A team of researchers led by Columbia University Mailman School of Public Health professor, Dr. Mary Beth Terry, evaluated four commonly used breast cancer prediction models and found that family-history-based models perform better than non-family-history based models, even for women at average or below-average risk of breast cancer. The study is the largest independent analysis to validate four widely used models of breast cancer risk to date. The findings are published online in The Lancet Oncology.
Dr. Terry and colleagues used the Breast Cancer Prospective Family Study Cohort composed of 18,856 women from Australia, Canada, and the U.S. without breast cancer, between March 1992 and June 2011. Women between the ages of 20 to 70 were selected for the study who had no previous history of bilateral prophylactic mastectomy or ovarian cancer, and whose family history of breast cancer was available. The researchers calculated 10-year risk scores for the final cohort of 15,732 women, comparing four breast cancer risk models: BOADICEA, BRCAPRO, BCRAT, and IBIS. A second analysis was conducted based on the mutation status of the BRCA1 or BRCA2 genes.
BOADICIA and IBIS models which have multigenerational family history data were more accurate in predicting breast cancer risk than the other models.
“Our study was large enough to evaluate model performance across the full spectrum of absolute risk, including women with the highest risk of cancer in whom accurate prediction is especially important,” said Dr. Terry, who is a professor of epidemiology at the Mailman School of Public Health, and the Herbert Irving Comprehensive Cancer Center.
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