Clinical Implementation of a Breast Cancer Risk Assessment Program in a Multiethnic Patient Population: Which Risk Model to Use?

Author(s): Park, Hannah Lui; Tran, Stephanie M; Lee, Jennifer; Goodman, Deborah; Ziogas, Argyrios; Kelly, Richard; Larsen, Kathryn M; Alvarez, Andrea; Tannous, Chris; Strope, Julie; Lynch, Wendy; Anton-Culver, Hoda | Abstract: The integration of risk assessment into clinical breast screening holds promise in decreasing breast cancer morbidity and mortality and increasing health care efficiency. Currently, clinical recommendations regarding risk counseling, screening, and chemoprevention are being made based on a woman’s personal risk. One of the criteria that can be used to categorize a woman as “increased risk” is her projected 5-year risk for invasive breast cancer as determined by one of various risk models which are heavily based on family history. We hypothesized that the frequency of screening mammography patients at our medical institution who would be considered at “increased risk” would be different according to different risk models and according to race/ethnicity, thus impacting the volume of patients who are targeted for risk-reducing intervention. Risk scores were calculated for 307 White, Hispanic, and Asian screening mammography patients according to the Gail, BCSC, and Tyrer-Cuzick models. Scores were compared within and between race/ethnicities, according to the different models, individually and in combination. As expected, White women had higher risk scores than Hispanic and Asian women according to all models tested (pl0.05), and a higher percent of White women were categorized as “increased risk” (pl0.0001). However, the correlations between models were moderate, resulting in inconsistencies of increased risk status for many women. Depending on the volume of patients undergoing risk assessment and the resources of staff and services providing the risk counseling and other downstream services, a prevention program may opt to use a combination of risk models suitable for their patient population instead of just one risk model.