Breast density helps predict breast cancer risk
Two new models for assessing patients' risk of developing breast cancer focus on breast density as an important predictor, two studies report in the September 6 issue of the Journal of the National Cancer Institute.
A breast cancer risk model called the Gail model was developed in the 1980s to assess the risk of breast cancer for women who undergo annual mammography screening, and several groups the Breast Cancer Surveillance Consortium (BCSC) and the Breast Cancer Detection Demonstration Project (BCDDP), among others have made attempts to update and expand it.
The first study in JNCI presents a new model that adds breast density as a factor predicting a patient's risk of developing breast cancer. A second study by the developer of the original Gail model updates that model to take breast density into account.
In the first study, William E. Barlow, Ph.D., of Cancer Research and Biostatistics in Seattle, and colleagues identified 11,638 women diagnosed with breast cancer in a large prospective risk study. They constructed models to predict breast cancer risk for pre- and postmenopausal women.
The authors found that several factors influenced breast cancer diagnosis in premenopausal women, including age, breast density, family history of breast cancer, and prior breast procedure. For postmenopausal women, risk factors included ethnicity, body mass index, natural menopause, use of hormone therapy, a prior false positive mammogram, and the risk factors in premenopausal women. They write that their model may identify women at high-risk for breast cancer more accurately than the Gail model.
The models establish breast density as a highly clinically significant predictor of breast cancer risk that is almost as powerful a risk factor as age. "Nonetheless, [the] ability to accurately predict breast cancer at the individual level remains limited," Barlow and colleagues write.
The second study by Jinbo Chen, Ph.D., and Mitchell H. Gail, M.D., Ph.D., of the National Cancer Institute in Bethesda, Md., and colleagues assesses the absolute risk of developing breast cancer in an updated version of the Gail model. The model included breast density, weight, age at first live birth, the number of previous benign breast biopsy examinations, and the number of first-degree relatives with breast cancer. The researchers investigated whether information on breast density, which was available for 7,251 women in the BCDDP, could improve absolute breast cancer risk predictions compared to an earlier version of the Gail model, which was also based on BCDDP data but did not incorporate breast density.
The new model predicted that women with a high breast density had a higher risk of cancer. The authors suggest that the latest model predicted risk more accurately than a model that did not take breast density into account, and it is applicable to both pre- and post-menopausal women.
The authors write, "If the new model is shown to be valid in independent evaluations, a more convenient program could be made available for counseling and other applications."
In an accompanying editorial, Melissa L. Bondy, Ph.D., of the M.D. Anderson Cancer Center in Houston, and Lisa A. Newman, M.D., of the University of Michigan Comprehensive Cancer Center in Ann Arbor, write, "Inclusion of breast density, and perhaps other modifiable risk factors, is indeed exciting in the ongoing evolution of breast cancer prediction tools and our quest for accurate, individualized estimates."
Article 1: Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, et al. Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography. J Natl Cancer Inst 2006;98:1204-1214.
Article 2: Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, et al. Projecting Absolute Invasive Breast Cancer Risk in White Women with Model that Includes Mammographic Density. J Natl Cancer Inst 2006;98:1215-1226.
Editorial: Bondy ML, Newman LA. Assessing Breast Cancer Risk: Evolution of the Gail Model. J Natl Cancer Inst 2006;98:1172-1173.
Note: The Journal of the National Cancer Institute is published by Oxford University Press and is not affiliated with the National Cancer Institute. Attribution to the Journal of the National Cancer Institute is requested in all news coverage. Visit the Journal online at http://jncicancerspectrum.oxfordjournals.org/.
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