by Pat Kramer and Alicia Di Rado

Using measurements of a woman’s breast density, as well as her family history, age and other similar factors, may improve the ability to predict a woman’s risk of developing breast cancer over today’s standard risk-assessment models.

City of Hope researchers and their colleagues from the University of Washington and the Fred Hutchinson Cancer Research Center recently reported these findings based on a sample from a study of more than 13,000 women considered at high risk for breast cancer. The researchers are investigating ways to determine women’s true risk of developing cancer so that women and their physicians can take better steps to prevent it.

Research has shown that women with certain patterns of dense breast tissue may have as much as a six-fold higher risk of developing breast cancer as other women, explained City of Hope’s Melanie Palomares, M.D., assistant professor of medical oncology and population sciences and the study’s lead author.

Breast density can be observed through mammography and may be a measure of breast cell proliferation. Researchers believe the more breast cells multiply over a lifetime, the greater the breast cancer risk.

At the same time, health professionals today rely on other noninvasive clinical tools to predict a woman’s breast cancer risk. One commonly used tool is the Gail model, which incorporates age, family history, childbearing history, previous breast biopsies and similar factors into a formula to calculate risk.

Palomares and colleagues compared risks determined using both the Gail model and breast density to understand the significance of each of the factors incorporated in the Gail model.

They found that breast tissue was significantly denser in women with a 15 percent or greater lifetime risk of breast cancer, as determined by the Gail model, compared to women with less than a 15 percent risk. For comparison, the average woman has a lifetime risk of 12.5 percent. Each factor in the Gail model corresponded to some of the risk increase associated with dense breast tissue. But a striking 7 percent of the women deemed at high risk through mammographic density could not be explained by any of the risk factors currently included in the Gail model. Other factors must be at play, Palomares said.

“We found that when the two were correlated, the Gail model could not explain all the relationships between mammographic density and breast cancer risk,” Palomares said. “This shows the importance of including mammographic density in future settings of breast cancer risk and prevention.”

This also wields implications for prevention. Many of the factors that influence breast density, such as hormone replacement therapy, diet and weight, can be changed through lifestyle choices. These factors are not part of the Gail model.

If women gain a better idea of the breast cancer risk they face, they may be more encouraged to improve the risk factors they can control, Palomares said.

In the future, more research must be done to improve how health professionals and researchers access digital mammograms and read breast density, Palomares said.

The group’s study was published in the July issue of Cancer Epidemiology Biomarkers & Prevention. Women in the study were part of the National Surgical Breast and Bowel Project Breast Cancer Prevention Trial’s site in Washington and were enrolled between 1992 and 1997. The National Cancer Institute funded the research.