Abstract
Objective
We sought to identify provider knowledge gaps and their predictors, as revealed by a breast density continuing education course marketed to the radiology community.
Methods
The course, continually available online during the study period of November 2, 2016 and December 31, 2018, includes demographics collection; a monograph on breast density, breast cancer risk, and screening; and a post-test. Four post-test questions were modified during the study period, resulting in different sample sizes pre- and postmodification. Multiple logistic regression was used to identify predictors of knowledge gaps (defined as > 25% of responses incorrect).
Results
Of 1649 analyzable registrants, 1363 (82.7%) were radiologic technologists, 226 (13.7%) were physicians, and 60 (3.6%) were other nonphysicians; over 90% of physicians and over 90% of technologists/nonphysicians specialized in radiology. Sixteen of 49 physicians (32.7%) and 80/233 (34.3%) technologists/nonphysicians mistakenly thought the Gail model should be used to determine “high-risk” status for recommending MRI or genetic testing. Ninety-nine of 226 (43.8%) physicians and 682/1423 (47.9%) technologists/nonphysicians misunderstood the inverse relationship between increasing age and lifetime breast cancer risk. Fifty-two of 166 (31.3%) physicians and 549/1151 (47.7%) technologists/nonphysicians were unaware that MRI should be recommended for women with a family history of BRCA1/BRCA2 mutations. Tomosynthesis effectiveness was overestimated, with 18/60 (30.0%) physicians and 95/272 (34.9%) technologists/nonphysicians believing sensitivity nearly equaled MRI. Knowledge gaps were more common in technologists/nonphysicians.
Conclusions
Important knowledge gaps about breast density, breast cancer risk assessment, and screening exist among radiologic technologists and radiologists. Continued education efforts may improve appropriate breast cancer screening recommendations.
Building a Better Model: A Comprehensive Breast Cancer Risk Model Incorporating Breast Density to Stratify Risk and Apply Resources
