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Published on: December 19, 2020
A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease
John Anderson Garcia Henao1, Arno Depotter, Danielle V Bower
1From the ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland (J.A.G.H., M.R.); Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital Bern, University of Bern, Bern, Switzerland (A.D., D.V.B., H.B., P.T.T., H.S.-J., M.C.B., H.M.B., A.P.); Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT (J.H., L.H.S., C.G., J.S.D.); Department of Biomedical Engineering, Yale University, New Haven, CT (J.H., J.Y., L.H.S., J.S.D.); Department of Electrical Engineering, Yale University, New Haven, CT (C.Y.); Section of "Scienze Radiologiche," Diagnostic Department, University Hospital of Parma, Parma, Italy (R.E.L., M.S., N.S.); Department of Medicine and Surgery, University of Parma, Italy (R.E.L., N.S.); Ricerca Clinica ed Epidemiologica, University Hospital of Parma, Parma, Italy (C.C.); Department of Radiology at Mayo Clinic College of Medicine and Science, Florida, Jacksonville, FL (I.O.C.); Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (C.S.D.C.); Department of Emergency Medicine, Inselspital University Hospital, University of Bern, Bern, Switzerland (W.H.); Campusradiologie, Department of Radiological Diagnostics, Lindenhofspital Bern, Bern, Switzerland (H.M.B.).; Campus Stiftung Lindenhof Bern, Bern, Switzerland (H.M.B.); and Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern, Switzerland (M.R.).
Objectives:
The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans.
Materials And Methods:
The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion.
Results:
AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95.
Conclusions:
A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.
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