• Users Online: 167
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 3  |  Page : 101-107

Computed tomography findings and clinical manifestations in different clinical types of coronavirus disease 2019


1 Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
2 Department of Radiology, The First People's Hospital, Wuhan, China
3 Department of Radiology, The Second Xiangya Hospital, Central South University; Department of Radiology, Quality Control Center, Changsha, China

Date of Submission04-Mar-2021
Date of Acceptance28-Jul-2021
Date of Web Publication5-Apr-2022

Correspondence Address:
Dr. Jun Liu
Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/RID.RID_6_22

Rights and Permissions
  Abstract 


OBJECTIVE: Since the coronavirus disease 2019 (COVID-19) outbreak in Wuhan in 2019, the virus has spread rapidly. We investigated the clinical and computed tomography (CT) characteristics of different clinical types of COVID-19.
MATERIALS AND METHODS: We retrospectively analyzed clinical and chest CT findings of 89 reverse transcription polymerase chain reaction confirmed cases from five medical centers in China. All the patients were classified into the common (n = 65), severe (n = 18), or fatal (n = 6) type. CT features included lesion distribution, location, size, shape, edge, density, and the ratio of lung lesions to extra-pulmonary lesions. A COVID-19 chest CT analysis tool (uAI-discover-COVID-19) was used to calculate the number of infections from the chest CT images.
RESULTS: Fatal type COVID-19 is more common in older men, with a median age of 65 years. Fever was more common in the severe and fatal type COVID-19 patients than in the common type patients. Patients with fatal type COVID-19 were more likely to have underlying diseases. On CT examination, common type COVID-19 showed bilateral (68%), patchy (83%), ground-glass opacity (48%), or mixed (46%) lesions. Severe and fatal type COVID-19 showed bilateral multiple mixed density lesions (56%). The infection ratio (IR) increased in the common type (2.4 [4.3]), severe type (15.7 [14.3]), and fatal type (36.9 [14.2]). The IR in the inferior lobe of both lungs was statistically different from that of other lobes in common and severe type patients (P < 0.05). However, in the fatal type group, only the IR in the right inferior lung (RIL) was statistically different from that in the right superior lung(RUL), right middle lung (RML), and the left superior lung (LSL) (P < 0.05).
CONCLUSION: The CT findings and clinical features of the various clinical types of COVID-19 pneumonia are different. Chest CT findings have unique characteristics in the different clinical types, which can facilitate an early diagnosis and evaluate the clinical course and severity of COVID-19.

Keywords: Clinical manifestations, clinical type, coronavirus disease 2019 pneumonia, computed tomography imaging


How to cite this article:
Tang F, Huang S, Xie X, Yang R, Wang X, Zhou J, Liu J. Computed tomography findings and clinical manifestations in different clinical types of coronavirus disease 2019. Radiol Infect Dis 2021;8:101-7

How to cite this URL:
Tang F, Huang S, Xie X, Yang R, Wang X, Zhou J, Liu J. Computed tomography findings and clinical manifestations in different clinical types of coronavirus disease 2019. Radiol Infect Dis [serial online] 2021 [cited 2022 Aug 20];8:101-7. Available from: http://www.ridiseases.org/text.asp?2021/8/3/101/342623




  Introduction Top


Since December 2019, numerous patients with pneumonia infected by a novel coronavirus have been found in Wuhan, Hubei province, China.[1] On February 11, 2020, the International Committee on Taxonomy of Viruses announced the official classification of the novel coronavirus as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), underscoring the similarity between the new virus and the SARS virus.[2] On the same day, the World Health Organization announced the official name of the disease caused by the virus as coronavirus disease 2019 (COVID-19).[3] With the spread of the disease, such cases have also been found in other parts of China and abroad. Most of the reported cases have a history of exposure to Wuhan or close contact with confirmed cases.

Based on COVID-19 guidelines (trial version 8),[4] the disease can be classified into the following four types: (1) Mild type, with mild clinical symptoms and normal imaging findings; (2) common type, with fever, respiratory symptoms, and radiographic manifestations of pneumonia; (3) severe type, with respiratory distress, RR ≥30 times/min, oxygen saturation <93% in the resting state, partial arterial oxygen pressure (PaO2)/oxygen absorption concentration (FiO2) ≤300 mmHg, and imaging lesions that progress >50% in 24–48 h; and (4) fatal type, with respiratory failure requiring mechanical ventilation, shock, and other organ failure requiring admission to an intensive care unit.

Our previous studies showed that computed tomography (CT) is of great significance in the diagnosis and prognosis of COVID-19.[5],[6] High-resolution CT cannot only display mild lesions at the millimeter level but can also accurately quantify the range and degree of the lesions which helps to assess the disease severity. Therefore, this study explored the correlation between CT findings and clinical manifestations in the different clinical types of COVID-19. The volume of pulmonary lesions was quantitatively analyzed by artificial intelligence (AI), aiming to improve the understanding of this disease, and provide important reference values for early clinical diagnosis and for the assessment of disease severity and course.


  Materials and Methods Top


This study was approved by our Medical Ethical Committee with Approval Number KL-2020001. The requirement for patients' informed consent was waived according to the CIOMS guideline.

The CT imaging and clinical manifestations of 89 patients with COVID-19 pneumonia from January 13 to February 20, 2020 were retrospectively analyzed through the infrastructure of the Radiology Quality Control Center, Hunan province, China. All the available clinical and epidemic (whether the patient had been in contact with other confirmed cases) characteristics were collected. The inclusion criteria were as follows: (1) Two positive COVID-19 nucleic acid tests (reverse transcription polymerase chain reaction method); (2) Chest CT images were obtained from the first chest CT examination after the onset of the disease; and (3) antibody testing for nine other respiratory tract viruses was negative. The exclusion criteria were as follows: (1) Non-COVID-19 patients; (2) recheck COVID-19 patients; and (3) the quality of chest CT images was poor and could not be used for image analysis.

Patient population

We divided patients into four groups: mild type, common type, severe type, and fatal type based on COVID-19 guidelines (Trial Version 7). Because of the absence of pneumonia imaging findings in the mild type, patients with mild type were not included in this study. Among the 89 COVID-19 patients enrolled (45 females, 44 males; mean age, 44.65 years ± 17.38 standard deviation; age range, 7–84 years), 65 were diagnosed with the common type, 18 with the severe type, and six with the fatal type. Clinical symptoms were recorded in all patients, including fever, cough, fatigue, chest tightness, expectoration, and pharyngeal pain. Routine peripheral blood testing (white blood cell [WBC] count and lymphocyte count) and hypersensitive C-reactive protein measurements were performed in all patients. No patients have hypersensitivity.

Imaging technique

All patients were scanned using one of the following three scanners: GE, HiSpeed-Dual, 64-slice Light Speed VCT (GE Medical Systems, USA), or Somatom emotion (Siemens Medical Solutions, Germany). The acquisition parameters were as follows: 120 kVp; 100-200 mAs; pitch, 0.75–1.5; and collimation, 0.625–5 mm. All imaging data were reconstructed by using a medium sharp reconstruction algorithm with a thickness of 0.625–5 mm. CT images were acquired in the supine position at full inspiration for all patients.

Imaging interpretation

Two experienced cardiothoracic radiologists with more than 10 years of experience reviewed all chest CT images, independently, and blinded to the clinical status of each patient. When the radiologists disagreed, they discussed and reached a consensus that was confirmed by a third cardiothoracic radiologist with more than 10 years of experience. All images were viewed on both lung and mediastinal settings. The CT findings of each patient were evaluated according to the following typical characteristic parameters: (1) Lesion distribution: Left lung (LL) (left superior lung, left inferior lung), right lung (RL) (right superior lung, right middle lung, right inferior lung), bilateral lungs; (2) lesion location: Peripheral or peripheral and central involvement; (3) lesion size: The diameter of the largest lesion was <1 cm, 1–3 cm, or >3 cm; (4) lesion morphology: Plaques, pulmonary segments, or pulmonary lobes; (5) lesion margins: Clear or indistinct; (6) lesion density: ground-glass opacity (GGO) solid, or mixed type; (7) extra-pulmonary manifestations: Enlarged lymph nodes and/or pleural effusion; and (8) the percentage of infection ratio (IR) in the lung lobe (including the whole lung [WL]-, LL, RL, left superior lung [LSL], left inferior lung [LIL], right superior lung [RSL], right middle lung [RML], and right inferior lung [RIL]).

The infection ratio of lesions in chest computed tomography images

In this study, a COVID-19 chest CT analysis tool (uAI-Discover-COVID-19),[7] developed by Shanghai United Imaging Intelligence Co., Ltd., was used to calculate the ratio of infections from the chest CT images. According to the segmentation results, the ratio of infections that are potentially related to COVID-19 was calculated. Specifically, the lung was segmented and divided into five lung lobes, i.e., superior/middle/inferior lobes of the RL (RSL, RML, and RIL) and superior/inferior lobes of the LL (LSL and LIL). The infection volume (IV) and ratio (IR) of the WL, RL/LL, and each lobe were calculated as quantitative features, as defined below:

IV(χ) = Vinfect)

IR(χ) =IV(χ)/V(χ) =Vinfect)/V(χ)

Where V(.) is the volume of input region, χ∈{WL, RL, LL, RSL, RML, RIL, LSL and LIL} and χ infect is the infected regions in χ.

Statistical analysis

All statistical analyses were performed using the SPSS software (version 24.0, USA). Qualitative data are expressed by frequency and rate. Quantitative data with a normal distribution are represented by X ± S; if not, by median (quartile spacing) (M[Q]). The clinical and CT features of different clinical subtypes were compared using the Chi-square test or Fisher's exact probability. The comparisons between the area of pulmonary lesions and lung volume were computed by using the analysis of variance (normal distribution) or the Kruskal − Wallis rank sum test (nonnormal distribution). The Mann–Whitney test was used for pairwise comparison. P < 0.05 was considered statistically significant.


  Results Top


Clinical characteristics

Among the 89 patients with COVID-19 (65 with the common type; 18 with the severe type; 6 with the fatal type; male 44, female 45), there were 77 (87%) cases of fever, 87 (98%) cases of cough, 10 (11%) cases of fatigue, 11 (12%) cases of chest tightness, 15 (17%) cases of expectoration, and 17 (19%) cases of pharyngeal pain. Fatal type patients were more often older men, with a median age of 65 years. Fever was the first symptom in 54 (83%) of the 65 patients with common type COVID-19 and cough was seen in all 65 common type COVID-19 patients. Of the severe patients, 17 (94%) presented with fever and 16 (89%) presented with cough. All six fatal type patients presented with cough and fever. Fever was more common in severe and fatal type COVID-19 patients than in common type patients. The incidence of pharyngeal pain, fatigue, and chest tightness was higher in fatal type patients than in severe and common type patients. In addition, 36 patients had underlying diseases, such as hypertension, diabetes, coronary heart disease, and liver or kidney dysfunction. Fatal type patients were more likely to have underlying diseases than those with severe or common type COVID-19. More than half of the fatal type patients (67%) had underlying disease. The peripheral blood leukocyte count was normal or decreased in 47 (53%) patients and the lymphocyte count was decreased in 42 (47%) patients. The rate of abnormal WBC and neutrophil counts in severe (100% and 78%, respectively) and fatal (67% and 83%, respectively) type COVID-19 patients were higher than that in common type patients (38% and 35%, respectively). Not surprisingly, all patients had a history of endemic exposure in Wuhan or close contact with a confirmed case. Two of the fatal type patients died in-hospital. The clinical characteristics of patients with different clinical types of COVID-19 are shown in [Table 1].
Table 1: Clinical characteristics of patients with different clinical types of coronavirus disease 2019 (case)

Click here to view


Computed tomography findings

In the CT findings, the majority of cases were characterized by GGO (38%) or mixed density (51%) and consolidation. The common type COVID-19 patients showed bilateral lung (68%), multiple, patchy (83%), GGO (48%), or mixed (46%) lesions as shown in [Figure 1]. Whereas severe and fatal type COVID-19 patients showed bilateral lung lesions (100%), and severe type COVID-19 patients showed multiple mixed density lesions (56%). The CT findings of all severe and fatal type patients were distributed in both lungs, mainly as multiple, patchy, mixed density foci, often involving the center and periphery of the lung as shown in [Figure 2] and [Figure 3]. Six fatal type COVID-19 patients had mixed density lesions >3 cm in size. The lesion margins were often clear (74%). Only one fatal type case showed pleural effusion. No mediastinal lymphadenopathy was seen in any cases. The CT findings in both severe and fatal type patients were bilateral and multifocal. The chest CT findings in patients with different types of COVID-19 are shown in [Table 2].
Figure 1: Chest computed tomography imaging of two common type COVID-19 patients. Patient 1. (a-c), In a 63-year-old man with a fever and cough for 2 days, computed tomography scan of the axial plane and coronal plane pulmo nary window revealed a bilateral patchy ground-glass lung lesion. Patient 2. (d-f), In a 34-year-old man with a cough and fatigue for 3 days, computed tomography images show patchy foci scattered bilaterally in the inferior lobes

Click here to view
Figure 2: Chest computed tomography imaging of two severe type COVID-19 patients. Patient 1. (a-c), In a 63-year-old woman with a cough for 1 month and with fever for 3 days, computed tomography images show bilateral multifocal mixed ground-glass opacity and consolidation lesions distributed by segments and lobules of both lungs. Traction bronchiectasis is also present. Patient 2. (d-f), In a 73-year-old woman with a fever for 4 days and a cough and expectoration for 2 days, computed tomography images show diffuse ground-glass opacity lesions involving the lung center and periphery

Click here to view
Figure 3: Chest computed tomography imaging of three fatal type COVID-19 patients. Patient 1. (a-c), In a 65-year-old woman with a fever and cough for 10 days, computed tomography images show bilateral multifocal mixed ground-glass opacity and consolidation lesions in pulmonary segments and lobules of both lungs, most obvious at the periphery of both lungs. Patient 2. (d-f), In a 65-year-old woman with a fever, cough, and dyspnea for 2 days, computed tomography images show bilateral consolidation distributed in the center of both lungs with traction bronchiectasis. This is the only case in which pleural effusion was visible. Patient 3. (g-i), In a 58-year-old man with an intermittent cough for 4 days, computed tomography images show diffuse solid lesions involving the lung center and periphery (”white lung”). Traction bronchiectasis and vascular enlargement are also present

Click here to view
Table 2: Computed tomography features of patients with different clinical types of COVID-19 (case)

Click here to view


The infection ratio of lesions in chest computed tomography images

The ratio of infectious foci in chest CT images was as follows: The IR of the common type (2.4 [4.3]) was significantly lower than that of severe type (15.7 [14.3]) and fatal type (36.9 [14.2]) COVID-19. This difference was statistically significant (P < 0.000b). The IR of severe type COVID-19 was also significantly lower than that of fatal type (P = 0.012b). There was no statistically significant difference in the IR of the RL and LL among the different COVID-19 type groups (P = 0.579). Similarly, there was no statistically significant difference in the IR of the RIL and LIL (P = 0.783, 0.501 and 0.394, respectively). However, the IR in the inferior lobe of both lungs was statistically different from that of the other lobes in the common and severe type patients (P < 0.05). In the fatal type group, only the IR in the RIL was significantly different from that in the RSL, RML, and LSL (P < 0.05). The IR of the RIL and the LIL was the largest, and the IR of the RIL was larger than that of the LIL. The ratio of infections in the different clinical types (M [Q]) is shown in [Table 3].
Table 3: The ratio of infections in different clinical types [M (Q)]

Click here to view



  Discussion Top


There is no doubt that COVID-19 has attracted the attention of most people worldwide. Confirmed cases are still increasing with an incredible speed worldwide. No fully proven antiviral solution against SARS-CoV-2 exists to date. Early detection, early diagnosis, early isolation, and early treatment remain the most effective basic strategies to fight the COVID-19 outbreak.

In this study, we have summarized the clinical characteristics and CT features of 89 patients with COVID-19. Not surprisingly, all patients had a history of endemic exposure in Wuhan or close contact with a confirmed case. The clinical presentation of each COVID-19 type was different. Fever was more common in severe and fatal type COVID-19 patients than in common type patients. The incidence of pharyngeal pain, fatigue, and chest tightness in fatal type patients was higher than that in severe and common type patients. This indicates that the degree of immune system activation is different in the various clinical types, which indirectly reflects the disease severity. Although the onset of symptoms varied, fever and cough were the most common symptoms, which is consistent with previous studies.[8],[9] This may help us screen suspected cases. Fatal type COVID-19 is more common in older men, with a median age of 65 years, and these patients are more likely to have underlying diseases, such as hypertension, diabetes, coronary heart disease, heart, and liver or kidney dysfunction. Older people with underlying diseases are likely to have compromised immunity. SARS-CoV-2 infection may further affect the immune system, causing diffuse alveolar damage with a large amount of inflammatory exudate,[10] leading to acute respiratory distress syndrome and eventually respiratory failure. It is suggested correlation with viral infection by laboratory tests. The rate of abnormal WBC and neutrophil counts in severe and fatal type COVID-19 patients was higher than that in common type patients, which also reflects the severity of the disease.

Chest CT can detect millimeter lesions in the lungs with high sensitivity and is currently the preferred conventional imaging method for the early detection, diagnosis, and assessment of disease severity in COVID-19 patients.[11],[12] On CT, the common type COVID-19 patients showed bilateral lungs, multiple, patchy, GGO or mixed lesions, distributed in the subpleural regions, which are common findings in COVID-19 pneumonia cases.[13],[14],[15],[16],[17],[18] The main CT characteristics of patients with severe and fatal type COVID-19 were multiple GGO and multifocal consolidations in pulmonary segments and/or lobules of both lungs, involving both the lung center and periphery. The margins of the consolidation lesions were relatively clear. That is because lesions in fatal type patients were often diffuse throughout the whole lobes, and the ground-glass content of the lesions reduced relatively as the amount of inflammatory exudate in the lesions increased, so that the margin of the lesions was relatively clear. This also reflects the course of pneumonia and aggravation of the disease. Only one fatal type case showed pleural effusion, suggesting that pleural effusion may be an indication of severe pneumonia.

With disease progression, the lesions showed a trend to progressively increase. In our study, AI was used to quantitatively calculate the percentage of CT-identified infection lesions in the total lung volume to objectively evaluate the course of the disease. We found that the IR of common type COVID-19 was significantly lower than that of severe or fatal type COVID-19. The IR of severe type COVID-19 was also significantly lower than that of the fatal type. These data suggest a trend of increasing pulmonary infection in common, severe, and fatal type patients. The IV and ratio were highly related to the severity of COVID-19. This finding further confirms data from a previous study.[19] Therefore, CT findings can be used assess disease severity and to provide accurate quantitative assessment indicators for the disease course. The IR of each pulmonary lobe in each group was not equal, but there was no statistically significant difference in the IR of the RL or the LL among the different type groups. There was also no statistically significant difference in the IR of the RIL and LIL. The largest IR was in the RIL and LIL, with the most extensive lesions in the RIL. These data suggest that pulmonary infection is more common in the inferior lung, especially in the RIL. This may be related to the fact that the bronchus in the inferior lobe of the RL is thicker and shorter, and therefore, the virus may infect this location more easily.[20] The IR in the inferior lobe of both lungs was statistically different from that of other lobes in the common and severe type patients. However, in the fatal type group, only the IR in the RIL was statistically different from that in the RSL, RML, and LSL, which also reflects disease progression and aggravation.


  Conclusion Top


In summary, COVID-19 has a variety of clinical manifestations and there are relative differences among clinical subtypes. The chest CT features are characteristic and CT can detect millimeter level ground-glass lesions. Therefore, CT can play an important role in the early diagnosis and in the assessment of the clinical course and severity of COVID-19. AI can quantitatively calculate the volume of CT infection lesions to objectively evaluate the disease severity.

Acknowledgment

We thank Leah Cannon, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn/), for editing the English text of a draft of this manuscript.

Ethic statement

Not applicable.

Financial support and sponsorship

This study was financially supported by Key Emergency Project of Pneumonia Epidemic of novel coronavirus infection. National Natural Science Foundation of China (81671671) and the Key R & D projects in Hunan Province (2019SK2131). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for the publication.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
WHO. Novel Coronavirus-China. Geneva: World Health Organization; 2020.  Back to cited text no. 1
    
2.
Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species Severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536-44.  Back to cited text no. 2
    
3.
WHO. Director-General's Remarks at the Media Briefing on 2019-nCoV on 11 February 2020. Geneva: World Health Organization; 2020.  Back to cited text no. 3
    
4.
Diagnosis and treatment of pneumonitis caused by new coronavirus. Available from: http://www.nhc.gov.cn/xcs/zhengcwj/list_gzbd.shtml. [Last accessed on 2021 Jul 11].  Back to cited text no. 4
    
5.
Xie X, Zhong Z, Zhao W, Wang F, Liu J. Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing. Radiology 2020;296:E41-5.  Back to cited text no. 5
    
6.
Zhao W, Zhong Z, Xie X, Yu Q, Liu J. CT Scans of patients with 2019 novel coronavirus (COVID-19) pneumonia. Theranostics 2020;10:4606-13.  Back to cited text no. 6
    
7.
Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, et al. Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med Phys 2021;48:1633-45.  Back to cited text no. 7
    
8.
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061-9.  Back to cited text no. 8
    
9.
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497-506.  Back to cited text no. 9
    
10.
Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 2020;8:420-2.  Back to cited text no. 10
    
11.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199-207.  Back to cited text no. 11
    
12.
Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet 2020;395:514-23.  Back to cited text no. 12
    
13.
Fang Y, Zhang H, Xu Y, Xie J, Pang P, Ji W. CT manifestations of two cases of 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology 2020;295:208-9.  Back to cited text no. 13
    
14.
Lei J, Li J, Li X, Qi X. CT imaging of the 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology 2020;295:18.  Back to cited text no. 14
    
15.
Kanne JP. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: Key points for the radiologist. Radiology 2020;295:16-7.  Back to cited text no. 15
    
16.
Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study. Lancet Infect Dis 2020;20:425-34.  Back to cited text no. 16
    
17.
Ajlan AM, Ahyad RA, Jamjoom LG, Alharthy A, Madani TA. Middle East respiratory syndrome coronavirus (MERS-CoV) infection: Chest CT findings. AJR Am J Roentgenol 2014;203:782-7.  Back to cited text no. 17
    
18.
Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT Imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 2020;295:202-7.  Back to cited text no. 18
    
19.
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. AJR Am J Roentgenol 2020;214:1072-7.  Back to cited text no. 19
    
20.
Shi H, Han X, Zheng C. Evolution of CT manifestations in a patient recovered from 2019 novel coronavirus (2019-nCoV) pneumonia in Wuhan, China. Radiology 2020;295:20.  Back to cited text no. 20
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed1327    
    Printed106    
    Emailed0    
    PDF Downloaded118    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]