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Published on: May 19, 2023
Are Lung Imaging Reporting and Data System Categories Clear to Radiologists? A Survey of the Korean Society of
Dae Hee Han1, Jin Mo Goo2, Semin Chong3
1Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
Objective:
To evaluate possible variability in chest radiologists' interpretations of the Lung Imaging Reporting and Data System (Lung-RADS) on difficult-to-classify scenarios.
Materials And Methods:
Ten scenarios of difficult-to-classify imaginary lung nodules were prepared as an online survey that targeted Korean Society of Thoracic Radiology members. In each question, a description was provided of the size, consistency, and interval change (new or growing) of a lung nodule observed using annual repeat computed tomography, and the respondent was instructed to choose one answer from five choices: category 2, 3, 4A, or 4B, or "un-categorizable." Consensus answers were established by members of the Korean Imaging Study Group for Lung Cancer.
Results:
Of the 420 answers from 42 respondents (excluding multiple submissions), 310 (73.8%) agreed with the consensus answers; eleven (26.2%) respondents agreed with the consensus answers to six or fewer questions. Assigning the imaginary nodules to categories higher than the consensus answer was more frequent (16.0%) than assigning them to lower categories (5.5%), and the agreement rate was below 50% for two scenarios.
Conclusion:
When given difficult-to-classify scenarios, chest radiologists showed large variability in their interpretations of the Lung-RADS categories, with high frequencies of disagreement in some specific scenarios.
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