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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 1  |  Page : 17-24

CT quantitative analysis in patients with severe coronavirus disease 2019 and its correlation with laboratory examination results


1 Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
2 Department of Radiology, Chongqing Three Gorges Central Hospital, Chongqing, China

Date of Submission17-Jun-2020
Date of Acceptance11-Jan-2021
Date of Web Publication18-Nov-2021

Correspondence Address:
Dr. Ting Chen
Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing
China
Dr. Wenbing Zeng
Department of Radiology, Chongqing Three Gorges Central Hospital, Chongqing
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/RID.RID_3_21

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  Abstract 


OBJECTIVE: To quantitatively analyze the longitudinal changes of ground-glass opacity (GGO), consolidation and total lesion in patients infected with severe coronavirus disease 2019 (COVID-19), and its correlation with laboratory examination results.
MATERIALS AND METHODS: All 76 computed tomography (CT) images and laboratory examination results from the admission to discharge of 15 patients confirmed with severe COVID-19 were reviewed, whereas the GGO volume ratio, consolidation volume ratio, and total lesion volume ratio in different stages were analyzed. The correlations of lesions volume ratio and laboratory examination results were investigated.
RESULTS: Four stages were identified based on the degree of lung involvement from day 1 to day 28 after disease onset. GGO was the most common CT manifestation in the four stages. The peak of lung involvement was at around stage 2, and corresponding total lesion volume ratio, GGO volume ratio, and consolidation volume ratio were 17.48 (13.44−24.33), 12.11 (7.34−17.08), and 5.51 (2.58−8.58), respectively. Total lesion volume ratio was positively correlated with neutrophil percentage, C-reactive protein (CRP), high-sensitivity CRP (Hs-CRP), procalcitonin, lactate dehydrogenase (LD), and creatine kinase isoenzyme MB (CK-MB), but negatively correlated with lymphocyte count, lymphocyte percentage, arterial oxygen saturation, and arterial oxygen tension. Consolidation volume ratio was correlated with most above laboratory examination results except Hs-CRP, LD, and CK-MB. GGO, however, was only correlated with lymphocyte count.
CONCLUSION: CT quantitative parameters could show longitudinal changes well. Total lesion volume ratio and consolidation volume ratio are well correlated with laboratory examination results, suggesting that CT quantitative parameters may be an effective tool to reflect the changes in the condition.

Keywords: Automatic segmentation, computed tomography, coronavirus disease 2019, quantitatively analyze, severe


How to cite this article:
Wu L, Yang R, Guo D, Li X, Li C, Zeng W, Chen T. CT quantitative analysis in patients with severe coronavirus disease 2019 and its correlation with laboratory examination results. Radiol Infect Dis 2021;8:17-24

How to cite this URL:
Wu L, Yang R, Guo D, Li X, Li C, Zeng W, Chen T. CT quantitative analysis in patients with severe coronavirus disease 2019 and its correlation with laboratory examination results. Radiol Infect Dis [serial online] 2021 [cited 2021 Dec 8];8:17-24. Available from: http://www.ridiseases.org/text.asp?2021/8/1/17/330564




  Introduction Top


The coronavirus disease 2019 (COVID-19) is a strain of new severe respiratory disease initially discovered in Wuhan, Hubei Province, China, and afterward, it has spread expeditiously throughout the world. The outbreak of the disease has caused 84849 confirmed cases in China, including 4634 fatalities by August 16, 2020.[1],[2],[3] Most COVID-19 patients have a mild disease course, while older men with comorbidities may experience severe or even fatal respiratory diseases.[2] Zhang et al.[4] reported that more abnormal laboratory examination results could be found in patients with severe COVID-19 compared to nonsevere patients, including elevated in leukocyte count, D-dimer, C-reactive protein (CRP), and reduced in lymphocyte percentage. Therefore, further attention should be focused on monitoring the disease progression in severe patients.

Chest computed tomography (CT) is not only of great significance in the diagnosis and evaluation of the therapeutic effect on diseases, but also an essential part of grading, the four stages were identified of COVID-19.[5] Previous studies[6],[7],[8] have concluded ground-glass opacity (GGO) and pulmonary consolidation to be typical CT findings in patients with COVID-19. In previous studies concerning severe acute respiratory syndrome coronavirus (SARS-COV), Middle East respiratory syndrome coronavirus (MERS-COV), and COVID-19, most scholars utilized CT scores to semi-quantitatively evaluate the degree of lung inflammation.[7],[9],[10] Although this method has a certain research value, it is unable to quantitatively evaluate the real volume of GGO and consolidation in the disease progression. With the rapid development of artificial intelligence technology in recent years, an increasing number of studies on automatic segmentation of lung lesions have been carried out.[11],[12],[13],[14] In this study, we used a new automatic segmentation software for lung inflammation which could quantitatively calculate the percentages of total lesion, consolidation, and GGO volume in two lungs, respectively. We named the total lesion, consolidation and GGO volume percentages in both lungs as total lesion volume ratio, consolidation volume ratio, and GGO volume ratio. At the same time, we selected CT data and laboratory examination results with close execution times for each patient to observe whether the changes are relevant.

The purpose of the study is to quantitatively describe the longitudinal changes of GGO volume ratio, consolidation volume ratio and total lesion volume ratio in patients with severe COVID-19 during hospitalization, and investigate its correlation with laboratory examination results.


  Materials and Methods Top


Patients

This retrospective study was approved by the Institutional Ethics Committee of our hospital, and the requirement for informed consent was waived.

From January 15, 2020 to February 26, 2020, severe COVID-19 patients who had been discharged from our hospital were involved in this study. The inclusion criteria were as follows: (1) confirmed as severe COVID-19 based on the severity grading set by the National Health Commission of the People's Republic of China,[5] (2) Wuhan-related exposure history within 14 days before the onset of symptoms, and (3) positive result of reverse-transcription polymerase chain reaction of SARS-CoV-2 in throat swabs or the lower respiratory tract. The exclusion criteria included: (1) patients with incomplete clinical data and (2) patients with other bacterial or coronavirus infections. Finally, a total of 15 patients, including 8 men and 7 women (age range, 31–79 years; median age, 49 years) were included. Two out of the 15 patients were transformed to critical COVID-19 ones during hospitalization (age, 45 and 60 years, respectively).

Clinical and laboratory examination results derived from the detailed medical records primarily included age, gender, initial symptoms, and presence of comorbidities. The results of laboratory examination during hospitalization were collected for further statistical analysis. All laboratory examinations were performed 24 hours before or after the CT scan. Specifically, the results included as follows: leukocyte, neutrophil percentage, lymphocyte count, lymphocyte percentage, lactate dehydrogenase (LD), procalcitonin, creatine kinase isoenzyme MB (CK-MB), CRP, D-dimer, high-sensitivity CRP (hs-CRP), arterial oxygen saturation (SaO2), and arterial oxygen tension (PaO2).

Computed tomography data acquisition

Series CT scans were performed on a 16-slice spiral CT scanner (Somatom Sensation, Siemens, Germany) for all enrolled patients. The patients underwent breath training before examination. The images were acquired by using the following scanning parameters: a tube voltage of 120 kV, smart mA tube current modulation, a slice thickness of 1.5 mm, and a detector width of 1.5 mm. The scanning field covered from the apex to the bottom. The initial and follow-up chest CT images were obtained with the same parameters.

Computed tomography image analysis

All chest CT images were reviewed by two experienced radiologists and a senior radiologist in consensus. In accordance with previous studies[6],[7],[8] reported in COVID-19, the CT features, including GGO, consolidation, mixed GGO, and consolidation were observed. In addition, crazy-paving pattern, spider web sign, and pleural effusion were also documented. The distribution of these CT manifestations was also evaluated and classified as follows: central (mainly two thirds of the inner lungs), peripheral (mainly one third of the outer lungs), and both central and peripheral (continuous involvement, regardless of lung segments).[9]

Computed tomography quantitative analysis

The measure of pneumonia volume ratio was conducted on the FACT Medical Imaging System (Dexin Medical Imaging Technology Co, Ltd.) in which Pulmonary Analysis System (V1.7.0.1) automatically segmented all lesions.[11],[14] Based on automatic segmentation, the precise adjustment of boundaries of each lesion was manually made to avoid the influences on noninflammatory lesions such as large blood vessels, pleural effusion, pleural thickening, or calcification. In addition, we used a semi-automatic segmentation method to further analyze the volume ratio of GGO and consolidation based on following two steps: (1) The system automatically segmented the GGO and consolidation based on different thresholds; (2) radiologists further manually adjusted the segmentation results according to the definition of GGO and consolidation in the Fleischner Society Recommendations.[15],[16] Following completion of all adjustments, the system automatically calculated the volumes ratio of total lesion, GGO, and consolidation.

All of the initial and follow-up CT image lesion volume ratios in patients with severe COVID-19 during hospitalization were used to observe the longitudinal changes and their relationship with the laboratory examination results.

Statistical analysis

SPSS Statistical Package version 20.0 (SPSS Statistical Package version (IBM,Armonk,NY,USA)) was used for the statistical analysis. P < 0.05 was considered to be statistically significant. Quantitative variables were showed as median and interquartile range. Categorical variables were showed as frequency and percentage. The Wilcoxon test was implemented to determine the significant differences in volume ratio between consolidation and GGO. Mann-Whitney U test was performed to determine the significant differences of total lesion volume ratio, consolidation volume ratio, GGO volume ratio, and laboratory examination results in different stage. Because the laboratory examination time of Stage 1 patients is not consistent with the CT scan time, the laboratory examination results of such patients are not included in the statistical analysis.

Spearman's or Pearson correlation analysis was implemented to determine the correlation of total lesion volume ratio, consolidation volume ratio, and GGO volume ratio with laboratory examination results, and the correlation coefficient strength is represented by r.


  Results Top


Clinical and laboratory findings in 15 patients with severe COVID-19

All the patients were discharged after a median hospitalized period of 16 (14 − 19) days. Clinical data on a total of 15 patients on admission are summarized in [Table 1]. The median age of the patients was 49, and 6 of them (40%) were older than 60 years. Eight (53%) patients had chronic diseases. Furthermore, the most commonly apparent symptoms of the patients were fever (12/15, 80%) and cough (11/15, 73.3%).
Table 1: Clinical data of the 15 patients with severe coronavirus disease 2019

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According to the medical records during hospitalization, total 60 leukocyte results, 60 neutrophil percentage, 60 lymphocyte count, 60 lymphocyte percentage, 60 LD, 59 procalcitonin, 49 CK-MB, 23 CRP, 29 D-dimer, 33 hs-CRP, 19 SaO2, and 19 PaO2 results during hospitalization were collected in this study. Most of the laboratory examination results during hospitalization were abnormal, with the most frequent abnormalities significantly elevated in CRP (10.54 [4.59 − 38.3] mg/L), hs-CRP (31.22 [9.44 − 42.285] mg/L), and neutrophil percentage (80.45 (71.65 − 89.08) %). However, there was a significant reduction in the lymphocyte percentage (11.25 [7.6%−19.18] %).

Chest computed tomography findings in 15 patients with severe COVID-19

The earliest pulmonary CT scan was obtained 8 (6 − 10) days (range: 1 − 12 days) after the onset of symptoms. A total of 76 CT scans were performed and each patient underwent a median of 5 (4 − 6) CT scans (range: 3 − 7) with a median interval of 3 (2 − 4) days (range: 2 − 7 days ).

Based on the degree of lung involvement from day 1 to day 28 after disease onset, four stages were identified from the onset of initial symptoms: Stage 1 (0 − 7 days), Stage 2 (8 − 14 days), Stage 3 (15 − 21 days), and Stage 4 (22 − 28 days). 7 of the total 76 pulmonary CT scans were in Stage 1, 32 in Stage 2, 26 in Stage 3, and 11 in Stage 4.

The distribution of lesions and major CT findings in four stages are summarized in [Table 2]. Both central and peripheral lesions (52/76) were more common than subpleural lesions (24/76), and there were no lesions only located in the central lung. The crazy-paving pattern is common in Stage 1 (4/7) and Stage 2 (16/32). The spider-web sign is common in Stage 2 (15/32), Stage 3 (21/26), and Stage 4 (9/11). The pleural effusion is common in stage 2 (10/32).
Table 2: Computed tomography characteristics of lung major lesions in 15 severe patients with coronavirus disease 2019 at different stages

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From the perspective of lesion volume ratio, GGO presented the most common CT manifestation in the four stages, whereas a small part was consolidation. The total lesion volume ratio, GGO volume ratio, and consolidation volume ratio are summarized in [Table 2]. In most patients (10 out of 15), the total lesion volume ratio increased to Stage 2 after the presence of symptoms while gradually decreasing in Stage 3. The total lesion volume ratio peaked on approximately day 9 after the onset of initial symptoms [Figure 1]a. In addition, the consolidation volume ratio increased to Stage 2 after symptoms appeared, gradually decreased in Stage 3, and then slightly increased in Stage 4 [Figure 1]b. However, the GGO volume ratio decreased steadily from Stage 1 to Stage 4 [Figure 1]c.
Figure 1: Longitudinal changes in lesion volume ratio. (a) Longitudinal changes in lesion volume ratio; (b) Longitudinal changes in consolidation volume ratio; (c) Longitudinal changes ground-glass opacity volume ratio on chest computed tomography from the time of onset of initial symptoms (in days). The red lines in 1a-1c, respectively, describe the changes in total lesion volume ratio, consolidation volume ratio, and ground-glass opacity volume ratio in 15 patients over time

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In the course of the longitudinal change curve of total lesion volume ratio for each patient during four stages [Figure 2], three curve forms were found. The first indicated a gradual decrease of the total lesion volume ratio from Stage 1 to Stage 4 (5/15) [Figure 3]; the second illustrated an increase of total lesion volume ratio in Stage 2 after the appearance of symptoms, and then a gradual decrease in Stages 3 and 4 (7/15) was presented [Figure 4]; the third showed an increase of total volume ratio in Stage 2 after the appearance of symptoms followed by a decline during stages 3 and 4. During this process, a slight rise was followed by a continuing decline (3/15).
Figure 2: Longitudinal change curve of total lesion volume ratio of each patient in four stages

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Figure 3: Longitudinal changes of computed tomography appearances in a 68-year-old male with severe coronavirus disease 2019. A small amount of pleural effusion and lymphadenopathy can be observed (a-d). (a) The first computed tomography scan (9 days after initial symptoms) showed multiple subpleural consolidation and ground-glass opacity in both lungs; (b-d) the follow-up computed tomography showed the gradually absorbed pulmonary lesions. (e) The three-dimensional image of peak total computed tomography pulmonary lesion is also shown

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Figure 4: Longitudinal changes of computed tomography appearances in a 44-year-old male with severe coronavirus disease 2019. (a) The first computed tomography scan (5 days after initial symptoms) showed bilateral consolidative opacities in the low lobes; (b) The follow-up computed tomography showed the bilateral ground-glass opacity increased; (c and d) The follow-up computed tomography showed the bilateral ground-glass opacity subsequently decreased. (e) The 3D image of peak total computed tomography pulmonary lesion is also shown

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Statistical differences of lesion volume ratio and laboratory examination results

The Mann–Whitney U test showed significant difference between Stages 1 and 2, and Stages 2 and 3 in consolidation volume ratio, and between Stages 2 and 3 in total volume ratio. The Wilcoxon test showed significant difference between the GGO volume ratio and consolidation volume ratio in Stages 1 to 4 (P < 0.05) [Table 3]. In addition, the Mann–Whitney U test also showed significant difference between Stages 2 and 3 in CRP, Hs-CRP, and LD, respectively; between Stages 2 and 4 in neutrophil percentage, lymphocyte percentage, CRP, CK-MB, and LD, respectively, and between Stages 3 and 4 in neutrophil percentage and CK-MB (all P < 0.05).
Table 3: Summary of laboratory examination results in three stages

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Correlation analysis between laboratory examination results and lesion volume ratio

According to the correlation analysis, total lesion volume ratio was positively correlated with neutrophil percentage, CRP, hs-CRP, procalcitonin, LD and CK-MB, respectively; rather negatively correlated with lymphocyte count, lymphocyte percentage, SaO2, and PaO2, respectively. Further, the consolidation volume ratio had a positive correlation with neutrophil percentage, CRP, and procalcitonin, respectively, but a negative one with lymphocyte count, lymphocyte percentage, SaO2 and PaO2, respectively. Furthermore, a negative correlation between GGO volume ratio and lymphocyte count was found [Table 4].
Table 4: Correlation between the laboratory examination results and volume ratio in 15 patients with coronavirus disease 2019

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  Discussion Top


According to the protocols of COVID-19 (Trial Version 6) proposed by the National Health Commission of the People's Republic of China,[5] chest CT imaging shows apparent absorption of acute exudative lesions can be referred as one of the discharge criteria, and the assessment of disease progression and outcome is feasible to be conducted by follow-up imaging of COVID-19 patients. However, the dynamic image characteristics of severe patients with COVID-19 remain uncertain. Previous reports have demonstrated a consistent imaging performance with the severity of the COVID-19 patients.[8] Therefore, the dynamic observation and follow-up imaging of severe COVID-19 patients are of great importance in the prevention of disease deterioration and reduction of mortality.

Diagnosis and treatment protocols[5] summarized a conclusion that severe patients developed dyspnea and hypoxemia one week after onset, and critical patients were immediately deteriorated into acute respiratory distress syndrome, metabolic acidosis, and coagulopathy. In our study, 15 severe patients underwent the first CT scan within a median 8 day after the onset of symptoms. Scattered lesions were found in the lungs of each patient, and the total lesion volume ratio peaked on approximately the 9th day after the onset of initial symptoms. The results, therefore, were consistent with the progression of severe patients as summarized in the diagnosis and treatment protocols.

Based on a dynamic study of 76 scans of chest CT divided into four stages, both lung lesions were distributed in the whole lobe or multiple lobes. Recent autopsy reports have shown a coincidence between the distribution of lung lesions in patients with COVID-19 death and chest CT imaging.[17] In CT imaging, the characteristics of severe patients were diffuse GGO, scattered subpleural consolidation, with crazy-paving pattern and pleural effusion. In our study, the proportion of lesions in the four stages was primarily GGO shadows, particularly in Stage 1 to Stage 3. The GGO seen on imaging corresponded to the gray-white alveolar lesions on the naked eye, suggesting that COVID-19 mainly caused deep airway and alveolar damage characterized by an inflammatory response.[17] It was reported that the pathological characteristics of COVID-19 presented close similarities to those caused by SARS and MERS-COV.[17] However, from the general anatomical observation, pulmonary fibrosis and consolidation of COVID-19 were as serious as those caused by SARS, but the exudative response was more obvious than SARS.[17] Consequently, the observation was consistent with our dynamic observation of chest CT changes in severe patients. In addition, observation of the dynamic curve of the consolidation volume ratio found a slight increase in Stage 4 which was presumably related to the histological changes in the end stage of COVID-19 pneumonia. Report showed that diffuse lung alveolar injury with fibrous mucus-like exudates was caused by COVID-19 end-stage pneumonia,[17] which transformed GGO lesions into consolidation lesions and formed a contracted band shadow. Therefore, as in the Stage 4 of the absorption course of the disease, the total volume ratio of the lesion decreased while the proportion of consolidation increased slightly. The pleural effusion was found in severe patients, which mainly occurred in Stage 2 of the disease course, but with only a small amount in most cases. In this study, the existence of pleural effusion in patients may be somewhat associated with their chronic underlying disease.

From the dynamic change curve of total lesion volume ratio of each patient in four stages, the curves showed in the form of three types, the apparent discrepancy in terms of shape of the curve in Stages 1 and 2 was possibly relevant to the time difference of CT examination in the early stage of the patient's symptoms. Moreover, in Stages 3 and 4, as the lesions were in the absorption phase, the total lesion volume of the three severe patients increased slightly, which related to the characteristics of the development of the lesions in severe patients. In these cases, absorption of some lesions improved and new lesions appeared simultaneously, probably due to the delayed inflammation response in some lung regions.

As shown in [Table 4], our study indicated a negative correlation between the volume ratio of total lesion, of consolidation, and of the GGO with the lymphocyte count and lymphocyte percentage, which suggested a possible association between COVID-19 and cellular immune deficiency. Previous studies[18],[19],[20],[21] have shown that SARS-COV and MERS-COV mainly infected T-lymphocytes in human peripheral blood and lymphoid organs, causing a decrease in the number of T-lymphocytes, especially in patients in critical or fatal condition. The anatomical and pathological findings of patients with COVID-19 indicated a substantial reduction in counts of peripheral CD4 and CD8 T-cells while their status was hyperactivated.[17] In addition, in our study, neutrophil percentage, CRP, hs-CRP, procalcitonin, LD, and CK-MB levels were positively correlated with the total lesion volume ratio, but partially correlated with consolidation volume ratio. On the whole, our study results are similar to previous research: the increase of CRP and neutrophil percentage may represent more prominent inflammation in severe or critical patients and the increase of procalcitonin may result from secondary bacterial infection.[4],[22] The change of LD and CK-MB was probably in connection with direct effects of virus or hypoxia and may reflect damage in both myocardial and liver.[4],[22] Furthermore, the finding that SaO2 and PaO2 are negatively correlated with the total lesion volume ratio and consolidation volume ratio, confirmed worse lung function being due to the increase of lung inflammation. Therefore, the result shows that the chest CT performance of the COVID-19 patients is consistent with the laboratory test results in assessing the development of the disease course, and chest CT can be served as an effective tool for dynamically monitoring the progression of the disease.[23]

However, there were some limitations in our study. First, the sample size is too small, and it may not be appropriate to divide patients into four stages based on the time from onset to discharge; this requires more cases for further study second, to date, no specific treatment for coronavirus infection has been recommended, which may cause the repeated condition of patient and affect the process of observations for the dynamic changes in CT. Third, in our research, comorbidities are common. It is unknown whether there is a correlation between its existence and laboratory test results, which requires further expansion of the sample size to observe. Finally, no lung biopsy specimens were available to confirm the relationship between dynamic changes in radiology and histopathological changes in real cases.


  Conclusion Top


Our study finds that CT quantitative parameters could show longitudinal changes well. Total lesion volume ratio and consolidation volume ratio are well correlated with laboratory examination results, suggesting that CT quantitative parameters may be an effective tool for monitoring progression of the disease.

Ethic statement

This retrospective study was approved by the Institutional Ethics Committee of our hospital, and the requirement for informed consent was waived.

Acknowledgment

We express our deepest sympathy to all COVID-19 patients and their families. We also express our heartfelt appreciation all medical staff who are fighting against COVID-19.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patients have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

This work is funded by the Novel Coronavirus Pneumonia Emergency Clinical Research Project of Chongqing Medical University (30).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
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. 1
    
2.
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507-13.  Back to cited text no. 2
    
3.
National Health Commission of the People's Republic of China. Update on the Novel Coronavirus Pneumonia Outbreak (August 16, 2020). Beijing: National Health Commission of the People's Republic of China. 8.17; 2020.  Back to cited text no. 3
    
4.
Zhang JJ, Dong X, Cao YY, Yuan YD, Yang YB, Yan YQ, et al. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 2020;75:1730-41.  Back to cited text no. 4
    
5.
National Health Commission of the People's Republic of China. Diagnosis and Treatment Protocols of Pneumonia Caused by a Novel Coronavirus (Trial Version 6), 2.18; 2020.  Back to cited text no. 5
    
6.
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.  Back to cited text no. 6
    
7.
Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes at chest ct during recovery from coronavirus disease 2019 (COVID-19). Radiology 2020;295:715-21.  Back to cited text no. 7
    
8.
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. 8
    
9.
Ooi GC, Khong PL, Müller NL, Yiu WC, Zhou LJ, Ho JC, et al. Severe acute respiratory syndrome: Temporal lung changes at thin-section CT in 30 patients. Radiology 2004;230:836-44.  Back to cited text no. 9
    
10.
Das KM, Lee EY, Enani MA, AlJawder SE, Singh R, Bashir S, et al. CT correlation with outcomes in 15 patients with acute middle east respiratory syndrome coronavirus. AJR Am J Roentgenol 2015;204:736-42.  Back to cited text no. 10
    
11.
Pu J, Paik DS, Meng X, Roos JE, Rubin GD. Shape “break-and-repair” strategy and its application to automated medical image segmentation. IEEE Trans Vis Comput Graph 2011;17:115-24.  Back to cited text no. 11
    
12.
Ren H, Zhou L, Liu G, Peng X, Shi W, Xu H, et al. An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing. Quant Imaging Med Surg 2020;10:233-42.  Back to cited text no. 12
    
13.
Yu N, Wei X, Li Y, Deng L, Jin CW, Guo Y. Computed tomography quantification of pulmonary vessels in chronic obstructive pulmonary disease as identified by 3D automated approach. Medicine (Baltimore) 2016;95:e5095.  Back to cited text no. 13
    
14.
Pu J, Zheng B, Leader JK, Wang XH, Gur D. An automated CT based lung nodule detection scheme using geometric analysis of signed distance field. Med Phys 2008;35:3453-61.  Back to cited text no. 14
    
15.
Wormanns D, Hamer OW. Glossary of terms for thoracic imaging--german version of the fleischner society recommendations. Rofo 2015;187:638-61.  Back to cited text no. 15
    
16.
Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner society: Glossary of terms for thoracic imaging. Radiology 2008;246:697-722.  Back to cited text no. 16
    
17.
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. 17
    
18.
Kohyama S, Ohno S, Suda T, Taneichi M, Yokoyama S, Mori M, et al. Efficient induction of cytotoxic T lymphocytes specific for severe acute respiratory syndrome (SARS)-associated coronavirus by immunization with surface-linked liposomal peptides derived from a non-structural polyprotein 1a. Antiviral Res 2009;84:168-77.  Back to cited text no. 18
    
19.
Ohno S, Kohyama S, Taneichi M, Moriya O, Hayashi H, Oda H, et al. Synthetic peptides coupled to the surface of liposomes effectively induce SARS coronavirus-specific cytotoxic T lymphocytes and viral clearance in HLA-A*0201 transgenic mice. Vaccine 2009;27:3912-20.  Back to cited text no. 19
    
20.
He Z, Zhao C, Dong Q, Zhuang H, Song S, Peng G, et al. Effects of severe acute respiratory syndrome (SARS) coronavirus infection on peripheral blood lymphocytes and their subsets. Int J Infect Dis 2005;9:323-30.  Back to cited text no. 20
    
21.
Chu H, Zhou J, Wong BH, Li C, Chan JF, Cheng ZS, et al. Middle East Respiratory syndrome coronavirus efficiently infects human primary T lymphocytes and activates the extrinsic and intrinsic apoptosis pathways. J Infect Dis 2016;213:904-14.  Back to cited text no. 21
    
22.
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. 22
    
23.
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. 23
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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