Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics.

Autor:	Sähn MJ; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Yüksel C; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Keil S; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Zeisberger MP; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Post M; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Kleines M; Laboratory Diagnostics Center, Universitätsklinikum Aachen, Germany., Brokmann JC; Emergency Department, Universitätsklinikum Aachen, Germany., Hübel C; Emergency Department, Universitätsklinikum Aachen, Germany., Kuhl CK; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Isfort P; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany., Schulze-Hagen MF; Department of Diagnostic and Interventional Radiology, Universitätsklinikum Aachen, Germany.
Jazyk:	angličtina
Zdroj:	RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin [Rofo] 2021 Sep; Vol. 193 (9), pp. 1081-1091. Date of Electronic Publication: 2021 Mar 26.
DOI:	10.1055/a-1388-7950
Abstrakt:	Purpose: To determine the performance of radiologists with different levels of expertise regarding the differentiation of COVID-19 from other atypical pneumonias. Chest CT to identify patients suffering from COVID-19 has been reported to be limited by its low specificity for distinguishing COVID-19 from other atypical pneumonias ("COVID-19 mimics"). Meanwhile, the understanding of the morphologic patterns of COVID-19 has improved and they appear to be fairly specific. Materials and Methods: Between 02/2020 and 04/2020, 60 patients with COVID-19 pneumonia underwent chest CT in our department. Cases were matched with a comparable control group of 60 patients of similar age, sex, and comorbidities, who underwent chest CT prior to 01/2020 for atypical pneumonia caused by other pathogens. Included were other viral, fungal, and bacterial pathogens. All 120 cases were blinded to patient history and were reviewed independently by two radiologists and two radiology residents. Readers rated the probability of COVID-19 pneumonia according to the COV-RADS classification system. Results were analyzed using Clopper-Pearson 95 % confidence intervals, Youden's Index for test quality criteria, and Fleiss' kappa statistics. Results: Overall, readers were able to correctly identify the presence of COVID-19 pneumonia in 219/240 (sensitivity: 91 %; 95 %-CI; 86.9 %-94.5 %), and to correctly attribute CT findings to COVID-19 mimics in 159/240 ratings (specificity: 66.3 %; 59.9 %-72.2 %), yielding an overall diagnostic accuracy of 78.8 % (378/480; 74.8 %-82.3 %). Individual reader accuracy ranged from 74.2 % (89/120) to 84.2 % (101/120) and did not correlate significantly with reader expertise. Youden's Index was 0.57. Between-reader agreement was moderate (κ = 0.53). Conclusion: In this enriched cohort, radiologists were able to distinguish COVID-19 from "COVID-19 mimics" with moderate diagnostic accuracy. Accuracy did not correlate with reader expertise. Key Points: · In a scenario of direct comparison (no negative findings), CT allows the differentiation of COVID-19 from other atypical pneumonias ("COVID mimics") with moderate accuracy.. · Reader expertise did not significantly influence these results.. · Despite similar patterns and distributions of pulmonary findings, radiologists were able to estimate the probability of COVID-19 pneumonia using the COV-RADS classification in a standardized manner in the larger proportion of cases.. Citation Format: · Sähn M, Yüksel C, Keil S et al. Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics. Fortschr Röntgenstr 2021; 193: 1081 - 1091. Competing Interests: The authors declare that they have no conflict of interest. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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