BACKGROUND: The Gail et al. model is considered the best available means for estimating an individual woman's risk of developing breast cancer. Such estimates are useful in decision making on the part of women, in designing prevention trials, and in targeting screening and prevention efforts. PURPOSE: Our purpose was to evaluate the ability of the model to accurately predict individual breast cancer risk, using a large population independent of the one from which the model was derived. METHODS: We compared the number of cancer cases predicted by the model to the actual number of cases observed in the Nurses' Health Study. The study population was 115,172 women who did not have breast cancer at the beginning of the study. Questionnaires were sent to participants every 2 years, seeking data on risk factors and diagnoses of breast cancer. Follow-up compliance was 95% over the 12-year study period. RESULTS: The model over-predicted absolute breast cancer risk by 33% (95% confidence interval [CI] = 28%-39%), with the overprediction more than twofold among premenopausal women (95% CI = 1.9-2.2), among women with extensive family history of breast cancer (95% CI = 1.1-3.9), and among women with age at first birth younger than 20 years (95% CI = 1.3-4.7). The correlation coefficient between observed and predicted risk was 0.67, indicating that the model is less than satisfactory for ranking individual levels of breast cancer risk. Overprediction occurred at all deciles of predicted risk. CONCLUSIONS: The model's performance is unsatisfactory for estimating breast cancer risk for individual women aged 25-61 years who do not participate in annual screening. Lower mammography screening rates in the Nurses' Health Study may account for some, but not all, of the discrepancy between observed and predicted cases. IMPLICATIONS: A recent modification of the model by the tamoxifen trial investigators is likely to have provided accurate power calculations. This modified form of the model should be useful for planning other large, population-based studies.
BACKGROUND: The Gail et al. model is considered the best available means for estimating an individual woman's risk of developing breast cancer. Such estimates are useful in decision making on the part of women, in designing prevention trials, and in targeting screening and prevention efforts. PURPOSE: Our purpose was to evaluate the ability of the model to accurately predict individual breast cancer risk, using a large population independent of the one from which the model was derived. METHODS: We compared the number of cancer cases predicted by the model to the actual number of cases observed in the Nurses' Health Study. The study population was 115,172 women who did not have breast cancer at the beginning of the study. Questionnaires were sent to participants every 2 years, seeking data on risk factors and diagnoses of breast cancer. Follow-up compliance was 95% over the 12-year study period. RESULTS: The model over-predicted absolute breast cancer risk by 33% (95% confidence interval [CI] = 28%-39%), with the overprediction more than twofold among premenopausal women (95% CI = 1.9-2.2), among women with extensive family history of breast cancer (95% CI = 1.1-3.9), and among women with age at first birth younger than 20 years (95% CI = 1.3-4.7). The correlation coefficient between observed and predicted risk was 0.67, indicating that the model is less than satisfactory for ranking individual levels of breast cancer risk. Overprediction occurred at all deciles of predicted risk. CONCLUSIONS: The model's performance is unsatisfactory for estimating breast cancer risk for individual women aged 25-61 years who do not participate in annual screening. Lower mammography screening rates in the Nurses' Health Study may account for some, but not all, of the discrepancy between observed and predicted cases. IMPLICATIONS: A recent modification of the model by the tamoxifen trial investigators is likely to have provided accurate power calculations. This modified form of the model should be useful for planning other large, population-based studies.
Authors: Matthew P Banegas; Mitchell H Gail; Andrea LaCroix; Beti Thompson; Maria Elena Martinez; Jean Wactawski-Wende; Esther M John; F Allan Hubbell; Shagufta Yasmeen; Hormuzd A Katki Journal: Breast Cancer Res Treat Date: 2011-12-07 Impact factor: 4.872
Authors: Min Yi; Funda Meric-Bernstam; Lavinia P Middleton; Banu K Arun; Isabelle Bedrosian; Gildy V Babiera; Rosa F Hwang; Henry M Kuerer; Wei Yang; Kelly K Hunt Journal: Cancer Date: 2009-03-01 Impact factor: 6.860
Authors: Lucile L Adams-Campbell; Kepher H Makambi; Wayne A I Frederick; Melvin Gaskins; Robert L Dewitty; Worta McCaskill-Stevens Journal: Breast J Date: 2009 Sep-Oct Impact factor: 2.431
Authors: V Shane Pankratz; Lynn C Hartmann; Amy C Degnim; Robert A Vierkant; Karthik Ghosh; Celine M Vachon; Marlene H Frost; Shaun D Maloney; Carol Reynolds; Judy C Boughey Journal: J Clin Oncol Date: 2008-10-14 Impact factor: 44.544