Researchers have now developed what they claim is a better method to quantify a woman’s risk of developing breast cancer. This new prediction model is ready for the clinic and could easily be integrated into yearly mammogram screening programs.

“The model performs at a higher level than the Gail model. We distinguish high-risk women with more precision. Up to a quarter of all breast cancers are diagnosed among women in the top 10% of risk in any 5-year age group. Thus, with this model, we believe we can better tailor screening and prevention messages, counseling for chemoprevention, and so forth,” said Graham Colditz, MD, DrPH, professor of medicine at Washington University School of Medicine in St. Louis, MO.

Over the past 20 years, Dr. Colditz and his colleagues have been working on a more complete model classifying breast cancer risk. Recently, they published their model validation in the journal, Breast Cancer Research and Treatment, which shows that it may have superiority over the Gail model; currently, the Gail model is the most commonly used breast cancer risk model.1

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Related: Benefits of Mammography Screening Consistent Across Studies

The research team identified 3,426 cases of breast cancer from the Nurses’ Health Study (follow-up, 1994 to 2008) and the California Teachers Study (1995 to 2009). They used the data to compare their new model—the Rosner-Colditz model—with the Gail model. The Rosner-Colditz model for breast cancer includes well-established factors known to contribute to breast cancer risk, including body mass index, alcohol consumption, and age at first menstrual period. However, this new model also includes information not considered in other prediction methods, such as a woman’s age at menopause and the type of menopause experienced, whether it be natural or surgical.

The investigators found that the Rosner-Colditz model outperformed the Gail model by 3% to 5% and it was most accurate for women aged 47 to 69 years. The Rosner-Colditz model was found to be more accurate than the Gail model for predicting the likelihood that a woman would develop breast cancer in the next 5 years. Dr. Colditz said 25% of all breast cancer cases are diagnosed in the 10% of women at highest risk in any 5-year age group, and added that these are the women who will benefit the most from interventions that are known to reduce risk. However, Dr. Colditz indicated the performance of the Rosner-Colditz model dropped off for women aged 70 years and older, and for predicting breast cancer risk over longer periods of time (more than 5 years).

Related: Breast Cancer Resource Center

“A model that has good discriminatory ability to rank women according to their level of breast cancer risk has several clinical applications. First, the use of chemoprevention for high-risk women requires that such women be accurately identified so they can be referred to counseling on the risks and benefits of chemoprevention. Second, an accurate estimate of risk to age 80 or lifetime risk is needed to identify women for whom magnetic resonance imaging (MRI) may be indicated. ACS [American Cancer Society] guidelines use a lifetime risk as a cut point and a model is needed to estimate that risk and guide screening strategies for high-risk women,” Dr. Colditz told

Breast cancer expert Marshall Pitz, MD, assistant professor of medicine at the University of Manitoba, in Manitoba, Canada, added that this new model could help lower both breast cancer-related morbidity and mortality. “The methodology appears to be well done, and the results are interesting. This provides evidence of a well-validated model to predict breast cancer risk and it compares favorably to the commonly used Gail model by using new variables known to impact breast cancer risk, such as alcohol consumption,” Dr. Pitz told

Currently, the new prediction model is being implemented in the context of routine mammography services at a breast health center at Washington University School of Medicine.


  1. Rosner BA, Colditz GA, Hankinson SE, et al. Validation of Rosner-Colditz breast cancer incidence model using an independent data set, the California Teachers Study. Breast Cancer Res Treat. 2013;142(1):187-202.