A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans
type: Article
item type: journalArticle
is part of: Invest Radiol
authors: Person
  surname: Henao
  given name: J. A. G.
authors: Person
  surname: Depotter
  given name: A.
authors: Person
  surname: Bower
  given name: D. V.
authors: Person
  surname: Bajercius
  given name: H.
authors: Person
  surname: Todorova
  given name: P. T.
authors: Person
  surname: Saint-James
  given name: H.
authors: Person
  surname: de Mortanges
  given name: A. P.
authors: Person
  surname: Barroso
  given name: M. C.
authors: Person
  surname: He
  given name: J.
authors: Person
  surname: Yang
  given name: J.
authors: Person
  surname: You
  given name: C.
authors: Person
  surname: Staib
  given name: L. H.
authors: Person
  surname: Gange
  given name: C.
authors: Person
  surname: Ledda
  given name: R. E.
authors: Person
  surname: Caminiti
  given name: C.
authors: Person
  surname: Silva
  given name: M.
authors: Person
  surname: Cortopassi
  given name: I. O.
authors: Person
  surname: Dela Cruz
  given name: C. S.
authors: Person
  surname: Hautz
  given name: W.
authors: Person
  surname: Bonel
  given name: H. M.
authors: Person
  surname: Sverzellati
  given name: N.
authors: Person
  surname: Duncan
  given name: J. S.
authors: Person
  surname: Reyes
  given name: M.
authors: Person
  surname: Poellinger
  given name: A.
subject: *covid-19
subject: Artificial Intelligence
subject: Disease Progression
subject: Humans
subject: Lung/diagnostic imaging
subject: Retrospective Studies
subject: Tomography, X-Ray Computed/methods
abstract: 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 kappa 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 mo…
date: 2023-07-26
pages: 882-893
coverage: 37493348

Invest Radiol
type: Journal
volume: 58
number: 12
identifier: DOI 10.1097/RLI.0000000000001005
identifier: ISSN 1536-0210 (Electronic) 0020-9996 (Print) 0020-9996 (Linking)