Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study
Identifieur interne : 000793 ( Pmc/Checkpoint ); précédent : 000792; suivant : 000794Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study
Auteurs : Kavitha Krishnan [Australie] ; Laura Baglietto [Australie, France] ; Carmel Apicella [Australie] ; Jennifer Stone [Australie] ; Melissa C. Southey [Australie] ; Dallas R. English [Australie] ; Graham G. Giles [Australie] ; John L. Hopper [Australie, Corée du Sud]Source :
- Breast Cancer Research : BCR [ 1465-5411 ] ; 2016.
Abstract
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.
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.
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.
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.
The online version of this article (doi:10.1186/s13058-016-0722-4) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s13058-016-0722-4
PubMed: 27316945
PubMed Central: 4912759
Affiliations:
- Australie, Corée du Sud, France
- Région capitale de Séoul, Victoria (État)
- Melbourne, Séoul
- Université de Melbourne, Université nationale de Séoul
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<author><name sortKey="Southey, Melissa C" sort="Southey, Melissa C" uniqKey="Southey M" first="Melissa C." last="Southey">Melissa C. Southey</name>
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<author><name sortKey="English, Dallas R" sort="English, Dallas R" uniqKey="English D" first="Dallas R." last="English">Dallas R. English</name>
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<author><name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G." last="Giles">Graham G. Giles</name>
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<affiliation wicri:level="3"><nlm:aff id="Aff4">Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia</nlm:aff>
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<author><name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L." last="Hopper">John L. Hopper</name>
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<country>Australie</country>
<placeName><settlement type="city">Melbourne</settlement>
<region type="état">Victoria (État)</region>
</placeName>
</affiliation>
<affiliation wicri:level="4"><nlm:aff id="Aff8">Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea</nlm:aff>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul</wicri:regionArea>
<placeName><settlement type="city">Séoul</settlement>
<region type="capital">Région capitale de Séoul</region>
<settlement type="city">Séoul</settlement>
</placeName>
<orgName type="university">Université nationale de Séoul</orgName>
</affiliation>
<affiliation wicri:level="4"><nlm:aff id="Aff9">Institute of Health and Environment, Seoul National University, Seoul, Korea</nlm:aff>
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<wicri:regionArea>Institute of Health and Environment, Seoul National University, Seoul</wicri:regionArea>
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</analytic>
<series><title level="j">Breast Cancer Research : BCR</title>
<idno type="ISSN">1465-5411</idno>
<idno type="eISSN">1465-542X</idno>
<imprint><date when="2016">2016</date>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>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.</p>
</sec>
<sec><title>Methods</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s13058-016-0722-4) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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</author>
<author><name sortKey="Brandt, Kr" uniqKey="Brandt K">KR Brandt</name>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Breast Cancer Res</journal-id>
<journal-id journal-id-type="iso-abbrev">Breast Cancer Res</journal-id>
<journal-title-group><journal-title>Breast Cancer Research : BCR</journal-title>
</journal-title-group>
<issn pub-type="ppub">1465-5411</issn>
<issn pub-type="epub">1465-542X</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">27316945</article-id>
<article-id pub-id-type="pmc">4912759</article-id>
<article-id pub-id-type="publisher-id">722</article-id>
<article-id pub-id-type="doi">10.1186/s13058-016-0722-4</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Krishnan</surname>
<given-names>Kavitha</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Baglietto</surname>
<given-names>Laura</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
<xref ref-type="aff" rid="Aff5"></xref>
<xref ref-type="aff" rid="Aff6"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Apicella</surname>
<given-names>Carmel</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Stone</surname>
<given-names>Jennifer</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff3"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Southey</surname>
<given-names>Melissa C.</given-names>
</name>
<xref ref-type="aff" rid="Aff7"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>English</surname>
<given-names>Dallas R.</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Giles</surname>
<given-names>Graham G.</given-names>
</name>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff2"></xref>
<xref ref-type="aff" rid="Aff4"></xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Hopper</surname>
<given-names>John L.</given-names>
</name>
<address><phone>+61 3 8344 0697</phone>
<fax>+61 3 9349 5815</fax>
<email>j.hopper@unimelb.edu.au</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
<xref ref-type="aff" rid="Aff8"></xref>
<xref ref-type="aff" rid="Aff9"></xref>
</contrib>
<aff id="Aff1"><label></label>
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia</aff>
<aff id="Aff2"><label></label>
Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia</aff>
<aff id="Aff3"><label></label>
Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Crawley, Australia</aff>
<aff id="Aff4"><label></label>
Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia</aff>
<aff id="Aff5"><label></label>
Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France</aff>
<aff id="Aff6"><label></label>
Gustave Roussy, F-94805 Villejuif, France</aff>
<aff id="Aff7"><label></label>
Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia</aff>
<aff id="Aff8"><label></label>
Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea</aff>
<aff id="Aff9"><label></label>
Institute of Health and Environment, Seoul National University, Seoul, Korea</aff>
</contrib-group>
<pub-date pub-type="epub"><day>18</day>
<month>6</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>18</day>
<month>6</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="ppub"><year>2016</year>
</pub-date>
<volume>18</volume>
<elocation-id>63</elocation-id>
<history><date date-type="received"><day>8</day>
<month>2</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>28</day>
<month>5</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s). 2016</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), 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 (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>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.</p>
</sec>
<sec><title>Methods</title>
<p>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.</p>
</sec>
<sec><title>Results</title>
<p>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.</p>
</sec>
<sec><title>Conclusions</title>
<p>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.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s13058-016-0722-4) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Mammographic density</kwd>
<kwd>Breast cancer</kwd>
<kwd>Detection mode</kwd>
<kwd>Tumor size</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution>VicHealth (AU)</institution>
</funding-source>
</award-group>
<award-group><funding-source><institution>Cancer Council Victoria (AU)</institution>
</funding-source>
</award-group>
<award-group><funding-source><institution>Victorian Breast Cancer Research Consortium</institution>
</funding-source>
</award-group>
<award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100000925</institution-id>
<institution>National Health and Medical Research Council</institution>
</institution-wrap>
</funding-source>
<award-id>251533, 209057, and 504711</award-id>
</award-group>
<award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001026</institution-id>
<institution>National Breast Cancer Foundation</institution>
</institution-wrap>
</funding-source>
</award-group>
<award-group><funding-source><institution>John and Allan Gilmour Research Award </institution>
</funding-source>
</award-group>
<award-group><funding-source><institution>May Stewart Bursary scholarships</institution>
</funding-source>
</award-group>
<award-group><funding-source><institution>Marie Curie International Incoming Fellowship </institution>
</funding-source>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2016</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Australie</li>
<li>Corée du Sud</li>
<li>France</li>
</country>
<region><li>Région capitale de Séoul</li>
<li>Victoria (État)</li>
</region>
<settlement><li>Melbourne</li>
<li>Séoul</li>
</settlement>
<orgName><li>Université de Melbourne</li>
<li>Université nationale de Séoul</li>
</orgName>
</list>
<tree><country name="Australie"><region name="Victoria (État)"><name sortKey="Krishnan, Kavitha" sort="Krishnan, Kavitha" uniqKey="Krishnan K" first="Kavitha" last="Krishnan">Kavitha Krishnan</name>
</region>
<name sortKey="Apicella, Carmel" sort="Apicella, Carmel" uniqKey="Apicella C" first="Carmel" last="Apicella">Carmel Apicella</name>
<name sortKey="Baglietto, Laura" sort="Baglietto, Laura" uniqKey="Baglietto L" first="Laura" last="Baglietto">Laura Baglietto</name>
<name sortKey="Baglietto, Laura" sort="Baglietto, Laura" uniqKey="Baglietto L" first="Laura" last="Baglietto">Laura Baglietto</name>
<name sortKey="English, Dallas R" sort="English, Dallas R" uniqKey="English D" first="Dallas R." last="English">Dallas R. English</name>
<name sortKey="English, Dallas R" sort="English, Dallas R" uniqKey="English D" first="Dallas R." last="English">Dallas R. English</name>
<name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G." last="Giles">Graham G. Giles</name>
<name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G." last="Giles">Graham G. Giles</name>
<name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G." last="Giles">Graham G. Giles</name>
<name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L." last="Hopper">John L. Hopper</name>
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<country name="Corée du Sud"><region name="Région capitale de Séoul"><name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L." last="Hopper">John L. Hopper</name>
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