Using known risk factors for breast cancer, mathematical models can be developed to help answer important questions. These mathematical models are useful tools for researchers and for patients as follows:
1.Research on risk factors The Claus risk assessment model was used to discover the subpopulation of people who had an autosomal dominant genetic allele that increased their risk from 10% to 92%. This led to the discovery of the BRCA genes associated with breast, ovarian, and prostate cancer.
2.Clinical trial eligibility The Gail risk assessment model was developed to help researchers determine who to enroll in the NSAPB Breast Cancer Prevention Trials
where chemoprevention was shown to reduce breast cancer risk.
3.Guidelines for doing BRCA testing BRCA testing is very expensive and practically worthless if done on everyone (because it is so rare to be homozygous for BRCA1 or BRCA2). Mathematical models such as the BRCAPRO, BOADICEA, and Tyrer-Cuzick models can help determine what patients should undergo BRCA testing. The decision for testing is usually made when one of these models predicts a 10% or greater chance that there is a mutation of the BRCA1, BRCA2, or both genes.
4.Guidelines for doing MRI screening for breast cancer – MRI screening for breast cancer is not a cost effective screening test for the general population, but in specific groups, there are clear cut reasons to do so. In general, screening MRI is recommended for women with 20-25% or greater lifetime risk of breast cancer. The BRCAPRO and Tyrer-Cuzick models have been used to help make clinical decisions about ordering MRIs for breast cancer screening.
5.Guidelines for breast cancer therapy The Gail model is used clinically to help
determine who should be put on tamoxifen or raloxifene for chemoprevention. Other models have been used to help make decisions about breast cancer risk reduction with prophylactic mastectomy.