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Emergency Radiology

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01-12-2020 | Coronavirus | Original Article

Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT

Authors: Chun Ma, Xiao-Ling Wang, Dong-Mei Xie, Yu-Dan Li, Yong-Ji Zheng, Hai-Bing Zhang, Bing Ming

Published in: Emergency Radiology | Issue 6/2020

Abstract

Purpose

To identify and quantify lung changes associated with coronavirus disease-2019 (COVID-19) with quantitative lung CT during the disease.

Methods

This retrospective study reviewed COVID-19 patients who underwent multiple chest CT scans during their disease course. Quantitative lung CT was used to determine the nature and volume of lung involvement. A semi-quantitative scoring system was also used to evaluate lung lesions.

Results

This study included eighteen cases (4 cases in mild type, 10 cases in moderate type, 4 cases in severe type, and without critical type cases) with confirmed COVID-19. Patients had a mean hospitalized period of 24.1 ± 7.1 days (range: 14–38 days) and underwent an average CT scans of 3.9 ± 1.6 (range: 2–8). The total volumes of lung abnormalities reached a peak of 8.8 ± 4.1 days (range: 2–14 days). The ground-glass opacity (GGO) volume percentage was higher than the consolidative opacity (CO) volume percentage on the first CT examination (Z = 2.229, P = 0.026), and there was no significant difference between the GGO volume percentage and that of CO at the peak stage (Z = - 0.628, P = 0.53). The volume percentage of lung involvement identified by AI demonstrated a strong correlation with the total CT scores at each stage (r = 0.873, P = 0.0001).

Conclusions

Quantitative lung CT can automatically identify the nature of lung involvement and quantify the dynamic changes of lung lesions on CT during COVID-19. For patients who recovered from COVID-19, GGO was the predominant imaging feature on the initial CT scan, while GGO and CO were the main appearances at peak stage.

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Title: Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT
Authors: Chun Ma
Xiao-Ling Wang
Dong-Mei Xie
Yu-Dan Li
Yong-Ji Zheng
Hai-Bing Zhang
Bing Ming
Publication date: 01-12-2020
Publisher: Springer International Publishing
Keywords: Coronavirus
Artificial Intelligence
Pneumonia
COVID-19
Published in: Emergency Radiology / Issue 6/2020
Print ISSN: 1070-3004
Electronic ISSN: 1438-1435
DOI: https://doi.org/10.1007/s10140-020-01856-4

At a glance: The STEP trials

Springer Medicine

Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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