About the Breast Cancer Risk Assessment Calculator (The Gail Model)

The Gail Model

The Breast Cancer Risk Assessment Tool (BCRAT) is based on a statistical model known as the Gail Model, named after Dr. Mitchell Gail, Senior Investigator in the Biostatistics Branch of the NCI Division of Cancer Epidemiology and Genetics

The tool uses a woman’s own personal information to estimate risk of developing invasive breast cancer over specific periods of time, including:

Age
Age at the start of menstruation
Age at first live birth of a child
Number of first-degree relative (mother, sisters, daughters) with breast cancer
Number of previous breast biopsies (whether positive or negative)
Presence of atypical hyperplasia in a biopsy

Data from the Breast Cancer Detection Demonstration Project (BCDDP), a joint NCI and American Cancer Society breast cancer screening study that involved 280,000 White women aged 35 to 74 years, and from the NCI Surveillance, Epidemiology, and End Results (SEER) Program, were used in developing the model.

Estimates for Black/African American women were based on data from the Women’s Contraceptive and Reproductive Experiences (CARE) Study and from SEER data. CARE participants included 1,607 women with invasive breast cancer and 1,637 without.

Estimates for Asian and Pacific Islander women in the United States were based on data from the Asian American Breast Cancer Study (AABCS) and SEER data. AABCS participants included 597 Asian and Pacific Islander women with invasive breast cancer, and 966 women without breast cancer.

Estimates for Hispanic women were based on the San Francisco Bay Area Breast Cancer Study (SFBCS) and the California Cancer Registry and SEER Program. SFBCS participants included 1,086 women with invasive breast cancer and 1,411 women without breast cancer.

Testing the Model

The Gail Model has been tested in large populations of White women and has been shown to provide accurate estimates of breast cancer risk. The model was tested for Asian and Pacific Islander women, Black/African American, and Hispanic women using data from the Women’s Health Initiative. It performed well but may underestimate risk in Black/African American women with previous biopsies and Hispanic women born outside the United States. The model needs further validation for Hispanic women and other subgroups. Researchers are conducting additional studies to gather more data to test and improve the model.

Other Risk Assessment Tools

Other risk assessment tools are more appropriate for women who have a history of certain medical conditions. Below is a list of alternative resources for women with a medical history of:

Breast Cancer or Lobular Carcinoma in Situ (LCIS) or Ductal Carcinoma in Situ (DCIS)

Women with a history of breast cancer have risks of recurrence that depend on the type of breast cancer, its stage at diagnosis, and treatment. A cancer doctor can provide guidance on future risks for breast cancer survivors.
Women with a history of DCIS have risk of invasive breast cancer that depends on type of treatment for DCIS; a cancer doctor can provide information on this risk.
Women with a history of LCIS can use the IBIS Breast Cancer Risk Evaluation Tool to estimate the risk of invasive breast cancer or DCIS. A cancer doctor can also provide information on the risk.

Treatment with Radiation to the Chest

Women who received radiation for the treatment of Hodgkin lymphoma have higher than average risk of breast cancer. These risks are discussed in the scientific manuscript Travis, L.B et al. (J Natl Cancer Inst 2005; 97:1428-37).

A Known Mutation in Either the BRCA1 or BRCA2 Gene

Women with a known mutation in either the BRCA1 or BRCA2 gene can use the BOADICEA model to estimate their breast cancer risk.

Other Rare Breast Cancer-Causing Syndromes, such as Li-Fraumeni Syndrome

Women with a known or suspected inherited breast cancer-causing syndrome should consult a specialist in medical genetics.

Download the Source Code

This tool may be updated periodically as new data or research become available. The algorithm was last updated in December 2017. The current version is 4.1. Source code in SAS and R may be obtained from the DCEG website.

References