While digital breast tomosynthesis is reportedly more effective at detecting cancer, new research suggests a higher recall rate due to fatigue and less experience in interpreting the images.
What kind of impact do fatigue, and experience level have in the assessment of digital breast tomosynthesis (DBT) images?
Recently published research in Radiology demonstrated that experience matters when it comes to interpreting DBT images as does the time of the day when they are interpreted.
In the retrospective study involving over 97,600 mammograms interpreted by 18 radiologists over a nearly two-year period, researchers assessed recall rates and false-positive rates for DBT imaging and digital mammography (DM). For radiologists with five years or less of post-training experience, recall rates for the use of DBT increased 11.5 percent with every additional hour in the day but researchers did not see this for patients who had DM. The study authors also found no increase in recall rates for either DBT or DM among radiologists with more than five years of post-training experience.
However, they also acknowledged considerably more detail with DBT, noting “hundreds of images per bilateral screening DBT examination, as compared with four images per bilateral screening DM examination.”
“(Due to) the higher number of images to review, DBT may require greater cognitive resources than DM, which could exacerbate the association between fatigue and radiologist performance,” wrote Ana P. Lourenco, M.D., a professor of diagnostic imaging at the Warren Alpert Medical School of Brown University, and colleagues.
The study authors noted that other researchers had found that DBT had an increased cancer detection rate and between a 15 to 40 percent reduction in recall rates. In the new study, the authors also found that patients in the DM group had a higher false-positive rate in comparison to those who had DBT (9.8 percent vs. 8.6 percent) and a higher overall recall rate (10.2 percent vs. 9.0 percent).
“Although DBT achieves a superior (true positive) rate, more junior radiologists appeared to compensate for their fatigue later in the day when using DBT by recalling a broader range of mammograms, more of which were (false positive) findings,” wrote Dr. Lourenco and colleagues.
The study authors noted limitations of the study, including a small sample size of radiologists as well as a lack of randomization for the imaging technology, patients, and radiologists. They also acknowledged that using the 5-year mark to distinguish experience is a rough approximation that may not account for work volume or the varied effect of experience for each person.
Mammography Study Suggests DBT-Based AI May Help Reduce Disparities with Breast Cancer Screening
December 13th 2024New research suggests that AI-powered assessment of digital breast tomosynthesis (DBT) for short-term breast cancer risk may help address racial disparities with detection and shortcomings of traditional mammography in women with dense breasts.
FDA Clears Updated AI Platform for Digital Breast Tomosynthesis
November 12th 2024Employing advanced deep learning convolutional neural networks, ProFound Detection Version 4.0 reportedly offers a 50 percent improvement in detecting cancer in dense breasts in comparison to the previous version of the software.