The single most important risk factor for breast cancer is age, and hormonally linked reproductive and anthropometric risk factors contribute to the etiology of the disease.[1-3] There is an increase in the risk of breast cancer among women who have benign breast disease, especially those with atypical ductal or lobular hyperplasia.[4,5] Lobular carcinoma in situ (LCIS) increases risk significantly, as do a history of breast cancer in first-degree relatives and the presence of BRCA1 or 2 mutations. Surprisingly, and perhaps counterintuitively, diet, exercise, and environmental factors play a very small role in overall risk. Mammographic breast density increases relative risk fivefold among women with the highest density, and breast cancer risk is two to three times greater in women with elevated serum levels of estradiol(Drug information on estradiol) or testosterone. Multivariable risk models allow determination of composite relative risks along with cumulative lifetime risk, although improved models for African-American women are required.
As outlined in Figure 1, management of women at increased risk for breast cancer should include comprehensive quantitative risk assessment, counseling appropriate to the individual’s risk, the opportunity for genetic testing where appropriate, and a specific management prescription.[8-10] The latter should include discussion of the risks and benefits of screening, prophylactic surgery when indicated, and risk reduction using approved chemopreventive agents. Clinicians who counsel women about selective estrogen-receptor modulators (SERMs) in this context should strive to ensure that the patient makes a fully informed decision that incorporates her personal values and preferences. The counseling process should be interactive and sensitive to the patient’s educational level and cultural background.
Breast Cancer Risk Assessment
The model developed by Gail et al is an accurate method of quantifying a woman’s risk of developing breast cancer. Only six factors need to be used as significant predictors of the lifetime risk of breast cancer:
(1) Current age
(2) Age at menarche
(3) Number of breast biopsies
(4) Age at first live birth (or nulliparity)
(5) History of breast cancer in first-degree relatives
A previous diagnosis of atypical lobular or ductal hyperplasia with atypia nearly doubles the estimated risk. The model accurately estimates the 5-year probability of developing breast cancer but slightly overestimates the risk for women classified in the higher quintiles of predicted 5-year risk and underestimates the risk for those in the lower quintiles.[12,13]
The Gail model works well for women at high risk, but other models may be required for women at only slightly increased risk or for whom age is the most important determinant of their risk. Risk-prediction models for breast cancer could possibly be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. Strategies for estrogen receptor (ER)-positive breast cancer risk reduction in postmenopausal women require screening of large populations to identify those with potential benefit. Age and age at menopause are statistically significantly associated with ER-positive but not ER-negative cancers.
A simpler model that includes only age, breast cancer in first-degree relatives, and previous breast biopsy examination performs similarly for ER-positive breast cancer prediction. Importantly, postmenopausal women aged 55 years or older with either a previous breast biopsy examination or a family history of breast cancer show a 5-year breast cancer risk of 1.8% or higher, a widely accepted definition of being at “high risk” for breast cancer. This simplified model may be easier to use than the Gail model for identifying women who are at increased risk for breast cancer.