| Autor: |
Rachel J. Meadows, Wilson Figueroa, Kate P. Shane‐Carson, Tasleem J. Padamsee |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Cancer Medicine, Vol 11, Iss 21, Pp 4043-4052 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2045-7634 |
| DOI: |
10.1002/cam4.4721 |
| Popis: |
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, |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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