Author:
Henao John Anderson Garcia,Depotter Arno,Bower Danielle V.,Bajercius Herkus,Todorova Plamena Teodosieva,Saint-James Hugo,de Mortanges Aurélie Pahud,Barroso Maria Cecilia,He Jianchun,Yang Junlin,You Chenyu,Staib Lawrence H.,Gange Christopher,Ledda Roberta Eufrasia,Caminiti Caterina,Silva Mario,Cortopassi Isabel Oliva,Dela Cruz Charles S.,Hautz Wolf,Bonel Harald M.,Sverzellati Nicola,Duncan James S.,Reyes Mauricio,Poellinger Alexander
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 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.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Radiology, Nuclear Medicine and imaging,General Medicine