Risk models used to counsel women for breast and ovarian cancer: a guide for clinicians

Abstract

Advances in the identification and treatment of breast and ovarian cancer have lead to a need for reliable estimates of susceptibility risk associated with these two cancers. These estimates may be used in clinical settings to identify individuals at increased risk of developing disease or of being a carrier of a disease susceptibility allele. Accurate assessment of these probabilities is important given the potential implications for medical decision-making including the identification of patients who might benefit from preventive measures, genetic counseling or from entry into clinical trials. A wide range of empirical and statistical models has been proposed, particularly for breast cancer risk prediction, including those that utilize logistic regression or Bayesian modeling. The specific data used to create the various risk models also varies and may include molecular, epidemiologic, or clinical information. This overview presents definitions of risk used in clinical oncology as well as several of the more frequently used methods of risk estimation for breast and ovarian cancer. In addition, the means by which different methods are able to provide a measure of error or uncertainty associated with a given risk estimate will be discussed.