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CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19—Definition and Evaluation

Open access
Prokop, Mathias, van Everdingen, Wouter, van Rees Vellinga, Tjalco, Quarles van Ufford, Henriëtte, Stöger, Lauran, Beenen, Ludo, Geurts, Bram, Gietema, Hester, Krdzalic, Jasenko, Schaefer-Prokop, Cornelia, van Ginneken, Bram, Brink, Monique, for the COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society·2020·Radiology

Abstract

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for use in the standardized assessment of pulmonary involvement of COVID-19 on unenhanced chest CT images and to report its initial interobserver agreement and performance. Materials and Methods The Dutch Radiological Society developed CO-RADS based on other efforts for standardization, such as the Lung Imaging Reporting and Data System or Breast Imaging Reporting and Data System. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients with moderate to severe symptoms of COVID-19. The system was evaluated by using 105 chest CT scans of patients admitted to the hospital with clinical suspicion of COVID-19 and in whom reverse transcription-polymerase chain reaction (RT-PCR) was performed (mean, 62 years ± 16 [standard deviation]; 61 men, 53 with positive RT-PCR results). Eight observers used CO-RADS to assess the scans. Fleiss κ value was calculated, and scores of individual observers were compared with the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared with results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss κ value was 0.47 (95% confidence interval [CI]: 0.45, 0.47), with the highest κ value for CO-RADS categories 1 (0.58, 95% CI: 0.54, 0.62) and 5 (0.68, 95% CI: 0.65, 0.72). The average AUC was 0.91 (95% CI: 0.85, 0.97) for predicting RT-PCR outcome and 0.95 (95% CI: 0.91, 0.99) for clinical diagnosis. The false-negative rate for CO-RADS 1 was nine of 161 cases (5.6%; 95% CI: 1.0%, 10%), and the false-positive rate for CO-RADS category 5 was one of 286 (0.3%; 95% CI: 0%, 1.0%). Conclusion The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) is a categorical assessment scheme for pulmonary involvement of COVID-19 at unenhanced chest CT that performs very well in predicting COVID-19 in patients with moderate to severe symptoms and has substantial interobserver agreement, especially for categories 1 and 5. © RSNA, 2020

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Metadata

Authors
Prokop, M., van Everdingen, W., van Rees Vellinga, T., Quarles van Ufford, H., Stöger, L., Beenen, L., Geurts, B., Gietema, H., Krdzalic, J., Schaefer-Prokop, C., van Ginneken, B., Brink, M., & for the COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society
Venue
Radiology
Published
2020-08
Volume
296
Issue
2
Pages
E97-E104
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
OpenAlex
W3019336217
Cited by
921
References
1
FWCI
24.29
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19—Definition and Evaluation. https://app.cochat.ai/papers/10.1148/radiol.2020201473
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Prokop, M., van Everdingen, W., van Rees Vellinga, T., Quarles van Ufford, H., Stöger, L., Beenen, L., Geurts, B., Gietema, H., Krdzalic, J., Schaefer-Prokop, C., van Ginneken, B., Brink, M., & for the COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society. (2020). CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19—Definition and Evaluation. Radiology. https://doi.org/10.1148/radiol.2020201473

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