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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. 45:782-787
- Publication Year :
- 2021
- Publisher :
- Ovid Technologies (Wolters Kluwer Health), 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. 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. 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. CONCLUSIONS: The diagnostic performance of CO-RADS for predicting COVID-19 pneumonia showed moderate interobserver agreement between residents and radiologists.
- Subjects :
- Male
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19)
Diagnostic accuracy
Computed tomography
Sensitivity and Specificity
Radiologists
medicine
Humans
Radiology, Nuclear Medicine and imaging
In patient
Lung
Reference standards
Aged
Retrospective Studies
Receiver operating characteristic
medicine.diagnostic_test
SARS-CoV-2
business.industry
COVID-19
Internship and Residency
Reproducibility of Results
Retrospective cohort study
Middle Aged
medicine.disease
Pneumonia
Radiology Information Systems
Female
Radiology
Tomography, X-Ray Computed
business
Subjects
Details
- ISSN :
- 15323145 and 03638715
- Volume :
- 45
- Database :
- OpenAIRE
- Journal :
- Journal of Computer Assisted Tomography
- Accession number :
- edsair.doi.dedup.....cd836ea497a0e9f231430c25ce38c0ba
- Full Text :
- https://doi.org/10.1097/rct.0000000000001172