Understanding Mathematical Models for Breast Cancer Risk Assessment and Counseling
Article first published online: 21 DEC 2001
Blackwell Science Inc.
The Breast Journal
Volume 7, Issue 4, pages 224–232, July 2001
How to Cite
Euhus, D. M. (2001), Understanding Mathematical Models for Breast Cancer Risk Assessment and Counseling. The Breast Journal, 7: 224–232. doi: 10.1046/j.1524-4741.2001.20012.x
- Issue published online: 21 DEC 2001
- Article first published online: 21 DEC 2001
- breast cancer;
- risk assessment
Abstract: Chemoprevention and prophylactic surgery are effective interventions for lowering breast cancer incidence. However, these approaches are associated with risks of their own. Accurate individualized breast cancer risk assessment is an essential component of the risk/benefit analysis that must take place prior to implementing either of these strategies. Several mathematical models for estimating individual breast cancer risk have been proposed over the last decade. The Gail model is the most generally applicable model; however, it neglects family history information in second-degree relatives, treats pre- and postmenopausal breast cancer the same, and ignores personal histories of lobular neoplasia. The Claus model is a better family history model, but it does not assign any special relevance to histories of bilateral breast cancer or ovarian cancer, and neglects all of the nonfamily history information accounted for by the Gail model. BRCAPRO is a Bayesian family history model that calculates individual breast cancer probabilities based on the probability that a family carries a mutation in one of the BRCA genes. Though its treatment of family history information is more thorough than the other models, it neglects the nonfamily history risk factors accounted for by the Gail model and may not appreciate familial clustering unrelated to BRCA gene mutation. A thorough understanding of the principles of risk analysis and the available mathematical models is essential for anyone wishing to perform intervention counseling. This review describes the basic components of risk analysis, explains how the mathematical models work and compares the strengths and weaknesses of the various models. CancerGene is a software tool for running all of these models. It may be obtained without charge at http://www.swmed.edu/home_pages/cancergene.