COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis - Carvalho, Alysson Roncally S.; Guimarães, Alan; Werberich, Gabriel Madeira; de Castro, Stephane Nery; Pinto, Joana Sofia F.; Schmitt, Willian Rebouças; França, Manuela; Bozza, Fernando Augusto; Guimarães, Bruno Leonardo da Silva; Zin, Walter Araujo; Rodrigues, Rosana Souza (Frontiers in Medicine, 2020)
Purpose: To develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement in COVID-19 as well as the radiological patterns referred to lung opacities in chest computer tomography (CT). Methods: One-hundred thirty subjects with COVID-19 pneumonia that 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 1475 ROIs) visually regarded as well-aerated (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 densities quantiles (2.5, 25, 50, 75 and 97.5%) of 1000 ROIs were used to train (700), validate (150) and test (150 ROIs) an artificial neural network classifier (60 neurons 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 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%, 0.80 sensitivity, 0.86 specificity, AUC 0.90, accuracy of 0.82, F score of 0.85 and 0.65 Matthews’ correlation coefficient. Accordingly, seventy-seven 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, higher proportion of hospitalization in intensive care units, 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.