Two new models for assessing patients' risk of developing breast cancer focus on breast density as an important predictor, two studies report in the September 6 issue of the Journal of the National Cancer Institute.
A breast cancer risk model called the Gail model was developed in the 1980s to assess the risk of breast cancer for women who undergo annual mammography screening, and several groups the Breast Cancer Surveillance Consortium (BCSC) and the Breast Cancer Detection Demonstration Project (BCDDP), among others have made attempts to update and expand it.
The first study in JNCI presents a new model that adds breast density as a factor predicting a patient's risk of developing breast cancer. A second study by the developer of the original Gail model updates that model to take breast density into account.
In the first study, William E. Barlow, Ph.D., of Cancer Research and Biostatistics in Seattle, and colleagues identified 11,638 women diagnosed with breast cancer in a large prospective risk study. They constructed models to predict breast cancer risk for pre- and postmenopausal women.
The authors found that several factors influenced breast cancer diagnosis in premenopausal women, including age, breast density, family history of breast cancer, and prior breast procedure. For postmenopausal women, risk factors included ethnicity, body mass index, natural menopause, use of hormone therapy, a prior false positive mammogram, and the risk factors in premenopausal women. They write that their model may identify women at high-risk for breast cancer more accurately than the Gail model.
The models establish breast density as a highly clinically significant predictor of breast cancer risk that is almost as powerful a risk factor as age. "Nonetheless, [the] ability to accurately predict breast cancer at the individual level remains limited," Barlow and colleagues write.