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Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia
In 2019, a large outbreak of a novel coronavirus disease (COVID-19) occurred in China. The purpose of this study is to quantitatively analyze the evolution of chest computed tomography (CT) imaging features in COVID-19. Nine patients with positive real-time reverse-transcriptase polymerase chain rea...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Formosan Medical Association. Published by Elsevier Taiwan LLC.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324409/ https://www.ncbi.nlm.nih.gov/pubmed/34373176 http://dx.doi.org/10.1016/j.jfma.2021.07.021 |
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author | Lan, Gong-Yau Lee, Yuarn-Jang Wu, Jen-Chung Lai, Hsin-Yi Liu, Hsin-Y- Chuang, Han-Chuan Li-Chun Hsieh, Kevin |
author_facet | Lan, Gong-Yau Lee, Yuarn-Jang Wu, Jen-Chung Lai, Hsin-Yi Liu, Hsin-Y- Chuang, Han-Chuan Li-Chun Hsieh, Kevin |
author_sort | Lan, Gong-Yau |
collection | PubMed |
description | In 2019, a large outbreak of a novel coronavirus disease (COVID-19) occurred in China. The purpose of this study is to quantitatively analyze the evolution of chest computed tomography (CT) imaging features in COVID-19. Nine patients with positive real-time reverse-transcriptase polymerase chain reaction results were included in this study. Totally 19 CT scans were analyzed. Lesion density, lesion volume, and lesion load were higher in the severe group than in the mild group. A significantly positive correlation was noted between major laboratory prognosticators with lesion volume and load. Lesion load at the first week of disease was significantly higher in severe group (p = 0.03). Our study revealed that several CT features were significantly different between severely and mildly infected forms of COVID-19 pneumonia. The CT lesion load value at the first week of infection may be applied as an outcome predictor of the disease. |
format | Online Article Text |
id | pubmed-8324409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Formosan Medical Association. Published by Elsevier Taiwan LLC. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83244092021-08-02 Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia Lan, Gong-Yau Lee, Yuarn-Jang Wu, Jen-Chung Lai, Hsin-Yi Liu, Hsin-Y- Chuang, Han-Chuan Li-Chun Hsieh, Kevin J Formos Med Assoc Short Communication In 2019, a large outbreak of a novel coronavirus disease (COVID-19) occurred in China. The purpose of this study is to quantitatively analyze the evolution of chest computed tomography (CT) imaging features in COVID-19. Nine patients with positive real-time reverse-transcriptase polymerase chain reaction results were included in this study. Totally 19 CT scans were analyzed. Lesion density, lesion volume, and lesion load were higher in the severe group than in the mild group. A significantly positive correlation was noted between major laboratory prognosticators with lesion volume and load. Lesion load at the first week of disease was significantly higher in severe group (p = 0.03). Our study revealed that several CT features were significantly different between severely and mildly infected forms of COVID-19 pneumonia. The CT lesion load value at the first week of infection may be applied as an outcome predictor of the disease. Formosan Medical Association. Published by Elsevier Taiwan LLC. 2022-03 2021-07-31 /pmc/articles/PMC8324409/ /pubmed/34373176 http://dx.doi.org/10.1016/j.jfma.2021.07.021 Text en © 2021 Formosan Medical Association. Published by Elsevier Taiwan LLC. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Short Communication Lan, Gong-Yau Lee, Yuarn-Jang Wu, Jen-Chung Lai, Hsin-Yi Liu, Hsin-Y- Chuang, Han-Chuan Li-Chun Hsieh, Kevin Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
title | Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
title_full | Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
title_fullStr | Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
title_full_unstemmed | Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
title_short | Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
title_sort | serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324409/ https://www.ncbi.nlm.nih.gov/pubmed/34373176 http://dx.doi.org/10.1016/j.jfma.2021.07.021 |
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