Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients

Introduction COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients. Materials and methods From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients andp < 0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis. Results Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66 +/- 16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63 +/- 15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55 +/- 0.26L vs 0.43 +/- 0.23L,p = 0.0005) and fibrotic alterations (0.05 +/- 0.03 L vs 0.04 +/- 0.03 L,p < 0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity,p < 0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity,p < 0.001), respectively. Conclusions Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19.