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Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China
OBJECTIVES: To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. METHODS: This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All pa...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283983/ https://www.ncbi.nlm.nih.gov/pubmed/32524223 http://dx.doi.org/10.1007/s00330-020-06976-6 |
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author | Wang, Yuan-Cheng Luo, Huanyuan Liu, Songqiao Huang, Shan Zhou, Zhen Yu, Qian Zhang, Shijun Zhao, Zhen Yu, Yizhou Yang, Yi Wang, Duolao Ju, Shenghong |
author_facet | Wang, Yuan-Cheng Luo, Huanyuan Liu, Songqiao Huang, Shan Zhou, Zhen Yu, Qian Zhang, Shijun Zhao, Zhen Yu, Yizhou Yang, Yi Wang, Duolao Ju, Shenghong |
author_sort | Wang, Yuan-Cheng |
collection | PubMed |
description | OBJECTIVES: To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. METHODS: This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups. RESULTS: A total of 484 patients (median age of 47 years, interquartile range 33–57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13–15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients. CONCLUSIONS: Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient’s condition during the course of illness. KEY POINTS: • Volume, density, and location of the pulmonary opacity on CT change over time in COVID-19. • The evolution of CT appearance follows specific pattern, varying with disease severity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06976-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7283983 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-72839832020-06-10 Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China Wang, Yuan-Cheng Luo, Huanyuan Liu, Songqiao Huang, Shan Zhou, Zhen Yu, Qian Zhang, Shijun Zhao, Zhen Yu, Yizhou Yang, Yi Wang, Duolao Ju, Shenghong Eur Radiol Chest OBJECTIVES: To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. METHODS: This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups. RESULTS: A total of 484 patients (median age of 47 years, interquartile range 33–57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13–15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients. CONCLUSIONS: Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient’s condition during the course of illness. KEY POINTS: • Volume, density, and location of the pulmonary opacity on CT change over time in COVID-19. • The evolution of CT appearance follows specific pattern, varying with disease severity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-06976-6) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-06-10 2020 /pmc/articles/PMC7283983/ /pubmed/32524223 http://dx.doi.org/10.1007/s00330-020-06976-6 Text en © European Society of Radiology 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Chest Wang, Yuan-Cheng Luo, Huanyuan Liu, Songqiao Huang, Shan Zhou, Zhen Yu, Qian Zhang, Shijun Zhao, Zhen Yu, Yizhou Yang, Yi Wang, Duolao Ju, Shenghong Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China |
title | Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China |
title_full | Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China |
title_fullStr | Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China |
title_full_unstemmed | Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China |
title_short | Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China |
title_sort | dynamic evolution of covid-19 on chest computed tomography: experience from jiangsu province of china |
topic | Chest |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283983/ https://www.ncbi.nlm.nih.gov/pubmed/32524223 http://dx.doi.org/10.1007/s00330-020-06976-6 |
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