Better Predictor of Breast Cancer Risk Developed
By Julia Evangelou Strait
Graham A. Colditz, MD, DrPH, is one of the developers of the Rosner-Colditz statistical model, an improved method to predict a woman’s risk of developing breast cancer. Photo by Robert Boston
Nov. 26, 2013 – Epidemiologists have designed
a better method to quantify a woman’s risk of developing breast cancer, according to researchers at Washington University School of Medicine in St. Louis and their collaborators. The model could help identify women at high
risk of breast cancer who may benefit from prevention strategies that reduce the chances
of developing the disease.
“Over the past 20 years, we have worked to develop a more complete model for classifying risk of breast cancer,” said Graham Colditz, MD, DrPH, associate director for cancer prevention and control at Siteman Cancer
Center at Barnes-Jewish Hospital and Washington University. “The next step is to incorporate it into clinical practice so we can improve prevention.”
The model validation appears online in the journal Breast Cancer Research and Treatment.
The Rosner-Colditz model for breast cancer considers well-established factors known to contribute to breast cancer risk, including body mass index, alcohol consumption and age at first menstrual period. But the model also includes information not considered in other prediction methods, such as a woman’s age at menopause and the type of menopause, whether natural or surgical (following the removal of ovaries).
According to the study, the Rosner-Colditz model outperforms the most commonly used model by 3 to 5 percent. The researchers verified the accuracy of their model using data from the California Teachers Study, which includes information about the development of breast cancer in more than 130,000 teachers over a 14-year period.
The model was most accurate for women ages 47 to 69 and for predicting the likelihood that a woman would develop breast cancer in the next five years. Similar to other models, the performance of the Rosner-Colditz model dropped off for women age 70 and older and for predicting breast cancer risk over longer periods of time.
Colditz, who is also the Niess-Gain Professor of Surgery, said the model is most useful for helping to stratify risk and identify women who are much more likely than average to receive a breast cancer diagnosis in the next five years.
“One-quarter of all breast cancer cases are diagnosed in the 10 percent of women at highest risk in any five-year age group,” Colditz said. “These are the women who will benefit most from interventions that are known to reduce risk.”
Interventions that reduce breast cancer risk include lifestyle changes such as weight loss and increased physical activity. Doctors who know they have a high-risk patient also have preventive options that include increased surveillance and treatments that influence the way the body uses or makes estrogen, such as drugs like tamoxifen, raloxifene and aromatase inhibitors.
Colditz said he and his colleagues are working to integrate this type of risk modeling into clinical care at the Joanne Knight Breast Health Center so that when a woman comes in for her yearly mammogram, she also is given an estimate of her risk of developing breast cancer over the next five years. The Knight Breast Health Center is part of the Siteman Cancer Center.
This work was supported by the National Cancer Institute, National Institutes of Health (PO1 CA87969), by NIH grants RO1 CA077398 and K05 CA136967, an American Cancer Society Clinical Research Professorship and the Breast Cancer Research Foundation.
Rosner BA, Colditz GA, Hankinson SE, Sullivan-Halley J, Lacey Jr. JV, Bernstein L. Validation of Rosner-Colditz breast cancer incidence model using an independent data set, the California Teachers Study. Breast Cancer Research and Treatment. November 2013.