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A comparison of five methods of measuring mammographic density: a case-control study

BACKGROUND: High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five ma...

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Autores principales: Astley, Susan M., Harkness, Elaine F., Sergeant, Jamie C., Warwick, Jane, Stavrinos, Paula, Warren, Ruth, Wilson, Mary, Beetles, Ursula, Gadde, Soujanya, Lim, Yit, Jain, Anil, Bundred, Sara, Barr, Nicola, Reece, Valerie, Brentnall, Adam R., Cuzick, Jack, Howell, Tony, Evans, D. Gareth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799922/
https://www.ncbi.nlm.nih.gov/pubmed/29402289
http://dx.doi.org/10.1186/s13058-018-0932-z
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author Astley, Susan M.
Harkness, Elaine F.
Sergeant, Jamie C.
Warwick, Jane
Stavrinos, Paula
Warren, Ruth
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Brentnall, Adam R.
Cuzick, Jack
Howell, Tony
Evans, D. Gareth
author_facet Astley, Susan M.
Harkness, Elaine F.
Sergeant, Jamie C.
Warwick, Jane
Stavrinos, Paula
Warren, Ruth
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Brentnall, Adam R.
Cuzick, Jack
Howell, Tony
Evans, D. Gareth
author_sort Astley, Susan M.
collection PubMed
description BACKGROUND: High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. METHODS: Women participating in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. RESULTS: The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72–7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56–3.78), 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67–1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79–7.18), 2.87 (95% CI 1.77–4.64) and 2.34 (95% CI 1.50–3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85–2.05). CONCLUSIONS: Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13058-018-0932-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-57999222018-02-13 A comparison of five methods of measuring mammographic density: a case-control study Astley, Susan M. Harkness, Elaine F. Sergeant, Jamie C. Warwick, Jane Stavrinos, Paula Warren, Ruth Wilson, Mary Beetles, Ursula Gadde, Soujanya Lim, Yit Jain, Anil Bundred, Sara Barr, Nicola Reece, Valerie Brentnall, Adam R. Cuzick, Jack Howell, Tony Evans, D. Gareth Breast Cancer Res Research Article BACKGROUND: High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. METHODS: Women participating in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. RESULTS: The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72–7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56–3.78), 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67–1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79–7.18), 2.87 (95% CI 1.77–4.64) and 2.34 (95% CI 1.50–3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85–2.05). CONCLUSIONS: Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13058-018-0932-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-05 2018 /pmc/articles/PMC5799922/ /pubmed/29402289 http://dx.doi.org/10.1186/s13058-018-0932-z Text en © The Author(s). 2018 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
Astley, Susan M.
Harkness, Elaine F.
Sergeant, Jamie C.
Warwick, Jane
Stavrinos, Paula
Warren, Ruth
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Brentnall, Adam R.
Cuzick, Jack
Howell, Tony
Evans, D. Gareth
A comparison of five methods of measuring mammographic density: a case-control study
title A comparison of five methods of measuring mammographic density: a case-control study
title_full A comparison of five methods of measuring mammographic density: a case-control study
title_fullStr A comparison of five methods of measuring mammographic density: a case-control study
title_full_unstemmed A comparison of five methods of measuring mammographic density: a case-control study
title_short A comparison of five methods of measuring mammographic density: a case-control study
title_sort comparison of five methods of measuring mammographic density: a case-control study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799922/
https://www.ncbi.nlm.nih.gov/pubmed/29402289
http://dx.doi.org/10.1186/s13058-018-0932-z
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