from the Quality Assessment of Diagnostic Accuracy Studies tool 13 and edited according to our study research question (Table 1) .

For each included study, publication date, country of origin, study design (retrospective or prospective), number, gender and age of included patients, inclusion criteria, number of symptomatic patients, duration of symptoms before chest CT, disease severity (based on reported descriptive data), chest CT interpreters, and time interval between chest CT and RT-PCR/gene sequencing, were extracted. For each included study, the frequency of chest CT findings (i.e., normal findings and all individually reported lung abnormalities according to the glossary of terms for thoracic imaging of the Fleischner Society 12 on a patient level) were extracted.

Prevalences of chest CT findings were pooled if supported by data from at least two studies. Between-study heterogeneity was assessed using the I 2 statistic. Pooled prevalences were calculated using a random effects model in case of heterogeneity (predefined as I 2 ≥50), otherwise a fixed effects model was used. Statistical analyses were performed using the Open Meta Analyst software package (http://www.cebm.brown.edu/openmeta).

The study selection is shown in Figure 1 . 165 studies were potentially eligible for inclusion. After reviewing the full text, 137 studies were excluded (Appendix 1).

Finally, 28 studies were included, 

The methodological quality assessment is displayed in Table 2 . Risk of bias with respect to method of patient selection was rated "unclear" in 13 studies, 14, [20] [21] [22] [23] 27, 28, [32] [33] [34] 36, 38, 41 because these studies did not report whether patients were randomly or consecutively included. Risk of bias with respect to patient spectrum was rated "high" in 8 studies, 14, 19, 20, 23, 25, 29, 31, 38 because these studies excluded patients with normal chest CT findings. Risk of bias with respect to patient spectrum was rated "unclear" in 2 studies, 34,39 because the number of patients with normal chest CT findings was not reported. Risk of bias with respect to flow and timing was rated "unclear" in 24 studies, 15, 16, [18] [19] [20] [21] [22] [23] [24] [26] [27] [28] [29] [30] [31] [32] [34] [35] [36] [37] [38] [39] [40] [41] because these studies did not report the time interval between CT and RT-PCR/gene sequencing. Risk of bias with respect to flow and timing was rated "high" in 2 studies, 14, 25 because time interval between CT and RT-PCR exceeded 72 hours (maximum of 7 and 14 days, respectively). Risk of bias with respect to observer variation was rated "high" in 27 studies, 40, 41 because these studies did not report data on observer agreement. Finally, risk of bias in the domain blinding to the reference standard was rated "unclear" in 14 studies, 15, 16, 20, 22, 23, 27, [30] [31] [32] [34] [35] [36] [37] 41 because these studies did not report whether the interpreters of chest CT were blinded to the RT-PCR results.

Pooled prevalences of chest CT findings in COVID-19 patients are shown in Table 3 .

Pooled prevalence of normal chest CT findings was 10.6% (95% confidence interval [95% CI]: 7.6%-13.7%). 

Pooled prevalences of pleural thickening and pleural effusion were 34.7% (95% CI:14.4%-55.0% ) and 5.2% (95% CI: 3.8%-6.7%), respectively. "Signs" Pooled prevalences of the "halo sign", and the "reversed halo sign" were 34.5% (95% CI: 13.8%-55.3%), and 11.1% (95% CI: 4.5%-17.7%), respectively.

Pooled prevalences of lymphadenopathy and pericardial effusion were 5.1% (95% CI:

3.2%-6.9%) and 1.6% (95% CI: 0.1%-3.1%), respectively.

The number of studies on chest CT imaging in COVID-19 has rapidly increased since the pandemic outbreak of this disease. However, both individual studies, nonsystematic reviews, and expert opinion articles may contain claims that are not substantiated by evidence. This is potentially dangerous, since healthcare providers need to be provided with unbiased, reliable data to make the right clinical decisions.

For other diseases that are already known and that do not pose an imminent threat to humanity, scientific evidence can be accumulated and reflected upon at a relatively slower pace. However, COVID-19 does not provide this relative luxury, hence the potentially higher risk for healthcare providers to make clinical decisions based on missing, incomplete, or incorrect information. Because of the potential of chest CT in adjunct to clinical examination and RT-PCR for the diagnosis of COVID-19, and the rapid proliferation of studies on this topic, a systematic review and meta-analysis was performed to assess the methodological quality of these studies and to determine the frequency of different chest CT findings that are found in this disease. . Importantly, some journals provide socalled "ultra-rapid" peer review services (within 24 hours) for COVID-19 related research 42 . It has been reported that such a service may result in a series of highquality research publications with downloads that are 6 to 30 times greater than the average articles that are published in the same journal, and that several of these COVID-19 publications have been in the top two or three trending articles on PubMed. 42 However, the results of the present study challenge the claim that only high-quality research is published with such a policy. In fact, they indicate the lack of a solid scientific foundation for chest CT in COVID-19, and the need for more highquality studies. Our findings resonate with a previous review that concluded that the published literature reporting on chest CT features in COVID-19 consisted of limited retrospective studies with methodological quality issues. 43 Within the boundaries of the available evidence, a critical finding of this systematic review and meta-analysis was that 10.6% of patients with proven COVID-19 (almost all of these patients were symptomatic) had normal chest CT findings. The substantial prevalence of normal chest CT findings is clinically relevant, because it implies that a negative chest CT cannot exclude COVID-19 with sufficient certainty, even not in symptomatic patients. Although it has been reported that normal findings at chest CT may occur more frequently in the first days after symptom onset, 44 a nonnegligible number of symptomatic patients with normal chest CT findings are observed during the later stage of the infection. [44] [45] [46] Therefore, it is questionable if chest CT can be used for accurate stratification of patients in a screening setting that aims to strictly isolate individuals with COVID-19 from those without. Importantly, six imaging findings were observed in more than 70% of COVID-19 proven cases, and these included posterior predilection, ground-glass opacity, bilateral abnormalities, left lower lobe involvement, vascular thickening, and right lower lobe involvement, in order of decreasing frequency. In geographic regions in which COVID-19 is endemic, the observation of these chest CT findings should raise the suspicion of possible COVID-19 infection. On the other hand, several imaging findings were observed in 5% or less of COVID-19 positive cases, and these included pleural effusion, lymphadenopathy, airway secretions/tree-in-bud sign, central lesion distribution, pericardial effusion, and cavitation/cystic changes, in order of decreasing frequency. The isolated observation of one or more of these chest CT findings may therefore be suggestive of another diagnosis, although it should be noted that COVID-19 cannot be completely eliminated from the differential diagnosis. Altogether, the above-mentioned chest CT findings on both sides of the spectrum with regard to observed frequencies in COVID-19 may be helpful to imaging physicians to determine the likelihood of COVID-19. However, some caution is warranted, because these chest CT findings were extracted from studies that generally provided no to little information on the presence and types of pulmonary comorbidities (which may cause CT abnormalities that are not related to in the patients that were included. Finally, other chest CT findings were found to be of relatively lower value in terms of true positive or false negative rates.

This systematic review and meta-analysis had some limitations. First, only RT-PCR proven COVID-19 cases were included. Chest CT features of COVID-19 may overlap with those of other entities, including, but not limited to, other viral and (atypical) bacterial pneumonias, interstitial lung diseases, drug-induced lung disease, alveolar hemorrhage, and pulmonary edema due to a wide range of cardiogenic or other non-cardiogenic causes. 47 The individual chest CT abnormalities that were retrieved by our analysis are nonspecific, and if a mixed group of infections were studied (as would be typical in most settings, except in the epicenter of a COVID-19 outbreak), it can be expected that specificity will be further compromised. Future studies are required to test which chest CT criteria achieve optimal sensitivity and specificity in differentiating COVID-19 from other entities in different clinical settings and with different disease prevalence rates. Second, the various chest CT findings based on the Fleischner Society's glossary terms were individually assessed and pooled with regard to frequency of appearance in COVID-19. However, a combination of chest CT findings will likely be necessary to establish an appropriate confidence scale for the diagnosis of COVID-19. Of interest, a Radiological Society of North America Expert consensus statement on reporting chest CT findings related to COVID-19 was recently published. 48 Four categories for reporting CT imaging findings potentially attributable to COVID-19 were proposed, and three of these categories used a combination of chest CT findings. 48 Furthermore, there are no

published studies yet which have evaluated this chest CT classification scale, to our knowledge. However, this categorization and the corresponding CT criteria were based on a limited number of studies that were selected by an expert committee. 48 Furthermore, there are no published studies yet which have evaluated this chest CT classification scale, to our knowledge. The findings of the present systematic review and meta-analysis may be helpful to further develop existing confidence scales for COVID-19, such as the one that was recently issued under auspices of the Radiological Society of North America. 48 The presented data may also serve as an input for machine learning-based diagnostics. Third, the far majority of studies that were included originated from China. Nevertheless, there is no a priori assumption as to whether chest CT findings in COVID-19 would be different in non-Chinese populations. Fourth, temporal changes on chest CT during the course of disease could not be assessed. Although several of the studies that were included also reported some information on temporal changes on chest CT during the course of disease, 15, 22, 27, 28, 32, [34] [35] [36] [37] [38] 40 they suffered from considerable flaws and limitations in the analysis of temporal changes on chest CT. None of these studies described sufficient details of the patients who underwent chest CT at different time points to understand potentially confounding factors on the temporal course of chest CT findings (such as pulmonary comorbidities and therapies that were administered), time points of chest CT imaging during the course of disease were dissimilar between all studies, and all studies provided mere descriptive data without performing comprehensive statistical analyses to assess for differences in chest CT findings between different time points. 15, 22, 27, 28, 32, [34] [35] [36] [37] [38] 40 Furthermore, one study compared different patients who underwent chest CT at different time points rather than evaluating the time course of chest CT findings in the same patients. 15 In the other studies who did perform an intrapatient evaluation during the course of disease, either only a subset of patients underwent follow-up chest CT causing selection bias 22, 27, 28, 32, [34] [35] [36] [37] [38] or chest CT findings were insufficiently reported. 40 Consequently, the available data on temporal changes on chest CT are unreliable, lack clinical applicability, and cannot be systematically summarized. The same concerns apply to the studies that were excluded from this systematic review and meta-analysis. A prospective well-designed study is still required to determine the natural evolution of chest CT findings in COVID-19.

In conclusion, studies on chest CT findings in COVID-19 suffer from methodological quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that should be interpreted with caution, several chest CT findings appear to be suggestive of COVID-19, but normal chest CT findings do not exclude COVID-19, even not in symptomatic patients. Table 1 Criteria used to assess the methodological quality of included studies (adopted from reference 13 and edited according to our study research question).

Signalling questions*

Were patients randomly or consecutively included?

2) Patient spectrum Was a sample of patients with COVID-19 included?

3) Flow and timing Was the interval between chest CT and RT-PCR or gene sequencing adequately short (i.e. ≤72 hours)?

Was the degree of observer variation in chest CT interpretation reported? 5) Blinding to reference standard Were the interpreters of chest CT blinded to RT-PCR or gene sequencing results?

23 * Each quality item was rated as at "low risk," "high risk," or "unclear" risk of bias. If the signaling question that belonged to a quality item was answered with "yes", then the quality item was considered at low risk of bias. If the signaling question that belonged to a quality item was answered with "no", then the quality item was considered at high risk of bias. If the signaling question that belonged to a quality item could not be answered with "yes" or "no", then the quality item was considered at unclear risk of bias."

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