Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019
To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-04-01
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| Series: | Journal of Pharmaceutical Analysis |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177920302264 |
| _version_ | 1818612142512799744 |
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| author | Cong Shen Nan Yu Shubo Cai Jie Zhou Jiexin Sheng Kang Liu Heping Zhou Youmin Guo Gang Niu |
| author_facet | Cong Shen Nan Yu Shubo Cai Jie Zhou Jiexin Sheng Kang Liu Heping Zhou Youmin Guo Gang Niu |
| author_sort | Cong Shen |
| collection | DOAJ |
| description | To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist- and computer-derived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (r ranged from 0.7679 to 0.8373, P < 0.05), and a moderate correlation between the proportion of GGO and mean lesion density (r = −0.5894, P < 0.05), and proportion of consolidation and mean lesion density (r = 0.6282, P < 0.05). Computer-aided quantification showed a statistical significant higher lesion percentage for lower lobes than that assessed by the radiologists (χ2 = 8.160, P = 0.004). Our experiments demonstrated that the computer tool could reliably and accurately assess the severity and distribution of pneumonia on CT scans. Keywords: Quantitative computed tomography (QCT), Coronavirus disease 2019 (COVID-19), Severity stratification |
| first_indexed | 2024-12-16T15:41:31Z |
| format | Article |
| id | doaj.art-25efd9a7eefe4e829211c64a97d8e1a4 |
| institution | Directory Open Access Journal |
| issn | 2095-1779 |
| language | English |
| last_indexed | 2024-12-16T15:41:31Z |
| publishDate | 2020-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj.art-25efd9a7eefe4e829211c64a97d8e1a42022-12-21T22:25:59ZengElsevierJournal of Pharmaceutical Analysis2095-17792020-04-01102123129Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019Cong Shen0Nan Yu1Shubo Cai2Jie Zhou3Jiexin Sheng4Kang Liu5Heping Zhou6Youmin Guo7Gang Niu8Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, ChinaDepartment of Radiology, The Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Xianyang, 712000, Shaanxi, ChinaDepartment of Radiology, Xi’an Chest Hospital, Xi’an, 710100, Shaanxi, ChinaDepartment of Radiology, Xi’an Chest Hospital, Xi’an, 710100, Shaanxi, ChinaDepartment of Radiology, Hanzhong Central Hospital, Hanzhong, 723000, Shaanxi, ChinaDepartment of CT&MR Imaging, Weinan Central Hospital, Weinan, 714000, Shaanxi, ChinaDepartment of Radiology, Ankang Central Hospital, Ankang, 725000, Shaanxi, ChinaDepartment of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, China; Corresponding authors.Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, China; Corresponding authors.To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist- and computer-derived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (r ranged from 0.7679 to 0.8373, P < 0.05), and a moderate correlation between the proportion of GGO and mean lesion density (r = −0.5894, P < 0.05), and proportion of consolidation and mean lesion density (r = 0.6282, P < 0.05). Computer-aided quantification showed a statistical significant higher lesion percentage for lower lobes than that assessed by the radiologists (χ2 = 8.160, P = 0.004). Our experiments demonstrated that the computer tool could reliably and accurately assess the severity and distribution of pneumonia on CT scans. Keywords: Quantitative computed tomography (QCT), Coronavirus disease 2019 (COVID-19), Severity stratificationhttp://www.sciencedirect.com/science/article/pii/S2095177920302264 |
| spellingShingle | Cong Shen Nan Yu Shubo Cai Jie Zhou Jiexin Sheng Kang Liu Heping Zhou Youmin Guo Gang Niu Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019 Journal of Pharmaceutical Analysis |
| title | Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019 |
| title_full | Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019 |
| title_fullStr | Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019 |
| title_full_unstemmed | Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019 |
| title_short | Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019 |
| title_sort | quantitative computed tomography analysis for stratifying the severity of coronavirus disease 2019 |
| url | http://www.sciencedirect.com/science/article/pii/S2095177920302264 |
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