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Diagnostic Performance of COVID-19 Reporting and Data System Classification Across Residents and Radiologists: A Retrospective Study.
- Source :
-
Journal of computer assisted tomography [J Comput Assist Tomogr] 2021 Sep-Oct 01; Vol. 45 (5), pp. 782-787. - Publication Year :
- 2021
-
Abstract
- Objective: The aim of the study was to evaluate the interobserver agreement and diagnostic accuracy of COVID-19 Reporting and Data System (CO-RADS), in patients suspected COVID-19 pneumonia.<br />Methods: Two hundred nine nonenhanced chest computed tomography images of patients with clinically suspected COVID-19 pneumonia were included. The images were evaluated by 2 groups of observers, consisting of 2 residents-radiologists, using CO-RADS. Reverse transcriptase-polymerase chain reaction (PCR) was used as a reference standard for diagnosis in this study. Sensitivity, specificity, area under receiver operating characteristic curve (AUC), and intraobserver/interobserver agreement were calculated.<br />Results: COVID-19 Reporting and Data System was able to distinguish patients with positive PCR results from those with negative PCR results with AUC of 0.796 in the group of residents and AUC of 0.810 in the group of radiologists. There was moderate interobserver agreement between residents and radiologist with κ values of 0.54 and 0.57.<br />Conclusions: The diagnostic performance of CO-RADS for predicting COVID-19 pneumonia showed moderate interobserver agreement between residents and radiologists.<br />Competing Interests: The authors declare no conflict of interest.<br /> (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)
- Subjects :
- Aged
Female
Humans
Lung diagnostic imaging
Male
Middle Aged
Reproducibility of Results
Retrospective Studies
SARS-CoV-2
Sensitivity and Specificity
COVID-19 diagnostic imaging
Internship and Residency statistics & numerical data
Radiologists statistics & numerical data
Radiology Information Systems standards
Tomography, X-Ray Computed methods
Subjects
Details
- Language :
- English
- ISSN :
- 1532-3145
- Volume :
- 45
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- Journal of computer assisted tomography
- Publication Type :
- Academic Journal
- Accession number :
- 34176881
- Full Text :
- https://doi.org/10.1097/RCT.0000000000001172