tetano
Editor, Senior Moderator
Ann Transl Med
. 2020 Aug;8(15):935.
doi: 10.21037/atm-20-4004.
Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study
Chao Wang[SUP] 1 [/SUP], Peiyu Huang[SUP] 1 [/SUP], Lihua Wang[SUP] 1 [/SUP], Zhujing Shen[SUP] 1 [/SUP], Bin Lin[SUP] 1 [/SUP], Qiyuan Wang[SUP] 1 [/SUP], Tongtong Zhao[SUP] 2 [/SUP], Hanpeng Zheng[SUP] 3 [/SUP], Wenbin Ji[SUP] 4 [/SUP], Yuantong Gao[SUP] 5 [/SUP], Junli Xia[SUP] 6 [/SUP], Jianmin Cheng[SUP] 7 [/SUP], Jianbing Ma[SUP] 8 [/SUP], Jun Liu[SUP] 9 [/SUP], Yongqiang Liu[SUP] 10 [/SUP], Miaoguang Su[SUP] 11 [/SUP], Guixiang Ruan[SUP] 12 [/SUP], Jiner Shu[SUP] 13 [/SUP], Dawei Ren[SUP] 14 [/SUP], Zhenhua Zhao[SUP] 15 [/SUP], Weigen Yao[SUP] 16 [/SUP], Yunjun Yang[SUP] 17 [/SUP], Bo Liu[SUP] 18 19 [/SUP], Minming Zhang[SUP] 1 [/SUP]
Affiliations
Abstract
Background: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia.
Methods: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement.
Results: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female.
Conclusions: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.
Keywords: Coronavirus disease 2019 (COVID-19); artificial intelligence (AI); chest CT; temporal changes.
. 2020 Aug;8(15):935.
doi: 10.21037/atm-20-4004.
Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study
Chao Wang[SUP] 1 [/SUP], Peiyu Huang[SUP] 1 [/SUP], Lihua Wang[SUP] 1 [/SUP], Zhujing Shen[SUP] 1 [/SUP], Bin Lin[SUP] 1 [/SUP], Qiyuan Wang[SUP] 1 [/SUP], Tongtong Zhao[SUP] 2 [/SUP], Hanpeng Zheng[SUP] 3 [/SUP], Wenbin Ji[SUP] 4 [/SUP], Yuantong Gao[SUP] 5 [/SUP], Junli Xia[SUP] 6 [/SUP], Jianmin Cheng[SUP] 7 [/SUP], Jianbing Ma[SUP] 8 [/SUP], Jun Liu[SUP] 9 [/SUP], Yongqiang Liu[SUP] 10 [/SUP], Miaoguang Su[SUP] 11 [/SUP], Guixiang Ruan[SUP] 12 [/SUP], Jiner Shu[SUP] 13 [/SUP], Dawei Ren[SUP] 14 [/SUP], Zhenhua Zhao[SUP] 15 [/SUP], Weigen Yao[SUP] 16 [/SUP], Yunjun Yang[SUP] 17 [/SUP], Bo Liu[SUP] 18 19 [/SUP], Minming Zhang[SUP] 1 [/SUP]
Affiliations
- PMID: 32953735
- PMCID: PMC7475384
- DOI: 10.21037/atm-20-4004
Abstract
Background: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia.
Methods: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement.
Results: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female.
Conclusions: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.
Keywords: Coronavirus disease 2019 (COVID-19); artificial intelligence (AI); chest CT; temporal changes.