Breast cancer prediction models more effective when they include family history data

Breast cancer prediction models based on family history are more effective than those that do not focus on that information, according to a new study published in The Lancet Oncology.

The authors studied data on more than 15,000 women from Australia, Canada and the United States who belonged to the Breast Cancer Prospective Family Study Cohort. All women analyzed for the study were between the ages of 20 and 70, did not have breast cancer when they were recruited into the cohort and had an available family history of breast cancer.

The researchers calculated 10-year risk scores for the women, comparing the results of four different breast cancer risk models: the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model, the Breast Cancer Risk Assessment Tool (BCRAT), the International Breast Cancer Intervention Study model (IBIS) and BRCAPRO. The team then performed a second analysis after 10 years to compare the performance of the four prediction models based on the patients’ BRCA1 and BRCA2 gene mutation status.

Overall, the BOADICEA and IBIS prediction models—which include family history data—were more accurate than the other two prediction models. The authors noted that this was even true for women at average or below-average risk of breast cancer.

“Our findings suggest that all women would benefit from risk assessment that involves collection of detailed family histories, and that risk models would be improved by inclusion of family history information including ages at diagnoses and types of cancer,” co-author Mary Beth Terry, PhD, a professor of epidemiology at the Columbia University Mailman School of Public Health in New York City, said in a prepared statement.