• Yi Wang, Yuntian Chen, Yi Wei, Man Li, Yuwei Zhang, Na Zhang, Shuang Zhao, Hanjiang Zeng, Wen Deng, Zixing Huang, Zheng Ye, Shang Wan, and Bin Song.
• Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
• Ann Transl Med. 2020 May 1; 8 (9): 594.

BackgroundThe coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven.MethodsAn automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence.ResultsThe VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from -549 to -450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from -149 to -50 HU was related to a lowered risk of ARDS.ConclusionsThe automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.2020 Annals of Translational Medicine. All rights reserved.

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