The review says risk reduction strategies for women at average risk of breast cancer should focus primarily on lifestyle factors. Among the recommendations: aside from following general dietary recommendations for healthy eating, there is no clear evidence that specific dietary components can effectively reduce breast cancer risk; while all women should be advised to moderate alcohol use, women at increased risk of breast cancer should moderate alcohol intake or even avoid alcohol; women should maintain a healthy body weight, since gaining over 20 pounds during adulthood has been reported to result in an increased risk of breast cancer.

The authors say use of pharmacotherapy to reduce the risk should be individualized to each patient after a thorough discussion of risks and benefits as part of a shared decision-making process.

"While decreases in both breast cancer incidence and breast cancer mortality have been apparent in recent years, the societal and economic impact of this malignancy continues to be huge," write the authors. "Although a constellation of breast cancer risk factors has been identified, many of these are not easily modified. Further, many women worry about the potential impact of a breast cancer diagnosis on themselves and their families. As a result, interest in strategies to prevent breast cancer remains strong."

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Using these and other data, researchers also calculated the model's performance for individuals using the concordance statistic (c-statistic), which reflects how closely the actual timing of breast cancer events aligned with model predictions. A c-statistic of 0.5 is observed if the predictions are no better than random chance; a c-statistic of 1.0 is observed if the predictions are perfectly concordant with the actual outcomes. In this study, the c-statistic was 0.5, reflecting that the Gail model worked no better than a coin flip in predicting which of the women with atypia would develop invasive breast cancer.

When assessed across other groups of women without respect to the presence of atypia, the Gail model typically performs better. In that setting, it has been shown to predict approximately the same number of breast cancers that later occur.

Lynn Hartmann, M.D., Mayo Clinic oncologist and co-investigator on the study, says that there is strong interest in predicting breast cancer risk. For example, the Gail model, posted on the National Cancer Institute's Web site (cancer/bcrisktool/), attracts 25,000 viewers each month.

"Doctors counsel women at high risk to have more frequent or intensive surveillance or to consider chemoprevention strategies such as tamoxifen or raloxifene," says Dr. Hartmann. "When making such decisions, women and their physicians must have highly reliable risk estimates."

Researchers are pursuing other avenues to better predict individual risk. Previously, Mayo Clinic researchers found that women with multiple sites of cellular atypia in a breast biopsy have significantly increased risk of developing breast cancer. In a study published earlier this year, Mayo researchers found that women whose atypia tissues express COX-2 enzymes were more likely to develop breast cancer, and the higher the COX-2 levels, the higher the risk.

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