key: cord-0887708-tfur69pl
authors: Sharif, Pouya Mahdavi; Nematizadeh, Mehran; Saghazadeh, Mahdia; Saghazadeh, Amene; Rezaei, Nima
title: Computed tomography scan in COVID-19: a systematic review and meta-analysis
date: 2022-01-05
journal: Pol J Radiol
DOI: 10.5114/pjr.2022.112613
sha: d0345a6d5f07c33edfeba2e8ef258947d1a35f6b
doc_id: 887708
cord_uid: tfur69pl

PURPOSE: Computed tomography (CT) scan is a commonly used tool for the diagnosis of the novel coronavirus disease 2019 (COVID-19), similarly to reverse transcription-polymerase chain reaction (RT-PCR). Because of the limitations of RT-PCR, there is growing interest in the usability of the CT scan. The present systematic review and meta-analysis aims to summarize the available data on the CT scan features of COVID-19. MATERIAL AND METHODS: We conducted a systematic search in electronic databases to find eligible studies published between 1 December 2019 and 4 April 2020, which investigated the computed tomographic features of patients with COVID-19. All preprint and peer-reviewed articles were included. No language limitation was applied. For proportional data, pooled prevalence was calculated using a Freeman-Tukey double arcsine transformation, with a 95% confidence interval (CI). RESULTS: Eighty-six studies were eligible to be included in the meta-analysis. For 7956 patients, the most common CT findings were bilateral pattern of involvement (78%; 95% CI: 0.73-0.82; p < 0.001), involvement of more than 1 lobe (75%; 95% CI: 0.68-0.82; p < 0.001), ground-glass opacities (GGO) (73%; 95% CI: 0.67-0.78; p < 0.001), and peripheral distribution of signs (69%; 95% CI: 0.61-0.76; p < 0.001). Only 5% of patients had a normal CT scan (95% CI:0.03-0.07; p < 0.001). The proportion of paediatric patients (age < 18 years) with unremarkable CT findings was higher (40%; 95% CI: 0.27-0.55; p < 0.001). Subgroup analysis showed that patients with the severe or critical type of COVID-19 were more likely to have pleural effusion (RR 7.77; 95% CI: 3.97-15.18; p < 0.001) and consolidation (RR 3.13; 95% CI: 1.57-6.23; p < 0.001). CT results in patients with COVID-19 were comparable with those of people having pneumonia from other causes, except for the lower incidence of consolidation (RR 0.81; 95% CI: 0.71-0.91; p < 0.001) and higher risk of showing GGO (RR 1.45; 95% CI: 1.13-1.86; p < 0.001). The mortality rate was slightly higher in patients with bilateral involvement (RR 3.19; 95% CI: 1.07-9.49; p = 0.04). CONCLUSIONS: Our study results show that COVID-19 shares some features with other viral types of pneumonia, despite some differences. They commonly present as GGO along with vascular thickening, air bronchogram and consolidations. Normal CT images, lymphadenopathies, and pleural effusions are not common. Consolidations and pleural effusions correlate with more severe disease. CT features are different between COVID-19 and non-COVID-19 pneumonia. Also, they differ by age, disease severity, and outcomes within COVID-19 patients.

In December 2019, multiple pneumonia cases infected by an unknown causative agent were identified in Wuhan, China [1] . Further investigations proved that the underlying pathogen was a novel coronavirus, the seventh human-infecting one after HCoV-NL63, HCoV-229E, HCoV-OC43, HCoVHKU1, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome coronavirus (MERS-CoV). After identifying the genetic sequence, it was named SARS-CoV-2 by the International Committee on Taxonomy of Viruses (ICTV) [2, 3] . It was officially recognized as coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) on 11 February 2020. It has spread worldwide and contributed to thousands of deaths. As a result, the WHO referred to COVID-19 as a public health emergency of international concern on 30 January 2020, and later on 11 March 2020 as a pandemic [4] . As of 13 April 2020, the number of confirmed cases and deaths of COVID-19 is 1,853,155 and 114,247, respectively [5] .

Before the emergence of COVID-19, 2 other b-coronaviruses, SARS-CoV and MERS-CoV, had caused epidemics beginning in 2002-2003 in China [6] [7] [8] and 2012-2013 in Saudi Arabia [9] [10] [11] , respectively. Recent investigations have shown several similarities and differences between COVID-19 and the coronaviruses mentioned above in terms of clinical, epidemiological, laboratory, and radiological characteristics [12] [13] [14] [15] . As observed in recent studies, clinical findings during the disease course may include but are not limited to fever (81-94% of cases), cough (65-78% of cases), muscle soreness or fatigue (21-65% of cases), and acute respiratory distress syndrome (ARDS) (4-29% of cases). So, a considerable portion of COVID-19 cases may progress to life-threatening conditions [16] . To prevent or manage this, early diagnosis of the disease is critical so that the isolation, monitoring, and treatment process may begin earlier, which might lead to better disease outcomes and fewer infections in the population.

Detection of the virus genome using the reverse transcription-polymerase chain reaction (RT-PCR) technique is currently the approved method for a definite diagnosis of COVID-19. However, its sensitivity is reportedly not high enough. Also, the test is time-consuming and not widely available, and false-negative results may be present because of errors in sampling methods, sampling sites, sample processing, and incorrect timing of sampling [17, 18] . On the other hand, chest computed tomography (CT) is a relatively available and non-invasive method used for identifying COVID-19 cases [19] . To apply CT imaging as a valid preliminary test, it is necessary to explain the radiological features of COVID-19. Many studies have reported radiological findings of the disease in different populations, and previously published reviews inferred some general details. Considering the numerous publications that become available every day, there is a need for a more detailed description of chest CT findings in COVID-19. Therefore, this study aims to summarize the available data regarding chest CT features in confirmed cases of COVID-19, provide more details of specific findings, clarify the findings and differences in paediatric patients, the different degrees of disease severity, and PCR-negative or non-COVID pneumonia, and evaluate the correlation between CT findings and patient outcomes.

The results of this meta-analysis are reported based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement [20] .

We systematically searched the MEDLINE, Web of Science, and Scopus databases for relevant publications. Because of the novelty of this topic and for the reduction of publication bias, we also searched Google Scholar for the possible missing articles on other databases. Our search terms were the following: "CT", "CT scan", "computed tomography", "imaging"; and "COVID19", "SARS-CoV-2", "Wuhan pneumonia", "Wuhan corona", "2019nCoV", and "2019 novel coronavirus". The search strategy for each database is provided with details in the supplement. No language restriction was applied. All relevant studies published between 1 December 2019 and 15 March 2020 were retrieved. We also manually searched the reference lists of reviews and relevant articles. An additional search was conducted on 4 April 2020 in MEDLINE using the same search terms.

We included all peer-reviewed and non-peer-reviewed publications that had reported computed tomographic features of RT-PCR-confirmed patients with SARS-CoV-2 infection. Editorials, letters, comments, reviews, and case series with less than 10 reported cases were excluded from our search results. Two authors (P.M. and M.N.) independently conducted the title and abstract screening, and then performed a detailed review of the resulting publications. Any disagreement was consulted with another author (A.S.).

We used a modified version of the Newcastle-Ottawa scale (NOS) designed for the cross-sectional studies as a tool for quality assessment [21] . This tool is concerned with three main areas of interest: sample selection, comparability of groups, and outcome. The maximum obtained score was ten stars.

patients. Because some studies had compared CT findings of confirmed COVID-19 patients with pneumonia patients of other infectious causes, the data of such patients were also retrieved with details. Extracted data consisted of the following parts: first author's name, year of publication, possible subgroups, number of cases that underwent CT scan, number of patients with normal CT findings, ground-glass opacities (GGO), consolidation, air-bronchogram, bronchial wall thickening, bronchiectasis, crazy-paving pattern, halo sign, reversed halo sign, lymphadenopathy, pleural effusion, pleural thickening, tree-in-bud sign, cavitation, vascular dilation and thickening, bilateral involvement of lungs, peripheral distribution of inflammation, the involvement of more than one lobe, and the number of each of the involved lobes (RUL, RML, RLL, LUL, and LLL). Detailed definitions for each of these signs are provided in Table 1 .

All proportional and between-group meta-analyses in this study were performed using Stata release 16.0 (StataCorp, College Station, TX, USA). For proportional data, we first transformed the proportions with the Freeman-Tukey double arcsine method so that the normal distribution could be achieved and the problem with proportions being too high or too low and their confidence intervals would be resolved [22] . Reported effect estimates and confidence intervals (CI) were then back-transformed to proportions. Hete rogeneity between studies was assessed using the p-value of Cochran's Q test and its more quantified form, I 2 statistics. We assessed publication bias using Egger's test [23] .

An I 2 value of less than 40% was considered as an indicator of low heterogeneity between publications. Based on the results of the I 2 statistics, we carried out a fixed or random effects model for meta-analysis. For proportional data, the effect size (ES) was the pooled proportion of CT features, reported with a 95% CI. For the between-group analysis, risk ratio and 95% CI were calculated. A p-value of less than 0.05 was considered significant. Because it seems that the course of the disease is generally milder in children [24] , we decided to analyse paediatric cases (age < 18 years) separately (n = 147; six publications [25] [26] [27] [28] [29] [30] ). Also, three types of subgroup analyses were considered, to assess whether the prevalence of CT findings is different between: (1) those with mild-moderate and severecritical disease; (2) surviving and deceased patients; and (3) those with RT-PCR-confirmed COVID-19 and those with pneumonia caused by other agents. For the severity of the disease, most studies classified their cases based on either the Guidelines for the Diagnosis and Treatment of New Coronavirus Pneumonia [31] or the American Thoracic Society guidelines for community-acquired pneumonia [32] .

Our first search resulted in 547 results. After the removal of duplications, 428 publications underwent title and abstract screening. After the exclusion of irrelevant articles based on title and abstract screening, 109 articles were assessed for eligibility by full-text screening. Among these, we found 65 studies eligible to be considered for quanti- Table 1 . Definition of computed tomography findings reported in this study (from the Fleischer Society: Glossary of Terms for Thoracic Imaging) [86] 

Air-bronchogram An air bronchogram is a pattern of air-filled (low-attenuation) bronchi on a background of opaque (high-attenuation) airless lung.

Morphologic criteria on thin-section CT scans include bronchial dilatation with respect to the accompanying pulmonary artery (signet ring sign), lack of tapering of bronchi, and identification of bronchi within 1 cm of the pleural surface.

A cavity is a gas-filled space, seen as a lucency or low-attenuation area, within pulmonary consolidation, a mass, or a nodule.

Consolidation appears as a homogeneous increase in pulmonary parenchymal attenuation that obscures the margins of vessels and airway walls.

Crazy-paving pattern This pattern appears as thickened interlobular septa and intralobular lines superimposed on a background of ground-glass opacity, resembling irregularly shaped paving stones.

Ground-glass opacity (GGO) On CT scans, GGO appears as hazy increased opacity of lung, with preservation of bronchial and vascular margins.

The halo sign is a CT finding of ground-glass opacity surrounding a nodule or mass.

There is a wide range in the size of normal lymph nodes. Mediastinal and hilar lymph nodes range in size from sub-CT resolution to 12 mm. Somewhat arbitrary thresholds for the upper limit of normal of 1 cm in short-axis diameter for mediastinal nodes and 3 mm for most hilar nodes.

The reversed halo sign is a focal rounded area of ground-glass opacity surrounded by a more or less complete ring of consolidation.

Tree-in-bud sign The tree-in-bud pattern represents centrilobular branching structures that resemble a budding tree. Table 2 . The proportion of CT scan findings

Among 80 publications, 58 had reported the number of patients without any abnormal CT findings (n = 6426). Pooled prevalence of patients with normal CT was 5% (95% CI: 0.03-0.07; p < 0.001; Figure 2 ). However, for paediatric patients this was higher (40%; 95% CI: 0.27-0.55; p < 0.001; Figure 3 ).

This is one of the most important CT findings suggestive of SARS-CoV-2 [33, 34] . The number of patients with GGO was reported in 59 studies (n = 5691). The pooled prevalence of GGO was 73% (95% CI: 0.67-0.78; p < 0.001; Figure 4 ), which makes it the most common imaging sign in patients with COVID-19. Also, GGO was found in 44% of paediatric patients with COVID-19 (95% CI: 0.26-0.63; p < 0.001; n = 65 patients; Supplementary Figure 1 ).

Microvascular and macrovascular dilation is probably due to inflammation and the resulting hyperaemia. Thirteen studies reported this sign, although with different nomenclature (vascular dilation, thickening, and enlargement). The pooled prevalence of this feature among COVID-19

patients was 63% (95% CI: 0.52-0.73; p < 0.001; Supplementary Figure 2 ).

Fifty-four articles reported bilateral involvement of the lungs (n = 5250). The proportion of patients with this feature was 78% (95% CI: 0.73-0.82; p < 0.001; Figure 5 ), which is the most common CT scan pattern found in COVID-19 patients in this meta-analysis. Thirty-two per cent of paediatric patients had this feature in their CT images (95% CI: 0.13-0.53; p < 0.001; n = 102 patients; Supplementary Figure 1 ).

Peripheral zone of lungs is generally defined as the outer one-third on lung parenchyma. We found that in 1942 cases, 69% had lesions located in the peripheral zone of the lungs (95% CI: 0.61-0.76; p < 0.001; Supplementary Figure 2 ).

Among the included studies, a few reported detailed information about the number of involved lobes for each patient. However, because other publications just reported the proportion of cases with more than one lobe involvement, we took this as a cut-off for reporting the results. For 1687 cas- es, 75% had involvement of more than one lung lobe, which makes it the second most common pattern of involvement seen in CT images (95% CI: 0.68-0.82; p < 0.001; Figure 6 ).

LLL and RLL were involved in 67% and 66% of patients, respectively. Proportions of patients with LUL, RUL, and RML involvement are provided in Table 3 ; however, they should be treated with caution due to publication bias.

Other more common signs we found in our meta-analysis were air-bronchogram (40%), consolidation (34%), crazypaving pattern or paving stone sign (31%), bronchiectasis (24%), and pleural thickening (24%). No cavitation was reported in patients, and only three of 501 cases had the treein-bud sign. The proportion of all extracted signs and patterns, along with 95%CI, I 2 statistics, and p-values for the Egger's test and ES, are provided in Table 3 and Supplementary Table 4 .

We found 14 publications that reported CT findings of patients with the severe-critical and mild-moderate disease, separately [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] . There were 1707 and 494 patients with mild-moderate and severe-critical situations, respectively. Subgroup analysis of these patients showed that those with severe-critical illness had a higher risk of having bilateral involvement (RR, 1.37; 95 CI: 1.06-1.78; p = 0.02; Figure 7 ) and consolidation (RR, 3.12; 95 CI: 1.57-6.23; p < 0.001; Figure 7) . Also, the severe-critical group had a lower proportion of patients with normal CT, compared with the mild-moderate group (RR, 0.31; 95 CI: 0.18-0.53; p < 0.001). The details of these findings are summarized in Table 5 and Supplementary Figure 3 .

We found three studies [49] [50] [51] comparing CT findings of living and dead COVID-19 patients. It turned out that the presence of GGO is not associated with a worse outcome (RR, 1.97; 95% CI: 0.48-8.00; p = 0.34; Figure 8 ), but bilateral involvement with an increased risk of mortality (RR, 3.19; 95% CI: 1.07-9.49; p = 0.04; Table 5 and Figure 8 ).

Nine studies [52] [53] [54] [55] [56] [57] [58] [59] [60] compared imaging findings of pneumonia patients with confirmed COVID-19 (n = 968) to those without RT-PCR confirmation of the infection with SARS-CoV-2 (n = 821). COVID-19 cases had a higher risk of having GGO (RR, 1.45; 95 CI: 1.13-1.86; p < 0.001; Figure 9 ), but there was no significant difference between the presence of pleural effusion, positive CT findings, and bilateral involvement in two groups. However, non-COVID-19 patients showed an increased risk of having consolidation (RR, 1.23; 95 CI: 1.09-1.39; p < 0.001; Figure 9 ). Detailed information is provided in Table 5 and Supplementary Figures 4 and 5 .

COVID-19 continues to spread globally as there are thousands of confirmed cases and deaths reported every day and more than 85,000 deaths in 210 countries up to 9 April 2020 [5] . With such a relatively unknown virus causing a pandemic, further studies, data gathering, and evaluation seem necessary. Early and fast detection of present cases, in such conditions with up to 80% prevalence of patients with mild symptoms and asymptomatic carriers [61, 62] , and long-term stability of the underlying virus on different surfaces [63] , leads to better isolation, treatment, and monitoring of patients. This can also improve disease outcomes, reduce costs, and ease the control of this progressing pandemic [64, 65] .

Chest CT evaluation is one of the most important aspects of the diagnostic process in suspected COVID-19 patients. RT-PCR, currently being the recommended test for laboratory confirmation of the disease [66] , has its own limitations, as previously discussed, such as being time consuming and not having adequate sensitivity, especially early in the infection course [67] . As a result, chest CT may become more important in confirming cases in need of hospital admission, isolation, etc. Despite being relatively sensitive in detecting pneumonia patients [67, 18] , chest CT may not have adequate specificity for COVID-19 in single images without detailed assessment [68] . In addition, high cost and relatively lower accessibility of chest CT in western countries, as well as development of more rapid RT-PCR testing methods have limited CT use in those regions [69, 70] . In order to gather the information needed for accurate evaluation of chest CTs by healthcare providers, we aim to provide a more detailed systematic review of chest CT findings and further evaluate them in specific subgroups.

Normal chest CT was seen in only 5% of our cases. This, along with some other previously published studies, supports the fact that chest CT is a sensitive diagnostic tool that is appropriate for screening suspected patients, especially in epidemics [67, 18] . Common findings in viral pneumonias include patchy or diffuse GGOs and reticulations. Consolidations may be present or absent [71] .

Consistent with this phenome non, our study results show that the most common radiological finding among CO-VID-19 patients is GGO, followed by vascular thickening (dilation), air bronchogram, and consolidation (observed in 73%, 61%, 41%, and 34% of cases, respectively). Other probable radiologic signs include crazy paving pattern (27%), pleural thickening (24%), bronchiectasis (24%), and halo sign (21%). Pleural effusion, reversed halo sign, and lymphadenopathies were rare, and almost no cases were seen with tree-in-bud sign or cavitations. A relatively low proportion of consolidation positive chest CTs may be a finding that differentiates SARS-CoV-2 infections from those caused by MERS-CoV and SARS-CoV. Conversely, GGOs are seen commonly in patients infected with all three epidemic-causing coronaviruses [72] .

Among five lung lobes, the left lower and right lower lobes were more likely to be involved in COVID-19 patients, as seen in 67% and 66% of cases, respectively. The least involved lobe was the right middle lobe. So, the infec- tion is more common in the lower lobes, is usually bilateral (78%), and frequently involves more than one lobe (76%) in peripheries (68%). Lesions are usually multifocal (66%), and diffuse distribution (8%) is not common. These findings are also in favour of the distribution patterns proposed previously [73] [74] [75] [76] .

Concerning the distribution of different lesions observed in COVID-19, peripheral distribution and lower lung infiltrations are the features in common with MERS-CoV and SARS-CoV [72] .

It was previously observed that the disease course might have a milder pattern in children, with a higher proportion of asymptomatic carriers present [77, 78] . In our study, a pooled prevalence of 40% was observed for normal chest CTs. Even considering the 95% CI, it is a significantly lower percentage compared with our general population. This implies that chest CT has lower sensitivity for identifying patients in the paediatric group, and relying on chest CT findings may increase the risk of missing patients with normal chest CTs. As a result, and considering the higher proportion of asymptomatic carriers in this age group, an integrated diagnostic approach that relies on thorough examination of clinical findings, exposure history, laboratory results, imaging, and RT-PCR tests might be a better option in children with suspected novel coronavirus infection. In addition, bilateral and unilateral involvement in chest CT images shows closer proportions in our findings, and GGOs are less common. This can mean that the typical imaging findings in children may also differ from those observed in the general population of COVID-19 patients.

In this meta-analysis, we tried to evaluate the differences in chest CT findings between the severe/critical group and mild/moderate group, classified based on the Guidelines for the Diagnosis and Treatment of New Coronavirus Pneumonia or the American Thoracic Society guidelines for community-acquired pneumonia.

In our study population for disease severity, 25% of cases were classified as severe/critical, considering the 19% report in a study on 72,314 confirmed and suspected COVID-19 patients in China [79] . According to their chest CT findings, it is inferred that the tendency towards developing consolidations and bilateral infiltrations is higher in patients with severe/critical disease. On the other hand, normal lung CT scan had a higher proportion in the mild/moderate group. Like in severe cases of MERS-CoV and SARS-CoV, pleural effusion is a radiological finding with greater prevalence in severe/critical SARS-CoV-2 infection [72] . This, combined with the clinical and laboratory data, may help in the classification of patients into severity groups and guide clinicians in choosing the subsequent plans accordingly.

Other than diagnostic values of chest CT, its findings might be useful in predicting disease outcomes and patient prognoses in COVID-19. Regarding the outcome, bilateral involvement of lung parenchyma was associated with a higher risk of mortality. This finding is in accordance with the reports of a recent preprint article showing that the elevated level of inflammatory cytokines (CCL7, CXCL10, and IL-1 receptor antagonist) is associated with the extent of lung injury and a fatal outcome [80] .

In this subgroup meta-analysis, we compared chest CT findings in conformed COVID-19 pneumonia cases with the negative-PCR group, including suspected patients without PCR confirmations and non-COVID pneumonias such as H1N1 influenza infection [81] . Consolidation, which was observed more commonly in severe/ Random-effects REML model critical patients than in mild/moderate ones, had a higher chance for development in non-COVID pneumonias. In other words, the interpretation for observing consolidations in chest CTs depends on the groups we want to differentiate. If severity of the COVID-19 pneumonia is the question, consolidation favours severe/critical disease. Despite that, if we are going to differentiate COVID-19 pneumonias from non-COVID ones, consolidations in chest CT images make COVID-19 diagnosis less likely. After all, other diagnostic methods may be useful in differentiating such conditions.

CT abnormalities can also be present in COVID-19 patients without clinical symptoms. Wang et al. reported that 37 of 55 asymptomatic patients (67.2%) had abnormalities in their chest CT scans [82] . Similar observations were reported in two other studies, in which 17 of 24 [83] and 12 of 13 [84] asymptomatic patients had positive CT results (70.8% and 92.3%, respectively). However, due to insufficient data, we could not perform a meta-analysis of the imaging features of asymptomatic patients.

This systematic review has several limitations. The interval between the initiation of clinical symptoms and the acquisition of the first CT scan is an important factor because the evolution of lesions seems to occur after the early phase of the disease [85] . However, due to significant heterogeneity among the "first symptom first-CT" intervals in different studies, we did not consider this in our meta-analysis. Moreover, for the definitions of chest CT features, some studies used the Glossary of Terms for Thoracic Imaging by the Fleischer Society [86] , but others just reported the findings without an exact denotation. Unfortunately, we also did not consider the underlying Figure 8 . Forest plot comparing the mortality rate between patients with and without bilateral involvement (up), and with and without ground-glass opacities (down) Heterogeneity: t 2 = 0.10, I 2 = 89.59%, H 2 = 9.60 Test of θ = θ 1 : Q(7) = 33.23, p = 0.00 Test of θ = 0: z = 2.89, p = 0.00

Random-effects REML model conditions and risk factors of patients, which are shown to be important prognostic factors [49, 87] . Regarding paediatric patients, there were only six publications that had reported CT findings of 147 paediatric patients separately. Another limitation of this meta-analysis is that the follow-up duration of three publications comparing survivors and deceased patients was also different. Now, there are more than 100000 cases of COVID-19 in the USA, Spain, Italy, Germany, and France, but most of the included publications in this meta-analysis belong to China. In addition, for the calculation of the prevalence of bilateral involvement, we had to impute the number of cases for a few studies. This was because such papers reported only the numbers of patients with positive CT findings and those with unilateral involvement. As a result, we presumed bilateral involvement to be equal to the difference between these two values. It is worth mentioning that the results of this meta-analysis are all based on the proportion of patients with different CT features. During the full-text screening, we found some studies with quantitative reports of CT findings (e.g. CT scores, the total number of lesions and involved segments, the volume of involved lung tissue, etc.), but because of the extreme heterogeneity of scales and reporting methods, we did not include them in the analysis. Among the 86 included studies, most are retrospective. This implies the urgent need for more prospective cohort studies with control of confounding factors, which hopefully will result in a more accurate comparison of clinical and imaging features. Finally, the potential correlation between CT symptoms within each patient would imply the importance of the reader experience and performance. However, the available evidence is not sufficient to analyse the effect of the learning curve in this regard.

The pandemic of COVID-19 [88, 89] has posed challenges to health care systems [90] with prevention [91, 92] , diagnosis [93] , treatment [91, [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104] , and management [99, 105, 106] in both adult and paediatric settings [107] . Recent research could improve our understanding of immunopathogenesis [96,97,101,102,108-120,] and proximal origin of the virus [121, 122] . However, no specific treatment and prevention has been developed, and the pandemic continues to affect multiple organs and systems and people of diverse immunogenetic background [109, 111, 113, 114, 118, [123] [124] [125] [126] [127] [128] .

In conclusion, chest CT can be a relatively sensitive and fast tool for distinguishing COVID-19 in the adult population. However, because of the relatively low inci-dence of pulmonary lesions detectable by CT, it might not be an appropriate diagnostic tool for paediatrics. SARS-CoV-2-infected patients show different patterns of lung involvement in the chest CT images, with similarities and differences with other viral types of pneumonia. Regarding what we observed, COVID-19 patients most commonly present with GGO, with or without air bronchogram and consolidations, with the lower lobes being more dominantly involved. Further studies are required for a more accurate interpretation of CT features in patients with COVID-19 concerning disease severity and outcomes. Also, given the risk of re-infection [129] , it would be interesting to compare CT findings between people with first infection and people with re-infection. Scientific collaboration with systemic knowledge production [130] [131] [132] [133] is needed in such a critical condition [134] to deal effectively with a pandemic that has caused changes to the humanity in numerous dimensions [185] .

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Diagnosis of the coronavirus disease (COVID-19): rRT-PCR or CT?

Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China

Comparison of hospitalized patients with acute respiratory distress syndrome caused by COVID-19 and H1N1

WHO. Coronavirus disease (COVID-2019): Similarities and differences between COVID-19 and influenza. 2020. Available at

Covid-19: four fifths of cases are asymptomatic, China figures indicate

Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1

WHO. Coronavirus disease (COVID-2019) situation report 79. 2020. Available at

Open COVID-19 Data Curation Group. Open access epidemiological data from the COVID-19 outbreak

Global surveillance for COVID-19 caused by human infection with COVID-19 virus Interim guidance

Sensitivity of chest CT for COVID-19: comparison to RT-PCR

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

A role for CT in COVID-19? What data really tell us so far

A British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic

Viral pneumonias in adults: radiologic and pathologic findings

Radiographic and CT features of viral pneumonia

Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist

CT imaging features of 2019 novel coronavirus (2019-nCoV)

Emerging 2019 novel coronavirus (2019-nCoV) pneumonia

Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing

Epidemiological characteristics of 2143 pediatric patients with 2019 coronavirus disease in China

Diagnosis and treatment of 2019 novel coronavirus infection in children: a pressing issue

Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention

Exuberant elevation of IP-10, MCP-3 and IL-1ra during SARS-CoV-2 infection is associated with disease severity and fatal outcome

Comparison of hospitalized patients with acute respiratory distress syndrome caused by COVID-19 and H1N1

Clinical outcomes in 55 patients with severe acute respiratory syndrome coronavirus 2 who were asymptomatic at hospital admission in Shenzhen

Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing

Follow-up of the asymptomatic patients with SARS-CoV-2 infection

Temporal changes of CT findings in 90 patients with COVID-19 pneumonia: a longitudinal study

Fleischner Society: glossary of terms for thoracic imaging

Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study

COVID-19: a chimera of two pandemics

COVID-19: developing from an outbreak to a pandemic

International efforts to save healthcare personnel during COVID-19

COVID-19: transmission, prevention, and potential therapeutic opportunities

Country quarantine during COVID-19: critical or not

Microfluidic devices for detection of RNA viruses

COVID-19 and multiple sclerosis: predisposition and precautions in treatment

COVID-19 and picotechnology: potential opportunities

The immune system as a target for therapy of SARS-CoV-2: a systematic review of the current immunotherapies for COVID-19

Interleukin-6 and severe COVID-19: a systematic review and meta-analysis

Monoclonal antibody as a potential anti-COVID-19

Regenerative medicine in COVID-19 treatment: real opportunities and range of promises

Targeted therapy strategies against SARS-CoV-2 cell entry mechanisms: a systematic review of in vitro and in vivo studies

Towards treatment planning of COVID-19: rationale and hypothesis for the use of multiple immunosuppressive agents: anti-antibodies, immunoglobulins

Lymphopenia in COVID-19: therapeutic opportunities

The possible double-edged sword effects of vitamin D on COVID-19: a hypothesis

COVID-19 and medical biotechnology

Challenges for management of immune thrombocytopenia during COVID-19 pandemic

COVID-19 and telemedicine: Immediate action required for maintaining healthcare providers well-being

COVID-19 affects healthy pediatricians more than pediatric patients

Anosmia: a missing link in the neuroimmunology of coronavirus disease

Case report: Death due to COVID-19 in three brothers

Cytokines and COVID-19: friends or foes?

Death due to COVID-19 in an infant with combined immunodeficiencies

Efficacy of plasmapheresis and immunoglobulin replacement therapy (IVIG) on patients with COVID-19

Genetic predisposition models to COVID-19 infection

Hyperinflammatory shock related to COVID-19 in a patient presenting with multisystem inflammatory syndrome in children: first case from Iran

The immune system and COVID-19: friend or foe?

Immune-epidemiological parameters of the novel coronavirus -a perspective

Immunotherapy for SARS-CoV-2: potential opportunities

Kawasaki disease and multisystem inflammatory syndrome in children with COVID-19

Primary immunodeficiency diseases in COVID-19 pandemic: a predisposing or protective factor?

SARS-CoV-2 -a tough opponent for the immune system

Angiotensinconverting enzyme 2 (ACE2) receptor and SARS-CoV-2: potential therapeutic targeting

Questions concerning the proximal origin of SARS-CoV-2

Central nervous system involvement in COVID-19

COVID-19 in a child with primary specific antibody deficiency

Cutaneous lesions and COVID-19: cystic painful lesion in a case with positive SARS-CoV-2

The involvement of the central nervous system in patients with COVID-19

Adrenal insufficiency in coronavirus disease 2019: a case report

SARS-CoV-2: a comprehensive review from pathogenicity of the virus to clinical consequences

With risk of reinfection, is COVID-19 here to stay

All together to fight COVID-19

Borderless collaboration is needed for COVID-19 -a disease that knows no borders

The urgent need for integrated science to fight COVID-19 pandemic and beyond

Quantity does not equal quality: scientific principles cannot be sacrificed

COVID-19 pandemic is not the time of trial and error

A comparative study of chest computed tomography features in young and older adults with corona virus disease (COVID-19)

Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease

Chest CT features of COVID-19 in

Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection

Chest CT findings in patients with corona virus disease 2019 and its relationship with clinical features

Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury

Clinical and high-resolution CT features of the COVID-19 infection: comparison of the initial and follow-up changes

Clinical and immunologic features in severe and moderate Coronavirus Disease

Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China

Clinical characteristics of 30 medical workers infected with new coronavirus pneumonia. Zhonghua Jie He He Hu Xi Za Zhi 2020

Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China

Clinical characteristics of imported cases of COVID-19 in Jiangsu province: a multicenter descriptive study

Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Clinical features and short-term outcomes of 102 patients with corona virus disease 2019 in Wuhan, China

Clinical features of 85 fatal cases of COVID-19 from Wuhan: a retrospective observational study

Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management

Coronavirus disease 2019: initial chest CT findings

CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China

Early clinical and CT manifestations of coronavirus disease 2019 (COVID-19) pneumonia

Emerging 2019 novel coronavirus (2019-nCoV) pneumonia

Epidemiologic and clinical characteristics of 91 hospitalized patients with COVID-19 in Zhejiang, China: a retrospective, multi-centre case series

Epidemiological, clinical characteristics of cases of SARS-CoV-2 infection with abnormal imaging findings

High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages

High-resolution CT features of 17 cases of corona virus disease 2019 in Sichuan province, China

Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2

Imaging features of coronavirus disease 2019 (COVID-19): evaluation on thin-section CT

Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area

Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China

Novel coronavirus pneumonia (COVID-19) CT distribution and sign features

Novel coronavirus pneumonia (COVID-19) progression course in 17 discharged patients: comparison of clinical and thin-section CT features during recovery

Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study

Risk factors associated with disease progression in a cohort of patients infected with the 2019 novel coronavirus

The characteristics and clinical value of chest CT images of novel coronavirus pneumonia

Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia

Analysis on the clinical characteristics of 36 cases of novel coronavirus pneumonia in Kunming

Association of cardiovascular manifestations with in-hospital outcomes in patients with COVID-19: a hospital staff data

Clinical characteristics of 36 non-survivors with COVID-19 in Wuhan, China

Clinical features of patients infected with the 2019 novel coronavirus (COVID-19) in Shanghai, China

Clinical outcomes of patients with 2019-nCoV: a preliminary summary

Comparison of epidemiological and clinical features of patients

Coronavirus disease 2019 (COVID-19) pneumonia: early stage chest CT imaging features and clinical rele vance

Early prediction of disease progression in 2019 novel coronavirus pneumonia patients outside Wuhan with CT and clinical characteristics

Epidemiological and clinical characteristics of 17 hospitalized patients with 2019 novel coronavirus infections outside Wuhan, China

Epidemiological and clinical features of 291 cases with coronavirus disease 2019 in areas adjacent to Hubei, China: a double-center observational study

How to differentiate COVID-19 pneumonia from heart failure with computed tomography at initial medical contact during epidemic period

Imaging profile of the COVID-19 infection: radiologic findings and literature review

Transmission and clinical characteristics of coronavirus disease 2019 in 104 outside-Wuhan patients, China

A comparative study on the clinical features of COVID-19 pneumonia to other pneumonias

Coronavirus Disease-COVID-19

The authors report no conflict of interest.