Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model
AuthorsSanten, Richard J
Boyd, Norman F
Chlebowski, Rowan T
Forbes, John F
Hankinson, Susan E
AffiliationDepartment of Internal Medicine/Endocrinology, University of Virginia Health System, Box 801416, Charlottesville, Virginia 22908, USA.
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AbstractThe majority of candidates for breast cancer prevention have not accepted tamoxifen because of the perception of an unfavorable risk/benefit ratio and the acceptance of raloxifene remains to be determined. One means of improving this ratio is to identify women at very high risk of breast cancer. Family history, age, atypia in a benign biopsy, and reproductive factors are the main parameters currently used to determine risk. The most powerful risk factor, mammographic density, is not presently employed routinely. Other potentially important factors are plasma estrogen and androgen levels, bone density, weight gain, age of menopause, and fracture history, which are also not currently used in a comprehensive risk prediction model because of lack of prospective validation. The Breast Cancer Prevention Collaborative Group (BCPCG) met to critically examine and prioritize risk factors that might be selected for further testing by multivariate analysis using existing clinical material. The BCPCG reached a consensus that quantitative breast density, state of the art plasma estrogen and androgen measurements, history of fracture and height loss, BMI, and waist-hip ratio had sufficient priority for further testing. As a practical approach, these parameters could be added to the existing Tyrer-Cuzick model which encompasses factors included in both the Claus and Gail models. The BCPCG analyzed potentially available clinical material from previous prospective studies and determined that a large case/control study to evaluate these new factors might be feasible at this time.
CitationCritical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. 2007, 14 (2):169-87 Endocr. Relat. Cancer