Korean Journal of Radiology

  • 제22권7호
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  • Pages.1213-1224
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  • 2021
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

  • Subhanik Purkayastha (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Yanhe Xiao (Department of Radiology, Xiangya Hospital, Central South University) ;
  • Zhicheng Jiao (Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania) ;
  • Rujapa Thepumnoeysuk (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Kasey Halsey (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Jing Wu (Department of Radiology, Xiangya Hospital, Central South University) ;
  • Thi My Linh Tran (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Ben Hsieh (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Ji Whae Choi (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Dongcui Wang (Department of Radiology, Xiangya Hospital, Central South University) ;
  • Martin Vallieres (Department of Computer Science, Universite de Sherbrooke) ;
  • Robin Wang (Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania) ;
  • Scott Collins (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Xue Feng (Carina Medical) ;
  • Michael Feldman (Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania) ;
  • Paul J. Zhang (Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania) ;
  • Michael Atalay (Department of Diagnostic Imaging, Rhode Island Hospital) ;
  • Ronnie Sebro (Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania) ;
  • Li Yang (Department of Radiology, Xiangya Hospital, Central South University) ;
  • Yong Fan (Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania) ;
  • Wei-hua Liao (Department of Radiology, Xiangya Hospital, Central South University) ;
  • Harrison X. Bai (Department of Diagnostic Imaging, Rhode Island Hospital)
  • 투고 : 2020.09.06
  • 심사 : 2021.01.06
  • 발행 : 2021.07.01
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초록

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

키워드

과제정보

This study was supported by the Brown COVID-19 Research Seed Award, the Amazon Web Services Diagnostic Development Initiative, RSNA Research Scholar Grant, and National Institutes of Health/National Cancer Institute R03 grant (R03CA249554) to Dr. Harrison X. Bai, as well as NIH grants (CA223358, DK117297, MH120811, and EB022573) to Dr. Yong Fan.

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