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Updated: Dec 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Interobserver and Intraobserver Variability in the CT Assessment of COVID-19 Based on RSNA Consensus Classification
Mohamad O Hadied1, Parth Y Patel1, Peter Cormier1
1Department of Radiology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202.
Purpose:
To assess the interobserver and intraobserver agreement of fellowship trained chest radiologists, nonchest fellowship-trained radiologists, and fifth-year radiology residents for COVID-19-related imaging findings based on the consensus statement released by the Radiological Society of North America (RSNA).
Methods:
A survey of 70 chest CTs of polymerase chain reaction (PCR)-confirmed COVID-19 positive and COVID-19 negative patients was distributed to three groups of participating radiologists: five fellowship-trained chest radiologists, five nonchest fellowship-trained radiologists, and five fifth-year radiology residents. The survey asked participants to broadly classify the findings of each chest CT into one of the four RSNA COVID-19 imaging categories, then select which imaging features led to their categorization. A 1-week washout period followed by a second survey comprised of randomly selected exams from the initial survey was given to the participating radiologists.
Results:
There was moderate overall interobserver agreement in each group (κ coefficient range 0.45-0.52 ± 0.02). There was substantial overall intraobserver agreement across the chest and nonchest groups (κ coefficient range 0.61-0.67 ± 0.06) and moderate overall intraobserver agreement within the resident group (κ coefficient 0.58 ± 0.06). For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that ranged from fair to perfect kappa values. When assessing agreement with PCR-confirmed COVID status as the key, we observed moderate overall agreement within each group.
Conclusion:
Our results support the reliability of the RSNA consensus classification system for COVID-19-related image findings.
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