© The Author 2006. Published by Oxford University Press.
ARTICLE |
Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography
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.
CiteULike 
Connotea 
Del.icio.us What's this?
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]
This article has been cited by other articles:
|
|
 |
|
  E. S. Burnside, J. Davis, J. Chhatwal, O. Alagoz, M. J. Lindstrom, B. M. Geller, B. Littenberg, K. A. Shaffer, C. E. Kahn Jr, and C. D. Page Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings Radiology, June 1, 2009; 251(3): 663 - 672. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. McTiernan, C.Y. Wang, B. Sorensen, L. Xiao, D. S.M. Buist, E. J. Aiello Bowles, E. White, M. A. Rossing, J. Potter, and N. Urban No Effect of Aspirin on Mammographic Density in a Randomized Controlled Clinical Trial Cancer Epidemiol. Biomarkers Prev., May 1, 2009; 18(5): 1524 - 1530. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Chhatwal, O. Alagoz, M. J. Lindstrom, C. E. Kahn Jr., K. A. Shaffer, and E. S. Burnside A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis Am. J. Roentgenol., April 1, 2009; 192(4): 1117 - 1127. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. R. Cummings, J. A. Tice, S. Bauer, W. S. Browner, J. Cuzick, E. Ziv, V. Vogel, J. Shepherd, C. Vachon, R. Smith-Bindman, et al. Prevention of Breast Cancer in Postmenopausal Women: Approaches to Estimating and Reducing Risk J Natl Cancer Inst, March 18, 2009; 101(6): 384 - 398. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Manduca, M. J. Carston, J. J. Heine, C. G. Scott, V. S. Pankratz, K. R. Brandt, T. A. Sellers, C. M. Vachon, and J. R. Cerhan Texture Features from Mammographic Images and Risk of Breast Cancer Cancer Epidemiol. Biomarkers Prev., March 1, 2009; 18(3): 837 - 845. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. T. Zon, E. Goss, V. G. Vogel, R. T. Chlebowski, I. Jatoi, M. E. Robson, D. S. Wollins, J. E. Garber, P. Brown, and B. S. Kramer American Society of Clinical Oncology Policy Statement: The Role of the Oncologist in Cancer Prevention and Risk Assessment J. Clin. Oncol., February 20, 2009; 27(6): 986 - 993. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Park, A. N. Freedman, M. H. Gail, D. Pee, A. Hollenbeck, A. Schatzkin, and R. M. Pfeiffer Validation of a Colorectal Cancer Risk Prediction Model Among White Patients Age 50 Years and Older J. Clin. Oncol., February 10, 2009; 27(5): 694 - 698. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. R. Perez-Lopez, P. Chedraui, and J. Haya Review Article: Vitamin D Acquisition and Breast Cancer Risk Reproductive Sciences, January 1, 2009; 16(1): 7 - 19. [Abstract] [PDF]
|
|
|
|
|
|
K. Kerlikowske, R. Walker, D. L. Miglioretti, A. Desai, R. Ballard-Barbash, D. S. M. Buist, and for the National Cancer Institute-Sponsored Breast Obesity, Mammography Use and Accuracy, and Advanced Breast Cancer Risk J Natl Cancer Inst, December 3, 2008; 100(23): 1724 - 1733. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. S. Pankratz, L. C. Hartmann, A. C. Degnim, R. A. Vierkant, K. Ghosh, C. M. Vachon, M. H. Frost, S. D. Maloney, C. Reynolds, and J. C. Boughey Assessment of the Accuracy of the Gail Model in Women With Atypical Hyperplasia J. Clin. Oncol., November 20, 2008; 26(33): 5374 - 5379. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. C. Mahoney, T. Bevers, E. Linos, and W. C. Willett Opportunities and Strategies for Breast Cancer Prevention Through Risk Reduction CA Cancer J Clin, November 3, 2008; (2008) CA.2008.0016v1. [Abstract] [Full Text]
|
|
|
|
|
|
J. J. Heine, M. J. Carston, C. G. Scott, K. R. Brandt, F.-F. Wu, V. S. Pankratz, T. A. Sellers, and C. M. Vachon An Automated Approach for Estimation of Breast Density Cancer Epidemiol. Biomarkers Prev., November 1, 2008; 17(11): 3090 - 3097. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Shen, J. P. Costantino, and J. Qin Tamoxifen Chemoprevention Treatment and Time to First Diagnosis of Estrogen Receptor-Negative Breast Cancer J Natl Cancer Inst, October 15, 2008; 100(20): 1448 - 1453. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. D. Kurniawan, M. H. Wong, I. Windle, A. Rose, A. Mou, M. Buchanan, J. P. Collins, J. A. Miller, R. L. Gruen, and G. B. Mann Predictors of Surgical Margin Status in Breast-Conserving Surgery Within a Breast Screening Program Ann. Surg. Oncol., September 1, 2008; 15(9): 2542 - 2549. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. R. Spitz, C. J. Etzel, Q. Dong, C. I. Amos, Q. Wei, X. Wu, and W. K. Hong An Expanded Risk Prediction Model for Lung Cancer Cancer Prevention Research, September 1, 2008; 1(4): 250 - 254. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. M. Hall and D. B. Kopans, MD Mammographically Determined Breast Density and Cancer Risk Radiology, September 1, 2008; 248(3): 1083 - 1084. [Full Text] [PDF]
|
|
|
|
|
|
J. A. Shepherd, S. Malkov, B. Fan, A. Laidevant, R. Novotny, and G. Maskarinec Breast Density Assessment in Adolescent Girls Using Dual-Energy X-ray Absorptiometry: A Feasibility Study Cancer Epidemiol. Biomarkers Prev., July 1, 2008; 17(7): 1709 - 1713. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X. Yang, W.-Y. So, R. C W Ma, G. T C Ko, A. P S Kong, Q. Wang, C. S Cockram, C.-C. Chow, J. C N Chan, and P. C Y Tong Predicting values of lipids and white blood cell count for all-site cancer in type 2 diabetes Endocr. Relat. Cancer, June 1, 2008; 15(2): 597 - 607. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. M. Hall The Rise and Impending Decline of Screening Mammography Radiology, June 1, 2008; 247(3): 597 - 601. [Full Text] [PDF]
|
|
|
|
 |
|
 E. Warner, H. Messersmith, P. Causer, A. Eisen, R. Shumak, and D. Plewes Systematic Review: Using Magnetic Resonance Imaging to Screen Women at High Risk for Breast Cancer Ann Intern Med, May 6, 2008; 148(9): 671 - 679. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Tice, S. R. Cummings, R. Smith-Bindman, L. Ichikawa, W. E. Barlow, and K. Kerlikowske Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model Ann Intern Med, March 4, 2008; 148(5): 337 - 347. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. T. Chlebowski, G. L. Anderson, D. S. Lane, A. K. Aragaki, T. Rohan, S. Yasmeen, G. Sarto, C. A. Rosenberg, F. A. Hubbell, and For the Women's Health Initiative Investigators Predicting Risk of Breast Cancer in Postmenopausal Women by Hormone Receptor Status J Natl Cancer Inst, November 21, 2007; 99(22): 1695 - 1705. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X. Wu, J. Lin, H. B. Grossman, M. Huang, J. Gu, C. J. Etzel, C. I. Amos, C. P. Dinney, and M. R. Spitz Projecting Individualized Probabilities of Developing Bladder Cancer in White Individuals J. Clin. Oncol., November 1, 2007; 25(31): 4974 - 4981. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. M. Hall and K. E. Martin, MD Mammographic Density Categories Radiology, October 1, 2007; 245(1): 300 - 302. [Full Text] [PDF]
|
|
|
|
 |
|
 C. M. Vachon, T. A. Sellers, E. E. Carlson, J. M. Cunningham, C. A. Hilker, R. L. Smalley, D. J. Schaid, L. E. Kelemen, F. J. Couch, and V. S. Pankratz Strong Evidence of a Genetic Determinant for Mammographic Density, a Major Risk Factor for Breast Cancer Cancer Res., September 1, 2007; 67(17): 8412 - 8418. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. M. Tamimi, C. Byrne, G. A. Colditz, and S. E. Hankinson Endogenous Hormone Levels, Mammographic Density, and Subsequent Risk of Breast Cancer in Postmenopausal Women J Natl Cancer Inst, August 1, 2007; 99(15): 1178 - 1187. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. J Santen, N. F Boyd, R. T Chlebowski, S. Cummings, J. Cuzick, M. Dowsett, D. Easton, J. F Forbes, T. Key, S. E Hankinson, et al. Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model Endocr. Relat. Cancer, June 1, 2007; 14(2): 169 - 187. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. R. Spitz, W. K. Hong, C. I. Amos, X. Wu, M. B. Schabath, Q. Dong, S. Shete, and C. J. Etzel A Risk Model for Prediction of Lung Cancer J Natl Cancer Inst, May 2, 2007; 99(9): 715 - 726. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. J. Fenton, S. H. Taplin, P. A. Carney, L. Abraham, E. A. Sickles, C. D'Orsi, E. A. Berns, G. Cutter, R. E. Hendrick, W. E. Barlow, et al. Influence of Computer-Aided Detection on Performance of Screening Mammography N. Engl. J. Med., April 5, 2007; 356(14): 1399 - 1409. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Kerlikowske, L. Ichikawa, D. L. Miglioretti, D. S. M. Buist, P. M. Vacek, R. Smith-Bindman, B. Yankaskas, P. A. Carney, and R. Ballard-Barbash Longitudinal Measurement of Clinical Mammographic Breast Density to Improve Estimation of Breast Cancer Risk J Natl Cancer Inst, March 7, 2007; 99(5): 386 - 395. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Saslow, C. Boetes, W. Burke, S. Harms, M. O. Leach, C. D. Lehman, E. Morris, E. Pisano, M. Schnall, S. Sener, et al. American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography CA Cancer J Clin, March 1, 2007; 57(2): 75 - 89. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Kerlikowske The Mammogram That Cried Wolfe N. Engl. J. Med., January 18, 2007; 356(3): 297 - 300. [Full Text] [PDF]
|
|
|
|
|
|
J. G. Elmore and S. W. Fletcher The Risk of Cancer Risk Prediction: "What Is My Risk of Getting Breast Cancer?" J Natl Cancer Inst, December 6, 2006; 98(23): 1673 - 1675. [Full Text] [PDF]
|
|
|
|
|
|
A. Decarli, S. Calza, G. Masala, C. Specchia, D. Palli, and M. H. Gail Gail Model for Prediction of Absolute Risk of Invasive Breast Cancer: Independent Evaluation in the Florence-European Prospective Investigation Into Cancer and Nutrition Cohort J Natl Cancer Inst, December 6, 2006; 98(23): 1686 - 1693. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Breast Density: An Important Predictor of Breast Cancer? Journal Watch Women's Health, October 19, 2006; 2006(1019): 1 - 1. [Full Text]
|
|
|
|
 |
|
 Mammographic Breast Density in the Prediction of Breast Cancer Risk Journal Watch (General), September 26, 2006; 2006(926): 3 - 3. [Full Text]
|
|
|
|
|
|
M. L. Bondy and L. A. Newman Assessing breast cancer risk: evolution of the gail model. J Natl Cancer Inst, September 6, 2006; 98(17): 1172 - 1173. [Full Text] [PDF]
|
|