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Purpose: To develop novel monitoring methods in Blinded Independent Central Review (BICR) imaging trials in which two radiologist reviewers assess the same images. In this project we aimed to ‘flag’ any reviewer that might have an assessment bias compared to the assessments of other reviewers on a specific study. Methods: Retrospective data analysis using R programming scripts was used to evaluate discordant assessments between two reviewers. We use a binomial test to determine the probability that an estimated low adjudication agreement rate is statistically less than the expected rate for all reviewer discordant assessment pairs. Results: We determined that for five or more discordant cases we can calculate the probability that each individual reviewer might have a statistically significant probability of low adjudication agreement for each discordant pair of assessments. We then analyzed the assessment data for sixteen oncological BICR clinical trials. Conclusions: The basic methods described can ‘flag’ or ‘signal’ a potential assessment ‘bias’. Although we initially focused on studies following one published clinical trial criteria to evaluate solid tumor we have applied the methods to other oncological studies with different published criteria which also may require double radiological reviews.
J. Michael O'Connor,Manish Sharma, andAnitha Singareddy
"Development of methods to evaluate probability of reviewer’s assessment bias in Blinded Independent Central Review (BICR) imaging studies", Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520P (4 March 2019); https://doi.org/10.1117/12.2512603
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J. Michael O'Connor, Manish Sharma, Anitha Singareddy, "Development of methods to evaluate probability of reviewer’s assessment bias in Blinded Independent Central Review (BICR) imaging studies," Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520P (4 March 2019); https://doi.org/10.1117/12.2512603