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. 2021 Jul;22(7):1213-1224.
doi: 10.3348/kjr.2020.1104. Epub 2021 Mar 9.

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

Subhanik Purkayastha #  1 , Yanhe Xiao #  2 , Zhicheng Jiao  3 , Rujapa Thepumnoeysuk  1 , Kasey Halsey  1   4 , Jing Wu  2 , Thi My Linh Tran  1   4 , Ben Hsieh  1   4 , Ji Whae Choi  1   4 , Dongcui Wang  2 , Martin Vallières  5 , Robin Wang  3 , Scott Collins  1 , Xue Feng  6 , Michael Feldman  7 , Paul J Zhang  7 , Michael Atalay  1 , Ronnie Sebro  3 , Li Yang  2 , Yong Fan  3 , Wei Hua Liao  8 , Harrison X Bai  1   9
Affiliations

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

Subhanik Purkayastha et al. Korean J Radiol. 2021 Jul.

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.

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Conflict of interest statement

The authors have no potential conflicts of interest to disclose.

Figures

Fig. 1
Fig. 1. Illustration of patient inclusion and exclusion.
Adapted from Zhang et al. Cell 2020;181:1423-1433.e11 [16]. HUP = Hospital of the University of Pennsylvania, RIH = Rhode Island Hospital, RT-PCR = reverse transcriptase-polymerase chain reaction
Fig. 2
Fig. 2. Illustration of our analysis pipeline.
A. Radiomics feature representation. For each patient, 1583 radiomics features were extracted from automatically segmented lung regions. B. Radiomics based severity prediction. Binary classifiers were applied to classify the patients into severe or non-severe classes based on the radiomics features. C. Radiomics based progression prediction. A random survival forest model was optimized based on the 1583 radiomics features to assign risk scores to different subjects. D. Clinically based progression prediction. Fifteen clinical variables extracted from demographic recordings were input to another survival forest model to assign risk scores to different subjects. Finally, for each patient, the deep learning-based and clinical-based predictions were added with two balanced weights to obtain the combined progression risk score.
Fig. 3
Fig. 3. Time-dependent ROC curves and AUCs for days 3, 5, and 7 for three progression models.
A–C. The results for the three models are shown: one trained on radiomics features, one trained on clinical variables, and one trained on the combination of radiomics features and clinical variables. The x-axis represents the false-positive rate and the y-axis represents the true-positive rate. AUC = area under the curve, ROC = receiver operating characteristic
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