tetano
Editor, Senior Moderator
Korean J Radiol
. 2021 Mar 9.
doi: 10.3348/kjr.2020.1104. Online ahead of print.
Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data
Subhanik Purkayastha[SUP] #[/SUP][SUP] 1 [/SUP], Yanhe Xiao[SUP] #[/SUP][SUP] 2 [/SUP], Zhicheng Jiao[SUP] 3 [/SUP], Rujapa Thepumnoeysuk[SUP] 1 [/SUP], Kasey Halsey[SUP] 1 4 [/SUP], Jing Wu[SUP] 2 [/SUP], Thi My Linh Tran[SUP] 1 4 [/SUP], Ben Hsieh[SUP] 1 4 [/SUP], Ji Whae Choi[SUP] 1 4 [/SUP], Dongcui Wang[SUP] 2 [/SUP], Martin Valli?res[SUP] 5 [/SUP], Robin Wang[SUP] 3 [/SUP], Scott Collins[SUP] 1 [/SUP], Xue Feng[SUP] 6 [/SUP], Michael Feldman[SUP] 7 [/SUP], Paul J Zhang[SUP] 7 [/SUP], Michael Atalay[SUP] 1 [/SUP], Ronnie Sebro[SUP] 3 [/SUP], Li Yang[SUP] 2 [/SUP], Yong Fan[SUP] 3 [/SUP], Wei Hua Liao[SUP] 8 [/SUP], Harrison X Bai[SUP] 1 9 [/SUP]
Affiliations
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 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.
Keywords: COVID-19; CT; Machine learning; Radiomics; Severity.
. 2021 Mar 9.
doi: 10.3348/kjr.2020.1104. Online ahead of print.
Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data
Subhanik Purkayastha[SUP] #[/SUP][SUP] 1 [/SUP], Yanhe Xiao[SUP] #[/SUP][SUP] 2 [/SUP], Zhicheng Jiao[SUP] 3 [/SUP], Rujapa Thepumnoeysuk[SUP] 1 [/SUP], Kasey Halsey[SUP] 1 4 [/SUP], Jing Wu[SUP] 2 [/SUP], Thi My Linh Tran[SUP] 1 4 [/SUP], Ben Hsieh[SUP] 1 4 [/SUP], Ji Whae Choi[SUP] 1 4 [/SUP], Dongcui Wang[SUP] 2 [/SUP], Martin Valli?res[SUP] 5 [/SUP], Robin Wang[SUP] 3 [/SUP], Scott Collins[SUP] 1 [/SUP], Xue Feng[SUP] 6 [/SUP], Michael Feldman[SUP] 7 [/SUP], Paul J Zhang[SUP] 7 [/SUP], Michael Atalay[SUP] 1 [/SUP], Ronnie Sebro[SUP] 3 [/SUP], Li Yang[SUP] 2 [/SUP], Yong Fan[SUP] 3 [/SUP], Wei Hua Liao[SUP] 8 [/SUP], Harrison X Bai[SUP] 1 9 [/SUP]
Affiliations
- PMID: 33739635
- DOI: 10.3348/kjr.2020.1104
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 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.
Keywords: COVID-19; CT; Machine learning; Radiomics; Severity.