Severity Assessment of COVID-19 Using a CT-Based Radiomics Model
The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists’ subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on d...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Stem Cells International |
| Online Access: | http://dx.doi.org/10.1155/2021/2263469 |
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| author | Zhigao Xu Lili Zhao Guoqiang Yang Ying Ren Jinlong Wu Yuwei Xia Xuhong Yang Milan Cao Guojiang Zhang Taisong Peng Jiafeng Zhao Hui Yang Jinfeng Hu Jiangfeng Du |
| author_facet | Zhigao Xu Lili Zhao Guoqiang Yang Ying Ren Jinlong Wu Yuwei Xia Xuhong Yang Milan Cao Guojiang Zhang Taisong Peng Jiafeng Zhao Hui Yang Jinfeng Hu Jiangfeng Du |
| author_sort | Zhigao Xu |
| collection | DOAJ |
| description | The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists’ subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n=75), progressive (n=58), severe (n=75), and absorption (n=76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19. |
| format | Article |
| id | doaj-art-b9bd3c05e79e45888597d600d2d0cada |
| institution | Kabale University |
| issn | 1687-966X 1687-9678 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stem Cells International |
| spelling | doaj-art-b9bd3c05e79e45888597d600d2d0cada2025-02-03T01:12:54ZengWileyStem Cells International1687-966X1687-96782021-01-01202110.1155/2021/22634692263469Severity Assessment of COVID-19 Using a CT-Based Radiomics ModelZhigao Xu0Lili Zhao1Guoqiang Yang2Ying Ren3Jinlong Wu4Yuwei Xia5Xuhong Yang6Milan Cao7Guojiang Zhang8Taisong Peng9Jiafeng Zhao10Hui Yang11Jinfeng Hu12Jiangfeng Du13College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001 Shanxi Province, ChinaDepartment of Radiology, The Third People’s Hospital of Datong, Datong, 037008 Shanxi Province, ChinaCollege of Medical Imaging, Shanxi Medical University, Taiyuan, 030001 Shanxi Province, ChinaDepartment of Materials Science and Engineering, Henan University of Technology, Zhengzhou, 450001 Henan Province, ChinaDepartment of Radiology, The Third People’s Hospital of Datong, Datong, 037008 Shanxi Province, ChinaHuiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing City, 100192, ChinaHuiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing City, 100192, ChinaDepartment of Science and Education, The Third People’s Hospital of Datong, Datong, 037008 Shanxi Province, ChinaDepartment of Science and Education, The Third People’s Hospital of Datong, Datong, 037008 Shanxi Province, ChinaDepartment of Radiology, The Second People’s Hospital of Datong, Datong, 037005 Shanxi Province, ChinaDepartment of Rehabilitation Medicine, Xiantao First People’s Hospital, Xiantao, 433000 Hubei Province, ChinaDepartment of Radiology, The Third People’s Hospital of Datong, Datong, 037008 Shanxi Province, ChinaDepartment of Radiology, The Second People’s Hospital of Datong, Datong, 037005 Shanxi Province, ChinaCollege of Medical Imaging, Shanxi Medical University, Taiyuan, 030001 Shanxi Province, ChinaThe coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists’ subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n=75), progressive (n=58), severe (n=75), and absorption (n=76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.http://dx.doi.org/10.1155/2021/2263469 |
| spellingShingle | Zhigao Xu Lili Zhao Guoqiang Yang Ying Ren Jinlong Wu Yuwei Xia Xuhong Yang Milan Cao Guojiang Zhang Taisong Peng Jiafeng Zhao Hui Yang Jinfeng Hu Jiangfeng Du Severity Assessment of COVID-19 Using a CT-Based Radiomics Model Stem Cells International |
| title | Severity Assessment of COVID-19 Using a CT-Based Radiomics Model |
| title_full | Severity Assessment of COVID-19 Using a CT-Based Radiomics Model |
| title_fullStr | Severity Assessment of COVID-19 Using a CT-Based Radiomics Model |
| title_full_unstemmed | Severity Assessment of COVID-19 Using a CT-Based Radiomics Model |
| title_short | Severity Assessment of COVID-19 Using a CT-Based Radiomics Model |
| title_sort | severity assessment of covid 19 using a ct based radiomics model |
| url | http://dx.doi.org/10.1155/2021/2263469 |
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