Risk Assessment Methods

There are several statistical models that are commonly used to determine a woman's risk of breast cancer. These models use data from large populations of women to estimate an individual woman's risk. Each tool has its own strengths and limitations.

Gail Risk Model
The Gail Risk Model is a computer program that uses your family history and medical history to estimate your chances of developing breast cancer in the next five years. The Gail Risk Model was developed by Dr. Mitchell Gail, a researcher from the National Cancer Institute. Your Gail Risk Model score can give you a general estimate of your risk.

The program was originally created in order to determine which women were eligible to participate in the National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 study. This study examined whether tamoxifen reduced the incidence of breast cancer in high-risk women. For the purposes of this study, any woman with a 5-year Gail Risk Model score of 1.7% or higher was considered high risk, and therefore, was eligible to participate.

What does a 1.7% score mean and how is it calculated? To use the Gail Risk Model, the computer program asks for information about a number of factors that could increase your risk. These factors include:

• Current age
• Age of first menstrual period
• Number of breast biopsies and whether atypical hyperplasia was found
• Age at first live birth
• Number of first-degree relatives with breast cancer

After this information is entered, the program calculates your absolute personal risk of developing breast cancer within a certain period of time. As an example, assume you wanted to calculate your risk within 5 years. If the program gave you a score of 1.7%, it means you have a 1.7% chance of developing breast cancer within the next five years.

There are a number of important limitations to the Gail Risk Model. For example, the model does not take into account the ages at which affected relatives were diagnosed with breast cancer. Further, it excludes second-degree relatives and any history of breast cancer on the father's side of the family. As a result, the model can under-predict risk in women who have one or more of these factors.

Generally, the Gail Risk Model has been found to over-predict breast cancer risk among women age 35 to 61 who do not receive annual mammograms. This is largely attributed to the fact that the model was developed based on data from women receiving annual mammograms, and is therefore most appropriately used among this population of women. These limitations mean that using the Gail Risk Model alone may not present a woman with a thorough picture of her risk level.

Claus Model
The Claus Model focuses solely on family history to estimate the probability that a woman will develop breast cancer. Using this model requires providing information about first-degree and second-degree relatives who have had breast cancer. Unlike the Gail Risk Model, it factors in the ages at which these relatives were diagnosed. It can also include breast cancer history on the father's side of the family as well.

The Claus Model's main advantage is its expanded inclusion of family history information. It is limited in that it can be used only with women who have at least one first- or second-degree relative with breast cancer. This assessment tool does not take into account other risk factors such as the presence of atypical hyperplasia, age of first menstrual period or age of first live birth.

How Does the FirstCyte^® Breast Test fit in?

Like the Gail Risk Model or Claus Model, the FirstCyte Breast Test can also provide information about a woman's risk of breast cancer. The approaches are complementary, but very different.

The Gail Risk Model and Claus Model use general statistics from a large population of women to approximate an individual woman's risk. In contrast, the FirstCyte Breast Test uses individualized cellular information from a woman's breast to indicate abnormal changes. Knowing whether abnormal cells are present will help the patient and physician make decisions on ways to reduce her risk.