Moffitt Researchers Develop A Novel Automated Measure Of Breast Density
Researchers at Moffitt Cancer Center and colleagues at the Mayo Clinic in Rochester, Minn., have developed a novel computer algorithm to easily quantify a major risk factor for breast cancer based on analysis of a screening mammogram. Multiple studies have shown increased levels of mammographic breast density 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 personalized care.
Mammographic breast density, or the proportion of fibroglandular tissue pictured on the mammogram, is an established risk factor for breast cancer. Women with high mammographic breast density have a greater risk of developing breast cancer. However, mammographic breast density has not been used in clinical settings for risk assessments due in large part to the lack of an automated and standardized measurement.
“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, Ph.D.
, associate member of the Cancer Epidemiology Program
and Cancer Imaging & Metabolism Department
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 July 4, 2012, issue of the Journal of the National Cancer Institute
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.”
Additionally, they found that the association between variation and the risk of br