Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Krishnan, Kavitha, Baglietto, Laura, Apicella, Carmel, Stone, Jennifer, Southey, Melissa C., English, Dallas R., Giles, Graham G., Hopper, John L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
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
_version_ 1782438319971368960
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
work_keys_str_mv AT krishnankavitha mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT bagliettolaura mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT apicellacarmel mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT stonejennifer mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT southeymelissac mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT englishdallasr mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT gilesgrahamg mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy
AT hopperjohnl mammographicdensityandriskofbreastcancerbymodeofdetectionandtumorsizeacasecontrolstudy