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."
