Variation in breast density is more reliable, accurate for assessing breast cancer risk
A novel computer algorithm has been developed that can easily quantify breast density, a major risk factor for breast cancer, based on analysis of a screening mammogram. Increased levels of mammographic breast density have been correlated with an 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.
Mammographic breast density, or the proportion of fibroglandular tissue pictured on the mammogram, is an established risk factor for breast cancer. Using this new method, researchers compared the accuracy and reliability of measuring breast density variation with tests that assess breast cancer risk based on the proportion of dense breast tissue seen on a mammogram.
Lead study author J. Heine, PhD, of the Moffitt Cancer Center in Tampa, Florida, stated that they found the variation measure was “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, the research team found the association between variation and risk of breast cancer was strong in mammograms obtained 4 years prior to diagnosis. The automated method also made clearer distinctions between breast cancer case subjects and controls that did not have breast cancer.
Many clinicians use the risk predictive value of percent of breast density seen on the mammogram as the amount or proportion of bright tissue in an image; however, Heine and colleagues found that the variation of dense tissue is also relevant to breast cancer, suggesting a relationship between percent of breast density and variation in breast density.
“The strengths of this study include the evaluation and validation of a novel breast density measure across three well-designed epidemiologic studies,” said study coauthor Thomas A. Sellers, PhD, MPH, also of Moffitt. “Because we were able to compare this novel breast density measure with an established percent density measure that was available 4 years before diagnosis, we were allowed to show that variation was present for at least 4 years, and in some cases, more than 8 years. Offering clinicians and patients the advantage of more timely, reliable, and accurate risk could open the door for interventions to lower risk and, hopefully, prevent the disease from occurring.”
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 study was published in the Journal of the National Cancer Institute (2013; doi:10.1093/jnci/djs254).