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COVID-19 CHEST COMPUTED TOMOGRAPHY TO STRATIFY SEVERITY AND DISEASE EXTENSION BY ARTIFICIAL NEURAL NETWORK COMPUTER-AIDED DIAGNOSIS
Computer-aided diagnosis
Deep learning
Quantitative chest CT-analysis
Radiomics
Pneumonia
Author
Affilliation
Porto University. Centro Hospitalar Universitário Do Porto. Faculty of Medicine. Cardiovascular R&D Center. Porto, Portugal / Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil / Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Departamento de Radiologia. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Hospital Barra D'Or. Rio de Janeiro, RJ, Brasil.
Centro Hospitalar Complexo Universitário Do Porto. Radiology Department. Porto, Portugal.
Centro Hospitalar Complexo Universitário Do Porto. Radiology Department. Porto, Portugal.
Centro Hospitalar Complexo Universitário Do Porto. Radiology Department. Porto, Portugal / Porto University. Instituto de Ciências Biomédicas Abel Salazar. Porto, Portugal.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil.
Hospital Niterói D'Or. Niterói, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Departamento de Radiologia. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Departamento de Radiologia. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Hospital Barra D'Or. Rio de Janeiro, RJ, Brasil.
Centro Hospitalar Complexo Universitário Do Porto. Radiology Department. Porto, Portugal.
Centro Hospitalar Complexo Universitário Do Porto. Radiology Department. Porto, Portugal.
Centro Hospitalar Complexo Universitário Do Porto. Radiology Department. Porto, Portugal / Porto University. Instituto de Ciências Biomédicas Abel Salazar. Porto, Portugal.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil.
Hospital Niterói D'Or. Niterói, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Departamento de Radiologia. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Educação. Rio de Janeiro, RJ, Brasil.
Abstract
Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (<cutoff of normality), moderate (cutoff of normality ≤ pulmonary involvement < Z score 3), and severe pulmonary involvement (Z score ≥3). Results: Cohen's kappa agreement between CAD and radiologist classification was 81% (79-84%, 95% CI). The ROC curve of PI by the ANN presented a threshold of 21.5%, sensitivity of 0.80, specificity of 0.86, AUC of 0.90, accuracy of 0.82, F score of 0.85, and 0.65 Matthews' correlation coefficient. Accordingly, 77 patients were classified as having severe pulmonary involvement reaching 55 ± 13% of the TLV (Z score related to controls ≥3) and presented significantly higher lung weight, serum C-reactive protein concentration, proportion of hospitalization in intensive care units, instances of mechanical ventilation, and case fatality. Conclusion: The proposed CAD aided in detecting and quantifying the extent of pulmonary involvement, helping to phenotype patients with COVID-19 pneumonia.
Keywords
COVID-19Computer-aided diagnosis
Deep learning
Quantitative chest CT-analysis
Radiomics
Pneumonia
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