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Agreement between emergency medicine residents and radiologists in RSNA-Based chest CT interpretation
Senol Ardic1, Recep Dede2, Ozgen Gonenc Cekic3
1Department of Emergency Medicine, Faculty of Medicine, Trabzon University, Topal Osman Street, Kasüstü, 61290 Yomra, Trabzon, Türkiye. senolardic@yahoo.com.
Background:
Standardized interpretation of chest computed tomography (CT) is essential for clinical decision-making in patients presenting to the emergency department with suspected coronavirus disease 2019 (COVID-19).
Objectives:
This study evaluated interobserver agreement between emergency medicine residents (EMRs) and radiologists using the Radiological Society of North America (RSNA) classification and examined the effect of training level.
Methods:
This retrospective single-center study included 1,104 patients with suspected COVID-19 who underwent chest CT between December 1, 2020, and January 1, 2021. CT findings were classified as typical, indeterminate, atypical, or negative for pneumonia according to RSNA criteria. A total of 23 EMRs independently evaluated 48 CT examinations each. Resident interpretations were compared with routine radiology reports. Interobserver agreement among radiologists was additionally assessed by independent re-evaluation of 100 CT scans. Agreement was measured using Cohen's kappa (κ) coefficient. Diagnostic performance of the typical category was assessed using real time polymerase chain reaction (RT-PCR) as the reference standard.
Results:
The mean patient age was 59.2 years, and 50.9% were male. RT-PCR positivity was 79.1%. Overall agreement between EMRs and radiologists was good (κ = 0.705; 95% CI, 0.671-0.740; p < 0.001). Agreement was very good for typical (κ = 0.827; 95% CI, 0.793-0.859) and negative (κ = 0.804; 95% CI, 0.765-0.847) categories, but lower for indeterminate (κ = 0.340; 95% CI, 0.253-0.428) and atypical (κ = 0.341; 95% CI, 0.216-0.456) categories. Agreement among radiologists was also good (κ = 0.730; 95% CI, 0.610-0.840; p < 0.001). Agreement improved with increasing training level.
Conclusion:
RSNA-based chest CT classification shows good overall agreement. Lower agreement in indeterminate and atypical patterns highlights the need for targeted training and closer radiology collaboration.
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