Mayo Clinic researchers tested the Gail model in 331 women with atypia who had benign breast biopsies at Mayo Clinic between 1967 and 1991. Of these women, 58 developed cancer during an average of 13.7 years of follow-up. In contrast, the model predicted that 34.9 women would develop breast cancer in that period.
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 (http://www.cancer.gov/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 si
|Contact: Karl Oestreich|