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Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort

INTRODUCTION: The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk As...

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Autores principales: Brentnall, Adam R., Harkness, Elaine F., Astley, Susan M., Donnelly, Louise S., Stavrinos, Paula, Sampson, Sarah, Fox, Lynne, Sergeant, Jamie C., Harvie, Michelle N., Wilson, Mary, Beetles, Ursula, Gadde, Soujanya, Lim, Yit, Jain, Anil, Bundred, Sara, Barr, Nicola, Reece, Valerie, Howell, Anthony, Cuzick, Jack, Evans, D. Gareth R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4665886/
https://www.ncbi.nlm.nih.gov/pubmed/26627479
http://dx.doi.org/10.1186/s13058-015-0653-5
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author Brentnall, Adam R.
Harkness, Elaine F.
Astley, Susan M.
Donnelly, Louise S.
Stavrinos, Paula
Sampson, Sarah
Fox, Lynne
Sergeant, Jamie C.
Harvie, Michelle N.
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Howell, Anthony
Cuzick, Jack
Evans, D. Gareth R.
author_facet Brentnall, Adam R.
Harkness, Elaine F.
Astley, Susan M.
Donnelly, Louise S.
Stavrinos, Paula
Sampson, Sarah
Fox, Lynne
Sergeant, Jamie C.
Harvie, Michelle N.
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Howell, Anthony
Cuzick, Jack
Evans, D. Gareth R.
author_sort Brentnall, Adam R.
collection PubMed
description INTRODUCTION: The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model). METHODS: Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression. RESULTS: The analysis included 50,628 women aged 47–73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34–1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25–1.48), O/E 60 % (95 % CI 44–74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12–1.33), O/E 46 % (95 % CI 26–65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33–1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32–1.60), combined AUC 0.59]. CONCLUSIONS: Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0653-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-46658862015-12-02 Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort Brentnall, Adam R. Harkness, Elaine F. Astley, Susan M. Donnelly, Louise S. Stavrinos, Paula Sampson, Sarah Fox, Lynne Sergeant, Jamie C. Harvie, Michelle N. Wilson, Mary Beetles, Ursula Gadde, Soujanya Lim, Yit Jain, Anil Bundred, Sara Barr, Nicola Reece, Valerie Howell, Anthony Cuzick, Jack Evans, D. Gareth R. Breast Cancer Res Research Article INTRODUCTION: The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model). METHODS: Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression. RESULTS: The analysis included 50,628 women aged 47–73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34–1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25–1.48), O/E 60 % (95 % CI 44–74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12–1.33), O/E 46 % (95 % CI 26–65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33–1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32–1.60), combined AUC 0.59]. CONCLUSIONS: Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0653-5) contains supplementary material, which is available to authorized users. BioMed Central 2015-12-01 2015 /pmc/articles/PMC4665886/ /pubmed/26627479 http://dx.doi.org/10.1186/s13058-015-0653-5 Text en © Brentnall et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Brentnall, Adam R.
Harkness, Elaine F.
Astley, Susan M.
Donnelly, Louise S.
Stavrinos, Paula
Sampson, Sarah
Fox, Lynne
Sergeant, Jamie C.
Harvie, Michelle N.
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Howell, Anthony
Cuzick, Jack
Evans, D. Gareth R.
Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
title Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
title_full Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
title_fullStr Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
title_full_unstemmed Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
title_short Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
title_sort mammographic density adds accuracy to both the tyrer-cuzick and gail breast cancer risk models in a prospective uk screening cohort
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4665886/
https://www.ncbi.nlm.nih.gov/pubmed/26627479
http://dx.doi.org/10.1186/s13058-015-0653-5
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