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Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study
BACKGROUND: Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-de...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912759/ https://www.ncbi.nlm.nih.gov/pubmed/27316945 http://dx.doi.org/10.1186/s13058-016-0722-4 |
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author | Krishnan, Kavitha Baglietto, Laura Apicella, Carmel Stone, Jennifer Southey, Melissa C. English, Dallas R. Giles, Graham G. Hopper, John L. |
author_facet | Krishnan, Kavitha Baglietto, Laura Apicella, Carmel Stone, Jennifer Southey, Melissa C. English, Dallas R. Giles, Graham G. Hopper, John L. |
author_sort | Krishnan, Kavitha |
collection | PubMed |
description | BACKGROUND: Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-detected include those that predict masking. Our aim was to investigate the associations between mammographic measures and (1) inherent risk, and (2) masking. METHODS: We conducted a case-control study nested within the Melbourne collaborative cohort study of 244 screen-detected cases (192 small tumors (<2 cm)) matched to 700 controls and 148 interval cases (76 small tumors) matched to 446 controls. Dense area (DA), percent dense area (PDA), and non-dense area (NDA) were measured using the Cumulus software. Conditional and unconditional logistic regression were applied as appropriate to estimate the odds per adjusted standard deviation (OPERA) adjusted for age and body mass index (BMI), allowing for the association with BMI to be a function of age at diagnosis. Tests of fit were performed using the Bayesian information criterion (BIC) and the area under the receiver operating characteristic curve. RESULTS: For screen-detected cancer, the association with BMI had a marginally significant dependence on age at diagnosis, and after adjustment both DA and PDA were associated with risk (OPERA approximately 1.2) and gave a similar fit. NDA was not associated with risk. For interval cancer, the BMI risk association was not dependent on age at diagnosis and the best fitting model was PDA alone (OPERA = 2.24, 95 % confidence interval 1.75, 2.86). Prediction of interval versus screen-detected cancer was best achieved by PDA alone (OPERA = 1.76, 95 % confidence interval 1.39, 2.22) with no association with BMI. When the analysis was restricted to small tumors to reduce the influence of tumor growth, we obtained similar results. CONCLUSIONS: Inherent breast cancer risk is predicted by BMI and DA or PDA, but not NDA. Masking is predicted by PDA, and not by BMI. Understanding risk and masking could help tailor mammographic screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-016-0722-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4912759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49127592016-06-19 Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study Krishnan, Kavitha Baglietto, Laura Apicella, Carmel Stone, Jennifer Southey, Melissa C. English, Dallas R. Giles, Graham G. Hopper, John L. Breast Cancer Res Research Article BACKGROUND: Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-detected include those that predict masking. Our aim was to investigate the associations between mammographic measures and (1) inherent risk, and (2) masking. METHODS: We conducted a case-control study nested within the Melbourne collaborative cohort study of 244 screen-detected cases (192 small tumors (<2 cm)) matched to 700 controls and 148 interval cases (76 small tumors) matched to 446 controls. Dense area (DA), percent dense area (PDA), and non-dense area (NDA) were measured using the Cumulus software. Conditional and unconditional logistic regression were applied as appropriate to estimate the odds per adjusted standard deviation (OPERA) adjusted for age and body mass index (BMI), allowing for the association with BMI to be a function of age at diagnosis. Tests of fit were performed using the Bayesian information criterion (BIC) and the area under the receiver operating characteristic curve. RESULTS: For screen-detected cancer, the association with BMI had a marginally significant dependence on age at diagnosis, and after adjustment both DA and PDA were associated with risk (OPERA approximately 1.2) and gave a similar fit. NDA was not associated with risk. For interval cancer, the BMI risk association was not dependent on age at diagnosis and the best fitting model was PDA alone (OPERA = 2.24, 95 % confidence interval 1.75, 2.86). Prediction of interval versus screen-detected cancer was best achieved by PDA alone (OPERA = 1.76, 95 % confidence interval 1.39, 2.22) with no association with BMI. When the analysis was restricted to small tumors to reduce the influence of tumor growth, we obtained similar results. CONCLUSIONS: Inherent breast cancer risk is predicted by BMI and DA or PDA, but not NDA. Masking is predicted by PDA, and not by BMI. Understanding risk and masking could help tailor mammographic screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-016-0722-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-18 2016 /pmc/articles/PMC4912759/ /pubmed/27316945 http://dx.doi.org/10.1186/s13058-016-0722-4 Text en © The Author(s). 2016 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 Krishnan, Kavitha Baglietto, Laura Apicella, Carmel Stone, Jennifer Southey, Melissa C. English, Dallas R. Giles, Graham G. Hopper, John L. Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
title | Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
title_full | Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
title_fullStr | Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
title_full_unstemmed | Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
title_short | Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
title_sort | mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912759/ https://www.ncbi.nlm.nih.gov/pubmed/27316945 http://dx.doi.org/10.1186/s13058-016-0722-4 |
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