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Chest CT Images for COVID-19: Radiologists and Computer-Based Detection
BACKGROUND: Characteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from ot...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044917/ https://www.ncbi.nlm.nih.gov/pubmed/33869276 http://dx.doi.org/10.3389/fmolb.2021.614207 |
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author | Dou, Qingli Liu, Jiangping Zhang, Wenwu Gu, Yanan Hsu, Wan-Ting Ho, Kuan-Ching Tong, Hoi Sin Yu, Wing Yan Lee, Chien-Chang |
author_facet | Dou, Qingli Liu, Jiangping Zhang, Wenwu Gu, Yanan Hsu, Wan-Ting Ho, Kuan-Ching Tong, Hoi Sin Yu, Wing Yan Lee, Chien-Chang |
author_sort | Dou, Qingli |
collection | PubMed |
description | BACKGROUND: Characteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated. PURPOSE: We aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system. METHODS: We conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate. RESULTS: The main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment. CONCLUSION: We documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation. |
format | Online Article Text |
id | pubmed-8044917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80449172021-04-15 Chest CT Images for COVID-19: Radiologists and Computer-Based Detection Dou, Qingli Liu, Jiangping Zhang, Wenwu Gu, Yanan Hsu, Wan-Ting Ho, Kuan-Ching Tong, Hoi Sin Yu, Wing Yan Lee, Chien-Chang Front Mol Biosci Molecular Biosciences BACKGROUND: Characteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated. PURPOSE: We aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system. METHODS: We conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate. RESULTS: The main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment. CONCLUSION: We documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation. Frontiers Media S.A. 2021-03-30 /pmc/articles/PMC8044917/ /pubmed/33869276 http://dx.doi.org/10.3389/fmolb.2021.614207 Text en Copyright © 2021 Dou, Liu, Zhang, Gu, Hsu, Ho, Tong, Yu and Lee. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Dou, Qingli Liu, Jiangping Zhang, Wenwu Gu, Yanan Hsu, Wan-Ting Ho, Kuan-Ching Tong, Hoi Sin Yu, Wing Yan Lee, Chien-Chang Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
title | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
title_full | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
title_fullStr | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
title_full_unstemmed | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
title_short | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
title_sort | chest ct images for covid-19: radiologists and computer-based detection |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044917/ https://www.ncbi.nlm.nih.gov/pubmed/33869276 http://dx.doi.org/10.3389/fmolb.2021.614207 |
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