A review of breast cancer risk assessment models

EDITORIAL: Comparing Breast Cancer Risk Assessment Models
Mitchell H. Gail and Phuong L. Mai
J. Natl. Cancer Inst. 2010 102: 665-668

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Many practitioners counsel women who have few risk factors for breast cancer as well as women with strong family histories. We believe that good calibration is the key requirement for using a particular risk assessment model to weigh the risks and benefits of an intervention and to design intervention trials.

Some applications, such as deciding who should receive screening mammography or allocating prevention resources to women at highest risk, require a risk assessment model with good discriminatory power. None of the models discussed by Amir et al has good discriminatory power, and the authors indicate possible ways to improve it. Promising directions include incorporating mammographic density, information on genotypes or regulation of gene expression [although initial studies have found only modest improvements in discriminatory accuracy from adding single-nucleotide  polymorphisms to models and more refined use of pathology data and biomarker data from biopsy samples. Thus, continuing efforts are needed to improve and assess risk models so that they can play a useful role, in concert with preventive interventions, in reducing the burden of breast cancer.


Assessing Women at High Risk of Breast Cancer: A Review of Risk Assessment Models
Eitan Amir, Orit C. Freedman, Bostjan Seruga, and D. Gareth Evans
J. Natl. Cancer Inst. 2010 102: 680-691

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It is well established that the greatest benefit from breast cancer prevention strategies comes from treating women who are at high risk of the disease. Among high-risk women, such prevention strategies have been shown to potentially reduce the incidence of breast cancer by up to 1500 cases per 100 000, whereas among low-risk women, the reduction is at best 25 cases per 100 000.

Consequently, it is imperative that accurate and individualized risk assessment can be carried out so that appropriate women are selected for prevention strategies. A number of models are available to assess both breast cancer risk and the chances of identifying a BRCA1 or BRCA2 mutation. 

Some models perform both tasks, but to date, none are totally  able to discriminate between families that do and do not have  mutations or between women who will and will not develop breast  cancer. Steady and incremental improvements in the models are  being made, but these changes require revalidation.

The discovery  of alleles that are associated with breast cancer risk will add a new layer of complexity to all of these models