A recent study from the University of Virginia (UVA) Cancer Center found that adding an automated measure of breast density more accurately predicts a woman’s risk of getting breast cancer. The findings could help pave the way for customized risk models that better indicate the ideal frequency for women to obtain mammograms.
“Our aim was to develop a breast cancer risk model that includes a measurement of breast density with other known risk factors to improve risk prediction and give women personalized knowledge to make decisions about screening and their breast health,” said Jennifer Hardy, MD, professor of radiology at the UVA School of Medicine and lead author of the study.
“Volumetric Breast Density Improves Breast Cancer Risk Prediction,” was presented during the 2014 San Antonio Breast Cancer Symposium. The study was authored by Harvey, George Stukenborg, Wendy Cohn, Kathy Repich, Olivier Alonzo, Wendy Novicoff, Martin Yaffe, and William Knaus and performed in collaboration with Sunnybrook Health Sciences Centre in Toronto and the NorthShore University HealthSystem Research Institute in Evanston, Ill.
The study examined the connection between risk factors and breast cancer diagnosis in more than 3,400 women who received digital mammograms at UVA between 2003 and 2013, including patients diagnosed with breast cancer and those who did not get cancer. Researchers used an automated software program to calculate breast density for each woman. Additional self-predicted risk factors were obtained through an online questionnaire.
“There is increasing interest in implementing personalized breast cancer screening strategies instead of guidelines based on a woman’s age. However, most risk models do not include breast density, which is an important indicator of a woman’s breast cancer risk,” Harvey said. In the UVA study, breast density emerged as one of the top five predictors of breast cancer risk and was shown to significantly improve the accuracy of breast cancer risk models.