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JNCI Journal of the National Cancer Institute 2006 98(17):1204-1214; doi:10.1093/jnci/djj331
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© The Author 2006. Published by Oxford University Press.

ARTICLE

Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography

William E. Barlow, Emily White, Rachel Ballard-Barbash, Pamela M. Vacek, Linda Titus-Ernstoff, Patricia A. Carney, Jeffrey A. Tice, Diana S. M. Buist, Berta M. Geller, Robert Rosenberg, Bonnie C. Yankaskas, Karla Kerlikowske

Affiliations of authors: Cancer Research and Biostatistics, Seattle, WA (WEB); School of Public Health, University of Washington, Seattle, WA (WEB, EW, DSMB); Center for Health Studies, Group Health Cooperative, Seattle, WA (WEB, DSMB); Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD (RBB); Department of Medical Biostatistics, University of Vermont College of Medicine, Vermont Cancer Center, Burlington, VT (PMV); Norris Cotton Cancer Center and Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH (LTE, PAC); Department of Family Medicine, Oregon Health and Science University, Portland, OR (PAC); Department of Medicine, University of California, San Francisco, CA (JAT, KK); Office of Health Promotion Research, College of Medicine, University of Vermont, Burlington, VT (BMG); Department of Radiology, University of New Mexico, Health Sciences Center, Albuquerque, NM (RR); Department of Radiology, University of North Carolina, Chapel Hill, NC (BCY); Department of Veterans Affairs and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA (KK)

Correspondence to: William E. Barlow, PhD, Cancer Research and Biostatistics, 1730 Minor Avenue, Suite 1900, Seattle, WA 98101 (e-mail: williamb{at}crab.org).

Background: Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography. Methods: There were 2 392 998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11 638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided. Results: Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women. Conclusion: Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.



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Editorial about this Article

Assessing Breast Cancer Risk: Evolution of the Gail Model
Melissa L. Bondy and Lisa A. Newman
J Natl Cancer Inst 2006 98: 1172-1173. [Extract] [Full Text] [PDF]



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