Breast Density Contributes to Breast Cancer Risk

Cancer Connect

According to the results of two studies published in the Journal of the National Cancer Institute, the addition of information about breast density improves the accuracy of tools to predict breast cancer risk.

The Gail model is a tool that estimates a womans risk of developing breast cancer. The model has been used to identify high-risk women for inclusion in breast cancer prevention studies, and has also been used to counsel individual women about their risk of breast cancer. The model considers a womans age, family history of breast cancer, reproductive history (age at first menstrual period and age at first birth), and history of breast biopsies. The model was originally tested in non-Hispanic White women, and still needs to be validated in other racial and ethnic groups.

In an attempt to improve the accuracy of the Gail model, researchers have considered the addition of more recently identified risk factors for breast cancer, such as breast density and postmenopausal hormone use. Breast density refers to the extent of glandular and connective tissue in the breast. Breasts with more glandular and connective tissue-and less fat-are denser. Breast density is assessed by mammography, and high breast density been linked with an increased risk of breast cancer.

The first study evaluated information from over two million screening mammograms.[1] Breast density was classified on a scale of one to four, with one being almost entirely fat and four being extremely dense.

  • Among premenopausal women, factors that were linked with an increased risk of breast cancer were increasing age, higher breast density, family history of breast cancer, and a prior breast procedure (such as a prior biopsy).
  • Among postmenopausal women, factors that were linked with an increased risk of breast cancer were increasing age, higher breast density, higher body mass index, no live birth or older age at first birth, family history of breast cancer, prior breast procedure, current postmenopausal hormone use, natural (as opposed to surgical) menopause, and previous false-positive mammogram. There was also variability by race and ethnicity, with Asian/Pacific Islanders and Native-Americans having a lower risk than White women, and Hispanic women having a lower risk than non-Hispanic women.
  • In both pre- and postmenopausal women, breast density was one of the strongest predictors of breast cancer risk. In premenopausal women, the risk of breast cancer was almost four times higher in those with the highest breast density than in those with the lowest breast density. In postmenopausal women, the risk of breast cancer was roughly three-times higher in those with the highest breast density.

The researchers conclude that breast density is a strong additional risk factor for breast cancer.

The second study also assessed the effect of adding information about breast density to breast cancer risk estimates.[2] The senior author of this second study was Mitchell Gail, the primary developer of the original Gail model. The researchers compared results from the Gail model to results from a model that included breast density, age at first birth, family history of breast cancer, number of previous breast biopsies, and weight.

The researchers reported that the new model provided modest improvements over the Gail model, but needs to be validated in other populations.

Taken together, these studies emphasize the important contribution of breast density to breast cancer risk. Incorporation of information about breast density may improve the accuracy of breast cancer risk estimates.

References:

[1] Barlow WE, White E, Ballard-Barbash R et al. Prospective Breast Cancer Risk Prediction Model for Women Undergoing Screening Mammography. Journal of the National Cancer Institute. 2006;98:1204-14.

[2] Chen J, Pee D, Ayyagari R et al. Projecting Absolute Invasive Breast Cancer Risk in White Women with a Model that Includes Mammographic Density. Journal of the National Cancer Institute. 2006;98:1215-26.

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