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

Abstract

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 and p < 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.

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome

“…In other words, the most useful prognostic CT sign in predicting the outcome of COVID-19 patients was the expression of global lung involvement, regardless of the type of alteration and the consolidation density of the images. Although some recent papers [25][26][27] described some results on quantification based on open-source software for semi-automated pulmonary segmentation, in our experience, the visual analysis of lung involvement proved to be a quick, easy-to-use and reliable method for the evaluation and quantitation of lung involvement. This procedure can also be used also in an emergency scenario, independently of sophisticated, different, and still not fully comparable software-based methods for the interpretation of CT images.…”

“…The Fleischner Society issued a consensus statement in order to explore the best application of imaging, primarily CT, for the evaluation and risk stratification of patients [10], acknowledging that, in addition to supporting diagnosis, CT has also revealed its usefulness for providing diagnostic information. This approach requires the quantification of abnormalities that currently can be reached through visual analysis [19][20][21][22][23], or more recently using a software-based assessment [24][25][26][27] or, possibly in the future, artificial intelligence (A.I.) [28][29][30].…”

Lymphadenopathy after BNT162b2 Covid-19 Vaccine: Preliminary Ultrasound Findings

“…Recent results have revealed the efficiency of some imaging methods, including chest radiographs and chest computed tomography scans, in the management of COVID-19 disease [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Instead, at the best of our knowledge, this is the first paper describing the appearance of nodes after BNT162b2 Covid-19 vaccine.…”

A Pictorial Review of the Role of Imaging in the Detection, Management, Histopathological Correlations, and Complications of COVID-19 Pneumonia

“…The prognostic value of CT has been reported by several studies [ 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 ]. The use of a CT severity score (CT-SS) may be useful to standardize the assessment of lung alterations in COVID-19 pneumonia and to stratify patient risk and predict short-term outcomes [ 108 , 145 , 148 , 150 , 151 , 152 ].…”