Dataset Information

Are Lung Imaging Reporting and Data System Categories Clear to Radiologists? A Survey of the Korean Society of Thoracic Radiology Members on Ten Difficult-to-Classify Scenarios.

ABSTRACT:

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.

SUBMITTER: Han DH

PROVIDER: S-EPMC5313529 | biostudies-literature | 2017 Mar-Apr

REPOSITORIES: biostudies-literature

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Publications

Are Lung Imaging Reporting and Data System Categories Clear to Radiologists? A Survey of the Korean Society of Thoracic Radiology Members on Ten Difficult-to-Classify Scenarios.

Han Dae Hee DH Goo Jin Mo JM Chong Semin S Ahn Myeong Im MI

Korean journal of radiology 20170207 2

<h4>Objective</h4>To evaluate possible variability in chest radiologists' interpretations of the Lung Imaging Reporting and Data System (Lung-RADS) on difficult-to-classify scenarios.<h4>Materials and methods</h4>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 observ ...[more]

PMID: 28246521

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Project description:ObjectivesRadiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond.MethodsBetween April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression.ResultsThe survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24-74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10-2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20-0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21-0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25-31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16-50.54, p < 0.001).ConclusionsLimited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption.Key points• Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

Dataset Information

Are Lung Imaging Reporting and Data System Categories Clear to Radiologists? A Survey of the Korean Society of Thoracic Radiology Members on Ten Difficult-to-Classify Scenarios.

Objective

Materials and methods

Results

Conclusion

Publications

Are Lung Imaging Reporting and Data System Categories Clear to Radiologists? A Survey of the Korean Society of Thoracic Radiology Members on Ten Difficult-to-Classify Scenarios.

Similar Datasets

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