Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT
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ORIGINAL ARTICLE
Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT Chun Ma 1
&
Xiao-Ling Wang 1 & Dong-Mei Xie 1 & Yu-Dan Li 1 & Yong-Ji Zheng 1 & Hai-Bing Zhang 1 & Bing Ming 1
Received: 28 June 2020 / Accepted: 24 September 2020 # American Society of Emergency Radiology 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. Keywords Coronavirus . Artificial intelligence . Tomography . X-ray . Lung . Pneumonia . viral
Introduction A new coronavirus was first detected in Wuhan, Hubei Province, China, in early December 2019, and then termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The disease has affected the whole world, and it has been named coronavirus disease-2019 (COVID-19). The WHO declared the COVID-19 constituted a public health emergency of international concern on January 30, 2020. As of June 28, Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10140-020-01856-4) contains supplementary material, which is available to authorized users. * Bing Ming [email protected] 1
Department of Radiology, People’s Hospital of Deyang City, Taishanbei 173, Jinyang District, Deyang 618000, China
2020, confirmed cases with COVID-19 worldwide had surpassed 9,000,000, while the death toll has topped 495,000. Similar to other coronavirus pneumonia, COVID-19 mainly causes alveolar edema with hemorrhage, bronchiolitis, alveolitis, and pulmonar
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