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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Visual and Quantitative Assessment of COVID-19 Pneumonia on Chest CT: The Relationship with Disease Severity and Clinical Findings

Author(s): Furkan Kaya*, Petek Şarlak Konya, Emin Demirel, Neşe Demirtürk, Semiha Orhan and Furkan Ufuk

Volume 17, Issue 9, 2021

Published on: 15 February, 2021

Page: [1142 - 1150] Pages: 9

DOI: 10.2174/1573405617666210215142528

Abstract

Background: Lungs are the primary organ involved in COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality.

Aim: We aimed to evaluate the pneumonia severity through the visual and quantitative assessment on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings.

Methods: We retrospectively evaluated adult COVID-19 patients who underwent chest CT along with theirclinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and the length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between the variables. The Pearson correlation test analyzed the correlation between the variables. The diagnostic performance of the variables was assessed using the receiver operating characteristic (ROC) analysis.

Results: The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with the disease severity (p < 0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r = 730, p < 0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization.

Conclusion: The VSQS and QCT scores can help manage the COVID-19 and predict the duration of the hospitalization.

Keywords: COVID-19, pneumonia, quantitative CT, visual CT, disease severity score, CURB-65.

Graphical Abstract
[1]
Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395(10223): 497-506.
[http://dx.doi.org/10.1016/S0140-6736(20)30183-5] [PMID: 31986264]
[2]
Zhu N, Zhang D, Wang W, et al. China novel coronavirus investigating and research team. A novel coronavirus from patients with pneumonia in china, 2019. N Engl J Med 2020; 382(8): 727-33.
[http://dx.doi.org/10.1056/NEJMoa2001017] [PMID: 31978945]
[3]
Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J. Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: Relationship to negative RT-PCR testing. Radiology 2020; 296(2): E41-5.
[http://dx.doi.org/10.1148/radiol.2020200343] [PMID: 32049601]
[4]
Chung M, Bernheim A, Mei X, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 2020; 295(1): 202-7.
[http://dx.doi.org/10.1148/radiol.2020200230] [PMID: 32017661]
[5]
Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis 2020; 20(4): 425-34.
[http://dx.doi.org/10.1016/S1473-3099(20)30086-4] [PMID: 32105637]
[6]
Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 2020; 323(11): 1061-9.
[http://dx.doi.org/10.1001/jama.2020.1585] [PMID: 32031570]
[7]
Xu Z, Shi L, Wang Y, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med 2020; 8(4): 420-2.
[http://dx.doi.org/10.1016/S2213-2600(20)30076-X] [PMID: 32085846]
[8]
Song F, Shi N, Shan F, et al. Emerging 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology 2020; 295(1): 210-7.
[http://dx.doi.org/10.1148/radiol.2020200274] [PMID: 32027573]
[9]
Ufuk F. Three-dimensional CT of COVID-19 Pneumonia. Radiology 2020; 296(3): E180.
[http://dx.doi.org/10.1148/radiol.2020201183] [PMID: 32228362]
[10]
Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol 2020; 30(8): 4407-16.
[http://dx.doi.org/10.1007/s00330-020-06817-6] [PMID: 32215691]
[11]
Colombi D, Bodini FC, Petrini M, et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology 2020; 296(2): E86-96.
[http://dx.doi.org/10.1148/radiol.2020201433] [PMID: 32301647]
[12]
Huang L, Han R, Ai T, et al. Serial quantitative chest ct assessment of covid-19: Deep-learning approach. Radiol Cardiothorac Imaging 2020; 2(2): e200075.
[http://dx.doi.org/10.1148/ryct.2020200075]
[13]
Chen A, Karwoski RA, Gierada DS, Bartholmai BJ, Koo CW. Quantitative CT analysis of diffuse lung disease. Radiographics 2020; 40(1): 28-43.
[http://dx.doi.org/10.1148/rg.2020190099] [PMID: 31782933]
[14]
Lim WS, van der Eerden MM, Laing R, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 2003; 58(5): 377-82.
[http://dx.doi.org/10.1136/thorax.58.5.377] [PMID: 12728155]
[15]
Park B, Park J, Lim JK, et al. Prognostic implication of volumetric quantitative CT analysis in patients with COVID-19: A multicenter study in daegu, korea. Korean J Radiol 2020; 21(11): 1256-64.
[http://dx.doi.org/10.3348/kjr.2020.0567] [PMID: 32767868]
[16]
National health commission & state administration of traditional chinese medicine on diagnosis and treatment protocol for novelCoronavirus pneumonia(Trial Version 7). 2020.; http://en.nhc.gov.cn/2020-03/29/c_78469.htmJun 23, 2020.
[17]
Sun D, Li X, Guo D, et al. CT quantitative analysis and Its relationship with clinical features for assessing the severity of patients with COVID-19. Korean J Radiol 2020; 21(7): 859-68.
[http://dx.doi.org/10.3348/kjr.2020.0293] [PMID: 32524786]
[18]
Ohkubo H, Kanemitsu Y, Uemura T, et al. Correction: normal lung quantification in usual interstitial pneumonia pattern: The impact of threshold-based volumetric CT analysis for the staging of idiopathic pulmonary fibrosis. PLoS One 2016; 11(8): e0160231.
[http://dx.doi.org/10.1371/journal.pone.0160231] [PMID: 27490799]
[19]
Durhan G, Ardalı Düzgün S, Başaran Demirkazık F, et al. Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings. Diagn Interv Radiol 2020; 26(6): 557-64.
[http://dx.doi.org/10.5152/dir.2020.20407] [PMID: 32876569]
[20]
Yuan M, Yin W, Tao Z, Tan W, Hu Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One 2020; 15(3): e0230548.
[http://dx.doi.org/10.1371/journal.pone.0230548] [PMID: 32191764]
[21]
Ufuk F, Demirci M, Uğurlu E, Çetin N, Yiğit N, Sarı T. Evaluation of disease severity with quantitative chest CT in COVID-19 patients. Diagn Interv Radiol 2021; 27(2): 164-71.
[http://dx.doi.org/10.5152/dir.2020.20281] [PMID: 33044173]
[22]
Guan WJ, Ni ZY, Hu Y, et al. China medical treatment expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in china. N Engl J Med 2020; 382(18): 1708-20.
[http://dx.doi.org/10.1056/NEJMoa2002032] [PMID: 32109013]
[23]
Li X, Xu S, Yu M, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 2020; 146(1): 110-8.
[http://dx.doi.org/10.1016/j.jaci.2020.04.006] [PMID: 32294485]
[24]
Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care 2020; 24(1): 108.
[http://dx.doi.org/10.1186/s13054-020-2833-7] [PMID: 32188484]
[25]
Chen G, Wu D, Guo W, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest 2020; 130(5): 2620-9.
[http://dx.doi.org/10.1172/JCI137244] [PMID: 32217835]
[26]
Liu Z, Jin C, Wu CC, et al. Association between initial chest CT or clinical features and clinical course in patients with coronavirus disease 2019 pneumonia. Korean J Radiol 2020; 21(6): 736-45.
[http://dx.doi.org/10.3348/kjr.2020.0171] [PMID: 32410412]
[27]
Leonard-Lorant I, Severac F, Bilbault P, et al. Normal chest CT in 1091 symptomatic patients with confirmed Covid-19: frequency, characteristics and outcome. Eur Radiol 2021; 31(7): 5172-7.
[PMID: 33439316]
[28]
Ufuk F, Demirci M, Sagtas E, Akbudak IH, Ugurlu E, Sari T. The prognostic value of pneumonia severity score and pectoralis muscle Area on chest CT in adult COVID-19 patients. Eur J Radiol 2020; 131: 109271.
[http://dx.doi.org/10.1016/j.ejrad.2020.109271] [PMID: 32942198]

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