Dataset Information

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.

SUBMITTER: Meadows RJ

PROVIDER: S-EPMC9636513 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Meadows Rachel J RJ Figueroa Wilson W Shane-Carson Kate P KP Padamsee Tasleem J TJ

Cancer medicine 20220406 21

<h4>Background</h4>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 pre ...[more]

PMID: 35388639

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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