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Diagn Interv Imaging ; 102(9): 493-500, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1397290


Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool to differentiate COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately depict myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.

COVID-19 , Heart Diseases , Artificial Intelligence , COVID-19/complications , COVID-19/diagnostic imaging , Heart Diseases/virology , Humans , Tomography, X-Ray Computed
Jpn J Radiol ; 39(5): 451-458, 2021 May.
Article in English | MEDLINE | ID: covidwho-1064585


PURPOSE: To assess the relationships among pulmonary vascular enlargement, computed tomography (CT) findings quantified with software, and coronavirus disease (COVID-19) severity. MATERIALS AND METHODS: Ultra-high-resolution (UHR) CT images of 87 patients (50 males, 37 females; median age, 63 years) with COVID-19 confirmed using real-time polymerase chain reaction were analyzed. The maximum subsegmental vascular diameter was measured on CT. Total CT lung volume (CTLV total) and lesion extent (ratio of lesion volume to CTLV total) of ground-glass opacities, reticulation, and consolidation were measured using software. Maximum pulmonary vascular diameter and lesion extent were analyzed using Spearman's correlation analysis. Logistic regression analysis was performed on CT results to predict disease severity. We also assessed changes in these measures on follow-up scans in 16 patients. RESULTS: All 23 patients with severe and critical illness had vascular enlargement (> 4 mm). Pulmonary vascular enlargement (odds ratio 3.05, p = 0.018) and CT lesion extent (odds ratio 1.07, p = 0.002) were independent predictors of disease severity after adjustment for age and comorbidities. On follow-up CT, vascular diameter and CT lesion volume decreased (p = 0.001, p = 0.002; respectively), but CTLV total did not change significantly. CONCLUSION: Subsegmental vascular enlargement is a notable finding to predict acute COVID-19 disease severity.

COVID-19/diagnosis , Lung/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Young Adult