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Severity Assessment of COVID-19 Using a CT-Based Radiomics Model.
Xu, Zhigao; Zhao, Lili; Yang, Guoqiang; Ren, Ying; Wu, Jinlong; Xia, Yuwei; Yang, Xuhong; Cao, Milan; Zhang, Guojiang; Peng, Taisong; Zhao, Jiafeng; Yang, Hui; Hu, Jinfeng; Du, Jiangfeng.
  • Xu Z; College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001 Shanxi Province, China.
  • Zhao L; Department of Radiology, The Third People's Hospital of Datong, Datong, 037008 Shanxi Province, China.
  • Yang G; Department of Radiology, The Third People's Hospital of Datong, Datong, 037008 Shanxi Province, China.
  • Ren Y; College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001 Shanxi Province, China.
  • Wu J; Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001 Shanxi Province, China.
  • Xia Y; Department of Materials Science and Engineering, Henan University of Technology, Zhengzhou, 450001 Henan Province, China.
  • Yang X; Department of Radiology, The Third People's Hospital of Datong, Datong, 037008 Shanxi Province, China.
  • Cao M; Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing City, 100192, China.
  • Zhang G; Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Haidian District, Beijing City, 100192, China.
  • Peng T; Department of Science and Education, The Third People's Hospital of Datong, Datong, 037008 Shanxi Province, China.
  • Zhao J; Department of Science and Education, The Third People's Hospital of Datong, Datong, 037008 Shanxi Province, China.
  • Yang H; Department of Radiology, The Second People's Hospital of Datong, Datong, 037005 Shanxi Province, China.
  • Hu J; Department of Rehabilitation Medicine, Xiantao First People's Hospital, Xiantao, 433000 Hubei Province, China.
  • Du J; Department of Radiology, The Third People's Hospital of Datong, Datong, 037008 Shanxi Province, China.
Stem Cells Int ; 2021: 2263469, 2021.
Article in English | MEDLINE | ID: covidwho-1443669
ABSTRACT
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 f 1-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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Stem Cells Int Year: 2021 Document Type: Article Affiliation country: 2021

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Stem Cells Int Year: 2021 Document Type: Article Affiliation country: 2021