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DOI: 10.4103/ijri.IJRI_303_20
Spectrum of chest CT manifestations of coronavirus disease (COVID-19): A pictorial essay
Abstract
Coronavirus disease (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is an enveloped single-stranded RNA virus belonging to the family of betacoronaviruses. Chest computed tomography (CT) has helped us in understanding this new disease. Typical CT features of COVID-19 pneumonia are ground-glass opacities (GGO), crazy paving pattern and GGO with superimposed consolidation with a basal, posterior and peripheral lung predilection. Less commonly bronchial wall thickening, bronchial dilatation and pleural thickening are seen. Presence of pleural effusion, pericardial effusion and mediastinal lymphadenopathy is seen in severe cases. Reticulations, fibrous stripes, reverse halo sign and perilobular opacities are seen late (>2 weeks) in the course of illness. We aim to present a pictorial review of CT imaging findings in COVID-19 to illustrate the typical and atypical manifestations of this disease in a bid to familiarize radiologists with the myriad imaging manifestations of this disease.
#
Introduction
Coronavirus disease (COVID-19) originated in Wuhan, China towards the end of 2019 and swiftly spread across the world infecting an estimated 6.5million people and claiming 382 867 lives till June 06, 2020.[[1]] COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is an enveloped single-stranded RNA virus belonging to the family of betacoronaviruses.[[2]] The disease is highly contagious and spreads through the respiratory route with lungs being the primary organs affected. Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) performed on respiratory secretions is considered to be the standard diagnostic test.[[3]] However, RT-PCR has a reported sensitivity of 60–71% with many false-negative reported cases.[[4]] Imaging serves as a complementary tool in confirming the diagnosis, especially in RT-PCR negative cases. Chest radiography is the preliminary investigation used for patients with suspected COVID-19. Chest radiographs may be normal in early or mild disease. However, up to 69% of patients with COVID-19 requiring hospitalization have an abnormal chest radiograph at the time of admission, and up to 80% show radiographic abnormalities sometime during hospitalization.[[5]] Chest computed tomography (CT) is sensitive to the detection of COVID-19 pneumonia. It is also useful in monitoring the progress of the disease or in monitoring the response to treatment. Nuclear imaging procedures (18F-FDG PET/CT or131I SPECT/CT) performed for various standard clinical conditions may incidentally detect signs of COVID-19 pneumonia with increased metabolic activity in the pulmonary lesions and mediastinal lymph nodes.[[6]] Given the increasing number of COVID-19 cases globally, many radiologists will incidentally encounter chest imaging of COVID-19 patients. So, familiarity with the imaging manifestations of this disease is essential. We performed a literature search of PubMed and Embase databases initially on May 17, 2020, and updated on June 9, 2020. After screening the titles and abstracts, 15 original studies and two review articles reporting CT imaging features of COVID-19 from countries like China, Europe and India were selected. Based on a compendious review of these published studies and our own experience in interpreting COVID-19 imaging in the frontline, we aim to present a pictorial review of CT imaging findings in COVID-19 to illustrate the typical and atypical manifestations of this disease in a bid to familiarize radiologists with the myriad imaging manifestations of this disease. The study was approved by the Institutional Review Board (IRB) and the requirement of informed patient consent was waived off.
#
Main Observations
Bilateral, peripheral and basal ground-glass opacities with multilobar involvement have been described as the initial CT manifestations of COVID-19 pneumonia.[[1]] During the intermediate stage of disease, progressive transformation of GGOs into consolidations occurs with the development of interlobular septal thickening producing characteristic crazy paving patterns. The CT findings reach a crescendo around the tenth day of symptom onset. Some patients deteriorate and develop extensive lung opacities leading to acute respiratory distress syndrome (ARDS), which is the main cause of death. In patients with clinical recovery, there is a gradual resolution of consolidative changes with a reduction in both the size and number of these opacities with a new development of reticulations and fibrous stripes, usually observed after 2 weeks. Pleural effusion, pericardial effusion, mediastinal lymphadenopathy, halo sign or reverse halo sign are uncommon but possible CT features of COVID-19 seen with disease progression.[[7], [8], [9], [10], [11], [12], [13], [14], [15]] [[Table 1]] provides a summary of different pulmonary findings in COVID-19 reported across various studies.
Parameter (%) |
Zhao (12) |
Bai (21) |
Bernheim (9) |
Ai (11) |
Guan (10) |
Han (13) |
Parry (17) |
Akin (19) |
---|---|---|---|---|---|---|---|---|
Number of patients |
101 |
219 |
121 |
1014 |
1099 |
108 |
211 |
185 |
Bilateral lung involvement |
82 |
75 |
60 |
- |
52 |
- |
76.4 |
86.4 |
Peripheral distribution |
87 |
80 |
52 |
- |
- |
90 |
100 |
87.1 |
Posterior distribution |
- |
- |
- |
- |
- |
100 |
46.3 |
|
Multilobar involvement |
- |
- |
85 |
- |
- |
65 |
- |
- |
GGO |
86 |
91 |
34 |
46 |
56 |
60 |
100 |
82.3 |
GGO and consolidation |
64 |
69 |
41 |
50 |
- |
41 |
47.2 |
32.7 |
Crazy-paving pattern |
- |
5 |
5 |
- |
- |
40 |
32.6 |
21.8 |
Air bronchogram |
- |
14 |
- |
- |
- |
48 |
24.7 |
23.1 |
Bronchial dilatation |
53 |
- |
1 |
- |
- |
- |
- |
19 |
Pleural effusion |
14 |
4 |
1 |
- |
- |
- |
1.5 |
2 |
Pleural thickening |
- |
15 |
- |
- |
- |
- |
- |
12.9 |
Pericardial effusion |
- |
- |
- |
- |
- |
- |
0 |
0 |
Lymphadenopathy |
1 |
3 |
0 |
- |
- |
- |
0 |
12.2 |
Bronchial wall thickening |
29 |
9 |
12 |
- |
- |
- |
- |
- |
Reticular pattern |
49 |
35 |
7 |
1 |
- |
- |
29.2 |
- |
Sub pleural lines |
28 |
- |
- |
- |
- |
- |
18 |
27.9 |
Reverse halo sign |
- |
5 |
2 |
- |
- |
- |
18 |
15 |
Nodules |
23 |
32 |
0 |
3 |
- |
- |
0 |
18.4 |
Vessel enlargement |
71 |
59 |
- |
- |
- |
80 |
67.4 |
34 |
We performed non-contrast chest CT in 179 non-consecutive RT-PCR confirmed SARS-CoV-2 infected patients. Among the total study population, 152 (84.9%) patients were symptomatic and 27 (15.1%) patients were asymptomatic. Among the symptomatic patients, the most common symptoms reported were fever (73%), cough (49%), myalgia (61%), fatigue (66%), sore throat (23%), breathlessness (9%), hyposmia/anosmia (4%) and dysguesia (3%).The CT findings of our study cohort are summarized in [[Table 2]].
Parameter |
All patients (n=179) |
Symptomatic patients (n=152; 84.9%) |
Asymptomatic patients (n=27; 15.1%) |
---|---|---|---|
CT findings |
|||
Present |
104 (58.1%) |
95 (62.5%) |
9 (33.3%) |
Absent |
75 (41.9%) |
57 (37.5%) |
18 (66.7%) |
Laterality of lung involvement |
|||
Unilateral |
19 (18.3%) |
13 (13.7%) |
6 (66.7%) |
Bilateral |
85 (81.7%) |
82 (86.3%) |
3 (33.3%) |
Focality |
|||
Unifocal |
9 (8.7%) |
4 (4.2%) |
5 (55.5%) |
Multifocal |
95 (91.3%) |
91 (95.7%) |
4 (44.5%) |
Axial distribution |
|||
Peripheral predominant |
96 (92.3%) |
88 (92.6%) |
8 (88.9%) |
Central and peripheral |
8 (7.7%) |
7 (7.4%) |
1 (11.1%) |
Antero-posterior distribution |
|||
Posterior predominant |
91 (87.5%) |
84 (88.4%) |
7 (77.8%) |
Anterior and posterior |
13 (12.5%) |
11 (11.6%) |
2 (22.2%) |
Type of lung opacity |
|||
Pure GGO |
64 (61.5%) |
56 (58.9%) |
8 (88.9%) |
GGO with consolidation |
18 (17.3%) |
18 (18.9%) |
- |
Crazy-paving pattern |
14 (13.5%) |
14 (14.7%) |
- |
Pure consolidation |
6 (5.8%) |
6 (6.3%) |
- |
Nodules |
2 (1.9%) |
1 (1.1%) |
1 (11.1%) |
Additional CT findings |
|||
Vessel dilatation sign |
71 (68.3%) |
71 (74.7%) |
- |
Reverse Halo sign |
17 (16.3%) |
15 (15.8%) |
2 (22.2%) |
Halo sign |
1 (0.9%) |
1 (1.1%) |
- |
Bronchial dilatation |
5 (4.8%) |
5 (5.3%) |
- |
Bronchial wall thickening |
4 (3.8%) |
4 (4.2%) |
- |
Air bubble sign |
6 (5.8%) |
5 (5.3%) |
1 (11.1%) |
Reticulations |
14 (13.5%) |
10 (10.5%) |
4 (44.4%) |
Subpleural lines |
13 (12.5%) |
10 (10.5%) |
3 (33.3%) |
Fibrous stripes |
6 (5.8%) |
6 (6.3%) |
- |
Perilobular sign |
5 (4.8%) |
5 (5.3%) |
- |
Pleural thickening |
4 (3.8%) |
4 (4.2%) |
- |
Pleural effusion |
4 (3.8%) |
4 (4.2%) |
- |
Mediastinal lymphadenopathy |
3 (2.9%) |
3 (3.2%) |
- |
Important negative findings |
|||
Tree-in-bud appearance |
0 |
||
Cavitation |
0 |
||
Pneumothorax |
0 |
||
Pericardial effusion |
0 |
Ground-glass opacity (GGO)
GGO is a descriptive term that denotes an area of increased lung attenuation on CT through which vascular and bronchial structures can be seen [[Figure 1]]. It results from the partial filling of alveoli with fluid, blood or cells or due to the thickening of pulmonary interstitium. COVID-19 has been typically described to present with multifocal GGOs with a basal, peripheral and posterior distribution. The GGOs can be patchy or confluent, rounded or elongated. Salehi et al.[[7]] in a systematic review of 22 studies found GGO as the commonest CT manifestation in COVID-19 with a cumulative prevalence of 88%. Parry et al.[[8]] reported GGO with a cumulative prevalence of 100%. Bernheim et al.[[9]] reported the presence of pure GGOs in 34% and GGOs mixed with consolidation in 41%. Caruso D et al.[[16]] reported GGOs in 100% of their patients.
#
Crazy-paving pattern
It refers to GGO with superimposed interlobular thickening producing a crazy pavement like pattern thus earning it the moniker of the crazy-paving pattern [[Figure 2]]. Crazy-paving pattern has been variably reported from 5% to 40% in COVID-19 pneumonia.[[9], [13], [17]] Its incidence also increases with the progression of disease and is seen predominantly around the eighth day of infection.
#
Consolidation
Consolidation connotes an increase in pulmonary attenuation with obscuration of underlying vascular and bronchial structures and pathologically represents flooding of air-filled alveolar spaces with fluid, blood or inflammatory cells [[Figure 3]]. Consolidations are seen increasingly in COVID-19 as the infection progresses with a peak incidence at 13–16 days. Consolidations are found more commonly superimposed on GGOs whereas pure consolidations are less common.[[7], [8]] Consolidations also have a peripheral and bilateral distribution akin to GGOs. Unilobar pure consolidation is seldom a feature of COVID-19 and should alert one to consider an alternate diagnosis of bacterial pneumonia.
#
Reticular pattern
Reticular pattern is characterized by a collection of innumerable interweaving linear or wavy shadows producing a mesh-like pattern on CT and results from a varying combination of interlobular and intralobular septal thickening [[Figure 4]]. They are found during the resorptive phase of the disease.[[8], [18]]
#
Subpleural curvilinear lines
On chest CT subpleural lines are represented by thin (1-3mm) curvilinear shadows lying within 1cm of pleural margin and coursing parallel to it [[Figure 4]]. It is pathologically represented by pulmonary edema or developing fibrosis. It has been reported with an incidence of 17–28% in COVID-19 patients.[[8], [12]]
#
Air bubble sign
Air bubble sign is the presence of a small air containing lucency within a GGO or consolidation and possibly represents entrapped physiological air space or cross-section of a small dilated bronchus or might represent the early evidence of resorption of consolidation [[Figure 5]]. It was initially reported in COVID-19 patients by Shi et al.[[18]] who called it round cystic change followed by Kong et al.[[3]] who referred to it as a cavity sign. But a glance through the depicted pictures in these studies led us to conclude that air bubble lucency would be an appropriate term for it.
#
Pulmonary nodules
Nodule represents a round or irregular opacity less than 3cm in the longest dimension [[Figure 6]]. Nodules are uncommonly encountered in COVID-19. It has been variably reported in COVID-19 with an incidence of 3–32% [[11], [19], [20], [21]]
#
Halo sign
It represents a ground-glass haze surrounding a nodule on CT [[Figure 6]]. Though classically seen in angioinvasive aspergillosis and hypervascular metastasis where it represents the area of perilesional hemorrhage it has been reported in COVID-19 with a frequency of 3–12%[[16], [18]]
#
Reverse Halo sign or atoll sign
It manifests as a region of ground-glass haze surrounded by a complete or incomplete ring of consolidation [[Figure 7]]. Thought initially to be specific of cryptogenic organizing pneumonia (COP) it was subsequently reported in other pathologies. It has been seen in progressive or resorptive stages of COVID-19.[[14], [22]] It might represent disease progression with peripheral areas of GGO transforming into consolidation. The converse is also possible where central GGO might be an area of resorption in the midst of a consolidation.
#
Fibrosis
Fibrous stripes or areas of fibrosis in COVID-19 were reported by Pan et al.[[14]] in 17% of patients. It represents the healing of areas of pneumonia with the formation of fibrosis [[Figures 8] and [9]].
#
Airway changes
Bronchial wall thickening and bronchial dilatation may be secondary to bronchial wall inflammation and destruction with surrounding pulmonary parenchymal damage also partly contributing to the bronchial dilatation [[Figure 9]].[[17]] Bronchial wall thickening has been reported in around 9–29% COVID-19 patients whereas bronchial dilatation has been reported in some cases.[[12], [21]]
#
Vascular changes
Segmental or subsegmental pulmonary vascular enlargement on CT chest seems to be a specific feature associated with COVID-19 [[Figure 10]]. Parry et al.[[8]] and Caruso D et al.[[16]] reported vascular enlargement in 70% and 89% of COVID-19 pneumonia, respectively. Bai et al.[[21]] described vascular enlargement to be frequently associated with COVID-19 pneumonia compared to non-COVID-19 pneumonia with a significant P value (<0.001). Small pulmonary vessel enlargement has been linked to the in-situ immunothrombosis of small pulmonary vessels in COVID-19 pneumonia.[[23]]
#
Pleural changes
Pleural effusion [[Figure 11]] has been reported uncommonly in COVID-19 patients with a varying incidence of 1–14%.[[12], [17]] Pleural thickening [[Figure 8]] has been reported in some patients.[[19], [21]]
#
Pericardial effusion
Pericardial effusion is uncommonly seen in COVID-19 patients. It has been reported to occur in severe or critically ill patients and possibly represents florid inflammation [[8], [18]]
#
Mediastinal lymphadenopathy
Enlargement of mediastinal nodes (>10mm in short axis) has been infrequently seen in COVID-19 patients especially in critically sick COVID-19 patients and was thus considered as a risk factor of severe or critical disease.[[12], [19]] However, the presence of lymphadenopathy with effusion and numerous pulmonary nodules may suggest bacterial superinfection.
#
Perilobular opacities and organizing pneumonia
Understandably, there is a paucity of literature regarding the long term pulmonary sequelae of COVID-19. The follow-up imaging late in the course of the disease (>2 weeks) usually shows a reduction in the extent of GGO with development of a mixed pattern of lung abnormalities consisting of arc-like perilobular opacities, subpleural curvilinear lines and subpleural fibrous stripes with architectural distortion of lungs [[Figure 12]]. The development of perilobular opacities suggests organizing pneumonia.[[22]] Viral pneumonias like severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and influenza are known to produce secondary organizing pneumonia. The currently available evidence also points to the development of secondary organizing pneumonia in the survivors of COVID-19.
Organizing pneumonia is the precursor to the development of lung fibrosis. However, given the fact that corticosteroid therapy has been previously shown effective in the treatment of organizing pneumonia, use of follow-up CT scans to detect the development of organizing pneumonia in COVID-19 patients and early institution of corticosteroid therapy may reduce the possibility of development of lung fibrosis in the survivors.[[22]]
#
#
Conclusion
In conclusion, although bilateral peripheral GGOs and consolidation are the predominant imaging manifestations of COVID-19 pneumonia, imaging findings can vary in different stages and patients. Familiarity with the myriad CT manifestations of COVID-19 pneumonia is essential to clinch an appropriate diagnosis.
#
Implications
In the current epidemic context, the presence of respiratory symptoms with imaging evidence of pulmonary opacities (GGO or consolidation) immediately brings the diagnosis of COVID-19 to mind. However, there are numerous infectious and noninfectious diseases that mimic COVID-19 on CT [[Table 3]]. Viral pneumonia of other etiologies (like influenza A and B, SARS, MERS), bacterial pneumonia (typical and atypical) and pneumocystis jiroveci pneumonia are infectious mimics of COVID-19 pneumonia. Among noninfectious causes, pulmonary edema, alveolar hemorrhage and drug-induced pneumonitis are the most notable mimics.
Parameter |
COVID-19 Pneumonia |
Other Viral pneumonias (Influenza) |
Bacterial pneumonia |
Pneumocystis jiroveci pneumonia |
Pulmonary edema |
Alveolar hemorrhage |
Drug-induced pneumonitis |
---|---|---|---|---|---|---|---|
Distribution |
Bilateral posterior peripheral basal |
Diffuse |
Confined to a lobe or segment |
Central with subpleural sparing |
Central predominance |
Diffuse |
Diffuse |
Dominant opacity |
GGO |
GGO |
Air-space consolidation |
GGO |
GGO |
GGO |
GGO or organizing pneumonia |
Peculiarity (imaging or clinical) |
Segmental vessel enlargement |
Pleural effusion (30%) Bronchial wall thickening (45%) |
Centrilobular nodules, mucoid impaction of bronchi, lymphadenopathy, effusion |
Underlying immunodeficiency |
Interlobular septal thickening and effusion |
Small cavitatory nodules/ hemoptysis/ renal failure |
History of drug intake |
#
#
Conflict of Interest
There are no conflicts of interest.
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References
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- 2 Chen Y, Li L. SARS-CoV-2: Virus dynamics and host response. Lancet Infect Dis 2020.
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- 14 Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T. et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): A study of 63 patients in Wuhan, China. EurRadiol 2020; 30: 3306-9
- 15 Zhang R, Ouyang H, Fu L, Wang S, Han J, Huang K. et al. CT features of SARS-CoV-2 pneumonia according to clinical presentation: A retrospective analysis of 120 consecutive patients from Wuhan city. Eur Radiol 2020; 30: 4417-26
- 16 Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C. et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology 2020:201237.doi: 10.1148/radiol. 2020201237.
- 17 Parry AH, Wani AH, Yaseen M, Jehangir M. Chest CT features of coronavirus disease-19 (COVID-19) pneumonia: Which findings on initial CT can predict an adverse short-term outcome?. BJR | Open 2020; 2: 20200016 DOI: 10.1259/bjro.20200016.
- 18 Ye Z, Zhang Y, Wang Y, Huang Z, Song B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): A pictorial review. Eur Radiol 2020; 30: 4381-9
- 19 Akın Ç, Cenk H, Selen B, Naim C, Recep S. CT Imaging features of COVID-19 pneumonia: Initial experience from Turkey. Diagn Interv Radiol 2020; 26: 308-14
- 20 Parry AH, Wani AH. Segmental pulmonary vascular changes in COVID-19 pneumonia. AJR Am J Roentgenol 2020:W1.
- 21 Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TM. et al. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology 2020:200823.doi: 10.1148/radiol. 2020200823.
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Address for correspondence
Publication History
Received: 25 April 2020
Accepted: 17 July 2020
Article published online:
13 July 2021
© 2021. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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References
- 1 World Health Organization. Coronavirus disease 2019 (COVID-19): Situation report, 136.
- 2 Chen Y, Li L. SARS-CoV-2: Virus dynamics and host response. Lancet Infect Dis 2020.
- 3 Kong WH, Li Y, Peng MW, Kong DG, Yang XB, Wang L. et al. SARS-CoV-2 detection in patients with influenza-like illness. Nat Microbiol 2020; 5: 675-8
- 4 Yang Y, Yang M, Shen C. et al. Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections. medRxiv 2020. doi: 10.1101/2020.02.11.20021493.
- 5 Wong HY, Lam HY, Fong AH, Leung ST, Chin TW, Lo CS. et al. Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology 2020:201160.doi: 10.1148/radiol. 2020201160.
- 6 Albano D, Bertagna F, Bertoli M, Bosio G, Lucchini S, Motta F. et al. Incidental findings suggestive of COVID-19 in asymptomatic patients undergoing nuclear medicine procedures in a high-prevalence region. J Nucl Med 2020; 61: 632-6
- 7 Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19): A systematic review of imaging findings in 919 patients. AJR Am J Roentgenol 2020; 215: 87-93
- 8 Parry AH, Wani AH, Yaseen M, Jehangir M, Choh NA, Dar KA. Spectrum of chest computed tomographic (CT) findings in coronavirus disease-19 (COVID-19) patients in India. Eur J Radiol 2020; 129: 109147
- 9 Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N. et al. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology 2020; 295: 200463
- 10 Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX. et al. Clinical characteristics of coronavirus disease 2019 in China. N EnglJMed 2020; 382: 1708-20
- 11 Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W. et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology 2020:200642.doi: 10.1148/radiol.2020200642.
- 12 Zhao W, Zhong Z, Xie X, Yu Q, Liu J. Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: A multicenter study. Am J Roentgenol 2020; 214: 1072-7
- 13 Han R, Huang L, Jiang H, Dong J, Peng H, Zhang D. Early clinical and CT manifestations of coronavirus disease 2019 (COVID-19) pneumonia. AJR Am J Roentgenol 2020:1-6.doi: 10.2214/AJR.20.22961.
- 14 Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T. et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): A study of 63 patients in Wuhan, China. EurRadiol 2020; 30: 3306-9
- 15 Zhang R, Ouyang H, Fu L, Wang S, Han J, Huang K. et al. CT features of SARS-CoV-2 pneumonia according to clinical presentation: A retrospective analysis of 120 consecutive patients from Wuhan city. Eur Radiol 2020; 30: 4417-26
- 16 Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C. et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology 2020:201237.doi: 10.1148/radiol. 2020201237.
- 17 Parry AH, Wani AH, Yaseen M, Jehangir M. Chest CT features of coronavirus disease-19 (COVID-19) pneumonia: Which findings on initial CT can predict an adverse short-term outcome?. BJR | Open 2020; 2: 20200016 DOI: 10.1259/bjro.20200016.
- 18 Ye Z, Zhang Y, Wang Y, Huang Z, Song B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): A pictorial review. Eur Radiol 2020; 30: 4381-9
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