For a woman in the United States, the average lifetime risk of breast cancer is about 12.3%; the 10-year risks of invasive breast cancer at ages 40, 50, and 60 years are 1.5%, 2.3%, and 3.5% respectively.
Numerous risk factors have been identified for breast cancer, although up to 60% of breast cancers occur in the absence of known risk factors.
Each individual risk factor confers only a modest relative risk increase, and most are common in the general population; therefore, combinations of risk factors are most frequently used in efforts to estimate breast cancer risk.
Several risk models attempt to use these risk factors to predict both breast cancer incidence in populations and individuals’ absolute risk.
The Gail model, developed in a population of women undergoing annual screening and including age at menarche, age at first birth, number of first-degree relatives with breast cancer, number of previous breast biopsies, and presence of atypical hyperplasia as risk factors, was one of the first.
Several limitations of the Gail model have been described, including its omission of breast density and its limited applicability in certain racial/ethnic groups and high-risk populations.
Revisions of the model include more diverse populations and breast density, which is associated with a 1.5- to 2-fold increased risk of breast cancer among women aged 40 to 50 years but raises the challenging question of whether a baseline mammogram should be obtained in all women.
Although these models help refine understanding of a woman’s absolute risk for breast cancer and can help communicate risk to women, they are more accurate in predicting incidence in population subgroups and far less useful in identifying which individual women will or will not get cancer.
Despite its limitations, the Gail model has been validated in 3 large populations and, as the basis for the National Cancer Institute’s online Breast Cancer Risk Assessment Tool is commonly used in clinical practice.
Several decision analysis models have attempted to estimate how individual risk profiles influence the benefits and harms of screening.
Older age and other factors that increase breast cancer risk also increase the absolute breast cancer mortality benefit with mammography.
The risk of false-positive results also generally increases with certain individual characteristics such as breast density.
Older age and more comorbidity increase the risk of overdiagnosis because of decreasing life expectancy, as do characteristics of the cancer itself ( aggressive tumors are less likely overdiagnosed than indolent tumors because of shorter lead time ).
A comparative study of 4 microsimulation models found that for women aged 40 to 49 years with a Gail-model breast cancer risk twice average, biennial mammography screening yielded the same ratio of benefits and harms as biennial screening for women 50 years or older at average risk.
Similarly, a cost-utility model found that biennial screening among women aged 40 to 49 years with high breast density and either a first-degree relative with breast cancer or a history of a breast biopsy had similar ratios of benefits to harms as biennial screening of women in their 50s without those risk factors.
Of note, however, none of these models considered overdiagnosis in their main analysis.
If a healthy 40-year-old woman had twice the average risk of breast cancer because of dense breasts, she would be expected to have twice the absolute benefit of annual screening ( eg, 10 lives saved per 10 000 instead of 5 ). She would, however, also have a higher risk of false-positive findings. ( Xagena )
Lydia E. Pace LE, Nancy L. Keating NL, JAMA 2014;311:1327-1335