tetano
Editor, Senior Moderator
Emerg Radiol
. 2020 Oct 10.
doi: 10.1007/s10140-020-01856-4. Online ahead of print.
Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT
Chun Ma[SUP] 1 [/SUP], Xiao-Ling Wang[SUP] 1 [/SUP], Dong-Mei Xie[SUP] 1 [/SUP], Yu-Dan Li[SUP] 1 [/SUP], Yong-Ji Zheng[SUP] 1 [/SUP], Hai-Bing Zhang[SUP] 1 [/SUP], Bing Ming[SUP] 2 [/SUP]
Affiliations
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: Artificial intelligence; Coronavirus; Lung; Pneumonia; Tomography; X-ray; viral.
. 2020 Oct 10.
doi: 10.1007/s10140-020-01856-4. Online ahead of print.
Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT
Chun Ma[SUP] 1 [/SUP], Xiao-Ling Wang[SUP] 1 [/SUP], Dong-Mei Xie[SUP] 1 [/SUP], Yu-Dan Li[SUP] 1 [/SUP], Yong-Ji Zheng[SUP] 1 [/SUP], Hai-Bing Zhang[SUP] 1 [/SUP], Bing Ming[SUP] 2 [/SUP]
Affiliations
- PMID: 33037946
- DOI: 10.1007/s10140-020-01856-4
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: Artificial intelligence; Coronavirus; Lung; Pneumonia; Tomography; X-ray; viral.