Your browser doesn't support javascript.
Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.
Purkayastha, Subhanik; Xiao, Yanhe; Jiao, Zhicheng; Thepumnoeysuk, Rujapa; Halsey, Kasey; Wu, Jing; Tran, Thi My Linh; Hsieh, Ben; Choi, Ji Whae; Wang, Dongcui; Vallières, Martin; Wang, Robin; Collins, Scott; Feng, Xue; Feldman, Michael; Zhang, Paul J; Atalay, Michael; Sebro, Ronnie; Yang, Li; Fan, Yong; Liao, Wei Hua; Bai, Harrison X.
  • Purkayastha S; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Xiao Y; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Jiao Z; Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
  • Thepumnoeysuk R; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Halsey K; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Wu J; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Tran TML; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Hsieh B; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Choi JW; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Wang D; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Vallières M; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Wang R; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Collins S; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Feng X; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Feldman M; Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada.
  • Zhang PJ; Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
  • Atalay M; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Sebro R; Carina Medical, Lexington, KY, USA.
  • Yang L; Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
  • Fan Y; Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
  • Liao WH; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Bai HX; Department of Radiology, Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Korean J Radiol ; 22(7): 1213-1224, 2021 07.
Article in English | MEDLINE | ID: covidwho-1143395
Semantic information from SemMedBD (by NLM)
1. COVID-19 PROCESS_OF Cohort
Subject
COVID-19
Predicate
PROCESS_OF
Object
Cohort
2. Critical Illness PROCESS_OF Patients
Subject
Critical Illness
Predicate
PROCESS_OF
Object
Patients
3. COVID-19 PROCESS_OF Patients
Subject
COVID-19
Predicate
PROCESS_OF
Object
Patients
4. COVID-19 PROCESS_OF Cohort
Subject
COVID-19
Predicate
PROCESS_OF
Object
Cohort
5. Critical Illness PROCESS_OF Patients
Subject
Critical Illness
Predicate
PROCESS_OF
Object
Patients
6. COVID-19 PROCESS_OF Patients
Subject
COVID-19
Predicate
PROCESS_OF
Object
Patients
ABSTRACT

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 712 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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Severity of Illness Index / Tomography, X-Ray Computed / Machine Learning / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Humans / Male / Middle aged Language: English Journal: Korean J Radiol Journal subject: Radiology Year: 2021 Document Type: Article Affiliation country: Kjr.2020.1104

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Severity of Illness Index / Tomography, X-Ray Computed / Machine Learning / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Humans / Male / Middle aged Language: English Journal: Korean J Radiol Journal subject: Radiology Year: 2021 Document Type: Article Affiliation country: Kjr.2020.1104