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

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.

Role of Breast Cancer Risk Estimation Models to Identify Women Eligible for Genetic Testing and Risk-Reducing Surgery

“…Many studies have shown that the IBIS model, compared to the Gail and Claus models, is able to identify the highest percentage of the population at high risk [38][39][40][41].…”

Involvement of Total Antioxidant Activity and eNOS Gene rs1799983/ rs2070744 Polymorphisms in Breast Carcinogenesis

“…Breast cancer is a life threatening malignancy that affects one in every eight women worldwide as stated by recent epidemiological statistics [1]. The high predicted global cancer statistics (12.5%) has put breast cancer as the second leading cause of cancer mortality in women with approximately 700,000 deaths in 2020 [2]. This emphasizes that much more efforts need to be allocated to understand the molecular events associated with breast cancer initiation and progression.…”