from screening before the age of 40 or from twice-yearly screening, though a small minority of women might benefit.

Finding techniques that permit such classification will demand a better and more precise understanding of risk factors. To date, the most significant risk factors are age and gender. The widely used “Gail model” identified five risk factors: age, age at menarche, age at first live birth, number of prior breast biopsies, and the number of first degree relatives with breast cancer. Based on data from the Breast Cancer Detection Demonstration Project conducted in the 1970s and involving 200,000 women, the model has proven highly accurate at predicting the numbers of women within various age and risk groups who will develop cancer within the next five years, but it is only moderately accurate at predicting which individual women will develop the disease.

Another limitation of the Gail model is that it does not include genetic risk factors. Risk assessments for women with BRCA genetic mutations have been developed from retrospective analyses of risks in the relatives of carriers from high-risk families. The accuracy of these analyses has been questioned as population-based studies indicate the risk may be substantially lower. Also risk assessments for carriers have not taken into account the other risk factors used in the Gail model.

The committee believes that individual screening strategies are crucial to improving the early detection of breast cancer and that accurate risk assessment is an essential step toward the eventual development of individualized screening strategies.


Researchers and technology developers should focus their efforts on developing tools to identify those women who would benefit most from breast cancer screening. Such tools should be based on individually tailored risk prediction techniques that integrate biologic and other risk factors. (Recommendation B1)

The combination of established risk factors with more comprehensive genetic risk profiles will require the development of mathematical models to relate genetic predictors, biological expression, natural course of disease, and responses to treatment in order to:

  • Elucidate the natural course of disease progression and identify disease subgroups with distinctive risk profiles and treatment susceptibilities;

  • Identify aspects of the models where further research and data collection are needed;

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