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Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer
The ability of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model to predict BRCA1 and BRCA2 mutations and breast cancer incidence in women with a family history of breast cancer was evaluated. Observed mutations in 263 screened families were compa...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3297752/ https://www.ncbi.nlm.nih.gov/pubmed/22124624 http://dx.doi.org/10.1007/s10689-011-9495-1 |
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author | Ståhlbom, Anne Kinhult Johansson, Hemming Liljegren, Annelie von Wachenfeldt, Anna Arver, Brita |
author_facet | Ståhlbom, Anne Kinhult Johansson, Hemming Liljegren, Annelie von Wachenfeldt, Anna Arver, Brita |
author_sort | Ståhlbom, Anne Kinhult |
collection | PubMed |
description | The ability of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model to predict BRCA1 and BRCA2 mutations and breast cancer incidence in women with a family history of breast cancer was evaluated. Observed mutations in 263 screened families were compared to retrospective predictions. Similarly, observed breast cancers in 640 women were compared to retrospective predictions of breast cancer incidence. The ratios of observed to expected number of BRCA1- , BRCA2- and BRCA(1 or 2) mutations were 1.43 (95% CI 1.05–1.90), 0.63 (95% CI 0.34–1.08), and 1.12 (95% CI 0.86–1.44), showing a significant underestimation of BRCA1 mutations. Discrimination between carriers and non-carriers as measured by area under the receiver operating characteristic (ROC) curve was 0.83 (95% CI 0.76–0.88). The ratio of observed to expected number of invasive breast cancers was 1.41 (0.91–2.08). The corresponding area under the ROC curve for prediction of invasive breast cancer at individual level was 0.62 (95% CI 0.52–0.73). In conclusion, the BOADICEA model can predict the total prevalence of BRCA(1 or 2) mutations and the incidence of invasive breast cancers. The mutation probability as generated by BOADICEA can be used clinically as a guideline for screening, and thus decrease the proportion of negative mutation analyses. Likewise, individual breast cancer risks can be used for selecting women whose risk of breast cancer indicates follow-up. Application of local mutation frequencies of BRCA1 and BRCA2 could improve the ability to distinguish between the two genes. |
format | Online Article Text |
id | pubmed-3297752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-32977522012-03-21 Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer Ståhlbom, Anne Kinhult Johansson, Hemming Liljegren, Annelie von Wachenfeldt, Anna Arver, Brita Fam Cancer Original Article The ability of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model to predict BRCA1 and BRCA2 mutations and breast cancer incidence in women with a family history of breast cancer was evaluated. Observed mutations in 263 screened families were compared to retrospective predictions. Similarly, observed breast cancers in 640 women were compared to retrospective predictions of breast cancer incidence. The ratios of observed to expected number of BRCA1- , BRCA2- and BRCA(1 or 2) mutations were 1.43 (95% CI 1.05–1.90), 0.63 (95% CI 0.34–1.08), and 1.12 (95% CI 0.86–1.44), showing a significant underestimation of BRCA1 mutations. Discrimination between carriers and non-carriers as measured by area under the receiver operating characteristic (ROC) curve was 0.83 (95% CI 0.76–0.88). The ratio of observed to expected number of invasive breast cancers was 1.41 (0.91–2.08). The corresponding area under the ROC curve for prediction of invasive breast cancer at individual level was 0.62 (95% CI 0.52–0.73). In conclusion, the BOADICEA model can predict the total prevalence of BRCA(1 or 2) mutations and the incidence of invasive breast cancers. The mutation probability as generated by BOADICEA can be used clinically as a guideline for screening, and thus decrease the proportion of negative mutation analyses. Likewise, individual breast cancer risks can be used for selecting women whose risk of breast cancer indicates follow-up. Application of local mutation frequencies of BRCA1 and BRCA2 could improve the ability to distinguish between the two genes. Springer Netherlands 2011-11-29 2012 /pmc/articles/PMC3297752/ /pubmed/22124624 http://dx.doi.org/10.1007/s10689-011-9495-1 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. |
spellingShingle | Original Article Ståhlbom, Anne Kinhult Johansson, Hemming Liljegren, Annelie von Wachenfeldt, Anna Arver, Brita Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer |
title | Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer |
title_full | Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer |
title_fullStr | Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer |
title_full_unstemmed | Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer |
title_short | Evaluation of the BOADICEA risk assessment model in women with a family history of breast cancer |
title_sort | evaluation of the boadicea risk assessment model in women with a family history of breast cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3297752/ https://www.ncbi.nlm.nih.gov/pubmed/22124624 http://dx.doi.org/10.1007/s10689-011-9495-1 |
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