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European Radiology
Published in: European Radiology 5/2021

Open Access 01-05-2021 | Coronavirus | Imaging Informatics and Artificial Intelligence

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia

Authors: Davide Ippolito, Maria Ragusi, Davide Gandola, Cesare Maino, Anna Pecorelli, Simone Terrani, Marta Peroni, Teresa Giandola, Marco Porta, Cammillo Talei Franzesi, Sandro Sironi

Published in: European Radiology | Issue 5/2021

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Abstract

Objectives

To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes.

Methods

All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated.

Results

A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3− (r = 0.284), and PaO2/FiO2 (P/F) values (r = − 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = −0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3− (r = 0.394), and P/F (r = − 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05).

Conclusion

The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation.

Key Points

• Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia.
• All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count.
• All lung volumes correlate with patient’s outcome, in particular concerning invasive ventilation.
Literature
1.
Xu X, Yu C, Qu J et al (2020) Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging 47(5):1275–1280
2.
3.
Chan JF-W, Yip CC-Y, To KK-W et al (2020) Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp/Hel real-time reverse transcription-PCR assay validated in vitro and with clinical specimens. J Clin Microbiol 58(5):e00310-20
4.
Li B, Li X, Wang Y et al (2020) Diagnostic value and key features of computed tomography in coronavirus disease 2019. Emerg Microbes Infect 9(1):787–793
5.
Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS (2020) Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med 46(5):837–840CrossRef
6.
Zach JA, Newell JD, Schroeder J et al (2012) Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults. Invest Radiol 47(10):596–602
7.
Newell JD, Sieren J, Hoffman EA (2013) Development of quantitative computed tomography lung protocols. J Thorac Imaging 28(5):266–271CrossRef
8.
Washko G (2010) Diagnostic Imaging in COPD. Semin Respir Crit Care Med 31(03):276–285CrossRef
9.
Silva M, Milanese G, Seletti V, Ariani A, Sverzellati N (2018) Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications. Br J Radiol 91(1083):20170644CrossRef
10.
Mascalchi M, Camiciottoli G, Diciotti S (2017) Lung densitometry: why, how and when. J Thorac Dis 9(9):3319–3345CrossRef
11.
den Harder AM, de Boer E, Lagerweij SJ et al (2018) Emphysema quantification using chest CT: influence of radiation dose reduction and reconstruction technique. Eur Radiol Exp 2(1):30
12.
de Boer E, Nijholt IM, Jansen S et al (2019) Optimization of pulmonary emphysema quantification on CT scans of COPD patients using hybrid iterative and post-processing techniques: correlation with pulmonary function tests. Insights Imaging 10(1):102. https://​doi.​org/​10.​1186/​s13244-019-0776-9
13.
Fischer AM, Varga-Szemes A, Martin SS et al (2020) Artificial intelligence-based fully automated per lobe segmentation and emphysema-quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers. J Thorac Imaging 35:S28–S34
14.
15.
16.
17.
18.
Huang G, Gong T, Wang G et al (2020) Timely diagnosis and treatment shortens the time to resolution of coronavirus disease (COVID-19) pneumonia and lowers the highest and last CT scores from sequential chest CT. AJR Am J Roentgenol 30:1–7
19.
20.
Sun Z, Zhang N, Li Y, Xu X (2020) A systematic review of chest imaging findings in COVID-19. Quant Imaging Med Surg 10(5):1058–1079CrossRef
21.
Belfiore MP, Urraro F, Grassi R et al (2020) Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med 125(5):500–504
22.
Colombi D, Bodini FC, Petrini M et al (2020) Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology. 17:201433
23.
Singhal T (2020) A review of coronavirus disease-2019 (COVID-19). Indian J Pediatr 87(4):281–286CrossRef
24.
Cheng Z, Lu Y, Cao Q et al (2020) Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China. AJR Am J Roentgenol 14:1–6
25.
Zhou S, Wang Y, Zhu T, Xia L (2020) CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China. AJR Am J Roentgenol 214(6):1287–1294CrossRef
26.
Metadata
Title
Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia
Authors
Davide Ippolito
Maria Ragusi
Davide Gandola
Cesare Maino
Anna Pecorelli
Simone Terrani
Marta Peroni
Teresa Giandola
Marco Porta
Cammillo Talei Franzesi
Sandro Sironi
Publication date
01-05-2021
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 5/2021
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-07271-0

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