Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study

Chao Wang; Peiyu Huang; Lihua Wang; Zhujing Shen; Bin Lin; Qiyuan Wang; Tongtong Zhao; Hanpeng Zheng; Wenbin Ji; Yuantong Gao; Junli Xia; Jianmin Cheng; Jianbing Ma; Jun Liu; Yongqiang Liu; Miaoguang Su; Guixiang Ruan; Jiner Shu; Dawei Ren; Zhenhua Zhao; Weigen Yao; Yunjun Yang; Bo Liu; Minming Zhang

doi:10.21037/atm-20-4004

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study

Ann Transl Med. 2020 Aug;8(15):935. doi: 10.21037/atm-20-4004.

Authors

Chao Wang¹, Peiyu Huang¹, Lihua Wang¹, Zhujing Shen¹, Bin Lin¹, Qiyuan Wang¹, Tongtong Zhao², Hanpeng Zheng³, Wenbin Ji⁴, Yuantong Gao⁵, Junli Xia⁶, Jianmin Cheng⁷, Jianbing Ma⁸, Jun Liu⁹, Yongqiang Liu¹⁰, Miaoguang Su¹¹, Guixiang Ruan¹², Jiner Shu¹³, Dawei Ren¹⁴, Zhenhua Zhao¹⁵, Weigen Yao¹⁶, Yunjun Yang¹⁷, Bo Liu^{18

19}, Minming Zhang¹

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² Department of Radiology, The Second People's Hospital of Fuyang, Fuyang, China.
³ Department of Radiology, Yueqing People's Hospital, Yueqing, Wenzhou, China.
⁴ Department of Radiology, Taizhou Hospital of Zhejiang Province, Taizhou, China.
⁵ Department of Radiology, Ruian People's Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, China.
⁶ Bozhou Bone Trauma Hospital Image Center, Bozhou, China.
⁷ Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
⁸ Department of Radiology, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, China.
⁹ Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China.
¹⁰ Department of Radiology, Kecheng People's Hospital, Quzhou, China.
¹¹ Department of Radiology, The People's Hospital of Pingyang, Pingyang Hospital Affiliated to Wenzhou Medical University, Pingyang, China.
¹² Department of Radiology, The First People's Hospital of Yuhang District, Hangzhou, China.
¹³ Department of Radiology, Jinhua Hospital of Zhejiang University, Jinhua Municipal Central Hospital, Jinhua, China.
¹⁴ Department of Radiology, Ningbo First Hospital, Ningbo, China.
¹⁵ Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.
¹⁶ Department of Radiology, Yuyao People's Hospital, The Affiliated Yangming Hospital of Ningbo University, Ningbo, China.
¹⁷ Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
¹⁸ Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China.
¹⁹ YITU AI Research Institute for Healthcare, Shanghai, China.

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.