New Test Assesses Individual Breast Cancer Risk
Oct. 6 -- MONDAY, Oct. 5 (HealthDay News) -- Analyzing individual breast tissue for specific structural characteristics may more precisely determine a woman's risk for developing breast cancer.
In the Oct. 5 online issue of the Journal of Clinical Oncology, researchers report that the more acini a woman has -- these are the sacs that produce milk -- and the larger her breast lobules, the higher the chance she will get breast cancer.
"A tremendous number of women get breast biopsies from abnormal mammograms. They number 1 to 2 million people a year in the U.S. alone," said study co-author Derek Radisky, an assistant professor of biochemistry at the Mayo Clinic in Jacksonville, Fla. "Of these, about one-quarter have positive findings of cancer while the rest could immediately benefit from this kind of assessment."
"We have recognized for a long time that the cancer risk-assessment models we have are not perfect, particularly for individual patients, so certainly this is an approach that's very interesting. But it's not going to change anything for a woman who walks in the clinic for the next year," said Dr. Angela Bradbury, director of the Margaret Dyson Family Risk Assessment Program at Fox Chase Cancer Center in Philadelphia. "But it's exactly this type of paper, this type of research that, two to three years down the road, may actually play out in the clinic. Anything that improves on our risk assessment will be useful."
Currently, factors such as family history of breast cancer, number of pregnancies and age at first pregnancy are helpful in predicting how often breast cancer will arise in a larger population.
But these same tools are poor indicators of individual risk.
"They're terrible on an individual basis, so there's no individualized ability to say whether a person is likely to get cancer or not," Radisky said.
Other than family history and genetics, the best tool experts have to predict individual breast cancer risk is the Gail model, which takes into account age and number of previous biopsies, as well as family history and pregnancy history.
