Predicting breast cancer risk in a racially diverse, community‐based sample of potentially high‐risk women

Abstract

Abstract Background Identifying women with high risk of breast cancer is necessary to study high‐risk experiences and deliver risk‐management care. Risk prediction models estimate individuals' lifetime risk but have rarely been applied in community‐based settings among women not yet receiving specialized care. Therefore, we aimed: (1) to apply three breast cancer risk prediction models (i.e., Gail, Claus, and IBIS) to a racially diverse, community‐based sample of women, and (2) to assess risk prediction estimates using survey data. Methods An online survey was administered to women who were determined by a screening instrument to have potentially high risk for breast cancer. Risk prediction models were applied using their self‐reported family and medical history information. Inclusion in the high‐risk subsample required ≥20% lifetime risk per ≥1 model. Descriptive statistics were used to compare the proportions of women identified as high risk by each model. Results N = 1053 women were initially eligible and completed the survey. All women, except one, self‐reported the information necessary to run at least one model; 90% had sufficient information for >1 model. The high‐risk subsample included 717 women, of which 75% were identified by one model only; 96% were identified by IBIS, 3% by Claus, <1% by Gail. In the high‐risk subsample, 20% were identified by two models and 3% by all three models. Conclusions Assessing breast cancer risk using self‐reported data in a community‐based sample was feasible. Different models identify substantially different groups of women who may be at high risk for breast cancer; use of multiple models may be beneficial for research and clinical care.

Keywords

• breast cancer prevention
• breast cancer risk
• Claus model
• Gail model
• IBIS model
• risk prediction