Diagnostic and Interventional Radiology
Chest Imaging - Original Article

COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging

1.

Department of Radiology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey

2.

Department of Pulmonary and Critical Care, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey

3.

Department of Medical Microbiology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey

4.

Department of Infectious Disease and Clinical Microbiology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey

5.

Department of Pulmonology and Critical Care, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey

Diagn Interv Radiol 2020; 26: 315-322
DOI: 10.5152/dir.2020.20351
Read: 1070 Downloads: 297 Published: 19 June 2020

PURPOSE

Because of the widespread use of CT in the diagnosis of COVID‑19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way.

 

METHODS

Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID‑19. The patients’ history, physical examination, CT findings, RT‑PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID‑19 or non-COVID‑19. Chest CT scans were classified according to the COVID‑19 S CT diagnosis criteria. Cohen’s kappa analysis was used.

 

RESULTS

Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID‑19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID‑19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV.

 

CONCLUSION

COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.

 

You may cite this article as: Gezer NS, Ergan B, Barış MM, et al. COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging. Diagn Interv Radiol 2020; 26:315–322.

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