Automated and clinical breast density evaluation methods are equally accurate in predicting a patient’s risk of breast cancer, according to a new study published in Annals of Internal Medicine.
The authors examined data from more than 1,600 women with screen-detected cancer, more than 300 women with interval invasive cancer and more than 4,000 control participants. Women with an automated assessment of extremely dense breasts had a 5.65 times higher risk of interval cancer than women with scattered fibroglandular densities. In addition, they had a 1.43 times higher risk of screen-detected cancer than women with scattered fibroglandular densities.
The ability of advanced algorithms and trained radiologists to evaluate breast density when looking at digital mammography images was compared. The two methods had “similar discriminatory accuracy” for all breast types.
“These findings demonstrate that breast-density evaluation can be done with equal accuracy by either a radiologist or an automated system,” lead author Karla Kerlikowske, MD, a professor of medicine at the University of California, San Francisco (UCSF), said in a news report from UCSF. “They also show the potential value of a reproducible automated evaluation in helping identify women with dense breasts who are at higher risk of aggressive tumors, and thus more likely to be candidates for supplemental screening.”
In the same news report, Kerlikowske explained some of the potential advantages of automated breast density assessments.
“There have been concerns raised about the reliability of BI-RADS breast density measures, since an assessment might vary for an individual woman depending on the radiologist and the mammogram,” she said. “Automated assessments, which are done by computer algorithm, are more reproducible and less subjective. Therefore, they could reduce variation and alleviate the sense of subjectivity and inconsistency.”
Michael has more than 16 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.