Researchers at Moffitt Cancer Center in Tampa, Florida, and colleagues at the Mayo Clinic in Rochester, Minnesota, have developed a novel computer algorithm to quantify breast density based on analysis of a screening mammogram. Increased levels of mammographic breast density have been shown in multiple studies to be correlated with elevated risk of breast cancer, but the approach to quantifying it has been limited to the laboratory setting where measurement requires highly skilled technicians. This new discovery opens the door for translation to the clinic where it can be used to identify high-risk women for tailored treatment.
“We recently developed an automated method to estimate mammographic breast density that assesses the variation in grayscale values in mammograms,” explained study lead author J. Heine, PhD, Associate Member of the Cancer Epidemiology Program and Cancer Imaging and Metabolism Department at Moffitt.
Using their new method, the researchers compared the accuracy and reliability of their measurements of variation in breast density with the performance of tests that use the degree of dense breast tissue in a mammogram to assess breast cancer risk. A study describing their novel method and its utility was published in the Journal of the National Cancer Institute.1
Potential Use in Risk Assessment
According to Dr. Heine, they found that the variation measure was a “viable, automated mammographic density measure that is consistent across film and digital imaging platforms” and “may be useful in the clinical setting for risk assessment.”
In addition, they found that the association between variation and the risk of breast cancer was strong for mammograms carried out 4 years prior to diagnosis. The automated method also made clearer distinctions between breast cancer case subjects and controls who did not have breast cancer.
The researchers concluded that the simplicity of the measure, and the ability to standardize and automate the measure across sites, could hold promise for clinicians and their patients if the measurements were incorporated into clinical risk assessment practices.
This work was supported with grants by the U.S. Department of Defense and National Cancer Institute.
Disclosure:The authors of the study reported no potential conflicts of interest. ■
Reference
1. Heine JJ, Scott CG, Sellers TA, et al: A novel automated mammographic density measure and breast cancer risk. J Natl Cancer Inst 104:1028-1037, 2012.
