WEDNESDAY, Sept. 7 (HealthDay News) -- Two new studies suggest breast density is nearly as important as age in predicting who is going to develop breast cancer.

The information may help better identify women at high risk for the disease, the researchers noted.

"After age, it's probably the most important factor," said William E. Barlow, lead author of one of the studies and a senior investigator at Group Health Cooperative in Seattle. "If we wanted to identify women who were really at high risk for chemoprevention efforts or more intense screening surveillance, then any model that incorporates breast density is going to be better at picking out those women."

Both studies are in the Sept. 6 issue of the Journal of the National Cancer Institute.

Since the late 1980s, medical professionals have relied on the Gail model to assess breast cancer risk in women undergoing annual mammography. That model uses risk factors known at the time, such as current age, age at first menstrual period, age at birth of first child, number of first-degree relatives with a family history of breast cancer and number of previous breast biopsies. More recently, race and atypical hyperplasia were added to the model.

Experts had speculated that adding newly identified risk factors for breast cancer such as breast density and use of hormone therapy might improve the test's predictive powers.

Barlow and his colleagues looked at 11,638 women who had developed breast cancer, out of a larger group of about 1 million.

Among premenopausal women, age, breast density, family history of breast cancer and a previous breast procedure were significant risk factors for developing breast cancer. Having any type of prior breast procedure was associated with about a 50 percent increased risk. Women with extremely dense breasts had about a fourfold greater risk than women whose breasts were not dense.

For postmenopausal women, factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body-mass index, natural menopause, hormone therapy and a prior false-positive mammogram.

The model may perform better than the Gail model, although the accuracy was far from perfect. This suggests that the major determinants of breast cancer are still unknown.

A second study, conducted at the National Cancer Institute, used an updated version of the Gail model to assess the absolute risk of developing breast cancer. This model also included breast density, along with weight, age at first live birth, number of previous benign biopsies and number of first-degree relatives with breast cancer.

Again, this model predicted that women with high breast density had an increased risk of breast cancer.

It's unclear if breast density can be considered a modifiable risk factor.

"It may be modifiable, but we don't know that for sure," Barlow said. "It is related to hormone use in women. Their breasts can be denser during the time they're on hormone replacement therapy."

It's also not clear exactly how this new information will be incorporated into practice. Breast density generally needs to be measured by a radiologist. "It's not something that a woman can judge for herself," Barlow explained. "There really isn't a feedback mechanism from the radiologist back to the woman to say what the breast density is."

In the future, however, Barlow envisions mammography facilities becoming more like risk-counseling facilities that incorporate breast density along with other risk factors and past mammogram results. "But that would require an evolution of mammography centers," he noted.

Even in the more immediate present, the findings reinforce the notion of taking steps to prevent breast cancer in high-risk and other women.

"We as a medical community still have not accepted the paradigm that we can identify women who are at a high risk for developing breast cancer," said Dr. Jay Brooks, chairman of hematology/oncology at Ochsner Health System in Baton Rouge, La. "We could intervene with a treatment to reduce their risk."

"We don't use the tools we already have to identify women at a high risk for breast cancer and offer them potential treatment to reduce their risk like we do for cholesterol and heart disease," he continued. "Now, we're further defining the model that will predict even better who could potentially benefit from these tools."

More information

For more on breast cancer, visit the American Cancer Society.