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Longitudinal study of mammographic density measures that predict breast cancer risk

Identifieur interne : 000870 ( Main/Exploration ); précédent : 000869; suivant : 000871

Longitudinal study of mammographic density measures that predict breast cancer risk

Auteurs : Kavitha Krishnan [Australie] ; Laura Baglietto [Australie, France, Italie] ; Jennifer Stone [Australie] ; Julie A. Simpson [Australie] ; Gianluca Severi [Italie] ; Christopher F. Evans [Australie] ; Robert J. Macinnis [Australie] ; Graham G. Giles [Australie] ; Carmel Apicella [Australie] ; John L. Hopper [Australie, Corée du Sud]

Source :

RBID : PMC:5380555

Abstract

Background

After adjusting for age and body mass index (BMI), mammographic measures - dense area (DA), percent dense area (PDA) and non-dense area (NDA) - are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time.

Methods

We collected 4,320 mammograms (age range, 24-83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1-14). DA, PDA and NDA were measured using the Cumulus software and normalised using the Box-Cox method. Correlations in the normalised risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modelling of Gompertz curves.

Results

Mean normalised DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid 60s onwards. Mean normalised NDA increased non-linearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalised DA were 0.94, 0.93, 0.91, 0.91 and 0.91 for mammograms taken 2, 4, 6, 8 and 10 years apart, respectively. Similar correlations were estimated for the age and BMI adjusted normalized PDA and NDA.

Conclusion

The mammographic measures that predict breast cancer risk are highly correlated over time.

Impact

This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g. early 40s or even younger) and recommended appropriate screening and prevention strategies.


Url:
DOI: 10.1158/1055-9965.EPI-16-0499
PubMed: 28062399
PubMed Central: 5380555


Affiliations:


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<wicri:regionArea>Department of Epidemiology and Preventive Medicine, Monash University, Melbourne</wicri:regionArea>
<placeName>
<settlement type="city">Melbourne</settlement>
<region type="état">Victoria (État)</region>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Apicella, Carmel" sort="Apicella, Carmel" uniqKey="Apicella C" first="Carmel" last="Apicella">Carmel Apicella</name>
<affiliation wicri:level="4">
<nlm:aff id="A1">Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Australia</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne</wicri:regionArea>
<placeName>
<settlement type="city">Melbourne</settlement>
<region type="état">Victoria (État)</region>
</placeName>
<orgName type="university">Université de Melbourne</orgName>
</affiliation>
</author>
<author>
<name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L" last="Hopper">John L. Hopper</name>
<affiliation wicri:level="4">
<nlm:aff id="A1">Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Australia</nlm:aff>
<country xml:lang="fr">Australie</country>
<wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne</wicri:regionArea>
<placeName>
<settlement type="city">Melbourne</settlement>
<region type="état">Victoria (État)</region>
</placeName>
<orgName type="university">Université de Melbourne</orgName>
</affiliation>
<affiliation wicri:level="4">
<nlm:aff id="A8">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="A9">Institute of Health and Environment, Seoul National University, Seoul, Korea</nlm:aff>
<country xml:lang="fr">Corée du Sud</country>
<wicri:regionArea>Institute of Health and Environment, 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>
</author>
</analytic>
<series>
<title level="j">Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology</title>
<idno type="ISSN">1055-9965</idno>
<idno type="eISSN">1538-7755</idno>
<imprint>
<date when="2017">2017</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass></textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<sec id="S1">
<title>Background</title>
<p id="P1">After adjusting for age and body mass index (BMI), mammographic measures - dense area (DA), percent dense area (PDA) and non-dense area (NDA) - are associated with breast cancer risk. Our aim was to use longitudinal data to estimate the extent to which these risk-predicting measures track over time.</p>
</sec>
<sec id="S2">
<title>Methods</title>
<p id="P2">We collected 4,320 mammograms (age range, 24-83 years) from 970 women in the Melbourne Collaborative Cohort Study and the Australian Breast Cancer Family Registry. Women had on average 4.5 mammograms (range, 1-14). DA, PDA and NDA were measured using the Cumulus software and normalised using the Box-Cox method. Correlations in the normalised risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modelling of Gompertz curves.</p>
</sec>
<sec id="S3">
<title>Results</title>
<p id="P3">Mean normalised DA and PDA were constant with age to the early 40s, decreased over the next two decades, and were almost constant from the mid 60s onwards. Mean normalised NDA increased non-linearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalised DA were 0.94, 0.93, 0.91, 0.91 and 0.91 for mammograms taken 2, 4, 6, 8 and 10 years apart, respectively. Similar correlations were estimated for the age and BMI adjusted normalized PDA and NDA.</p>
</sec>
<sec id="S4">
<title>Conclusion</title>
<p id="P4">The mammographic measures that predict breast cancer risk are highly correlated over time.</p>
</sec>
<sec id="S5">
<title>Impact</title>
<p id="P5">This has implications for etiologic research and clinical management whereby women at increased risk could be identified at a young age (e.g. early 40s or even younger) and recommended appropriate screening and prevention strategies.</p>
</sec>
</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Australie</li>
<li>Corée du Sud</li>
<li>France</li>
<li>Italie</li>
</country>
<region>
<li>Piémont</li>
<li>Région capitale de Séoul</li>
<li>Toscane</li>
<li>Victoria (État)</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Melbourne</li>
<li>Orsay</li>
<li>Pise</li>
<li>Séoul</li>
<li>Turin</li>
<li>Villejuif</li>
</settlement>
<orgName>
<li>Université Paris-Sud</li>
<li>Université de Melbourne</li>
<li>Université de Pise</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="Evans, Christopher F" sort="Evans, Christopher F" uniqKey="Evans C" first="Christopher F" last="Evans">Christopher F. Evans</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>
<name sortKey="Macinnis, Robert J" sort="Macinnis, Robert J" uniqKey="Macinnis R" first="Robert J" last="Macinnis">Robert J. Macinnis</name>
<name sortKey="Macinnis, Robert J" sort="Macinnis, Robert J" uniqKey="Macinnis R" first="Robert J" last="Macinnis">Robert J. Macinnis</name>
<name sortKey="Simpson, Julie A" sort="Simpson, Julie A" uniqKey="Simpson J" first="Julie A" last="Simpson">Julie A. Simpson</name>
<name sortKey="Stone, Jennifer" sort="Stone, Jennifer" uniqKey="Stone J" first="Jennifer" last="Stone">Jennifer Stone</name>
<name sortKey="Stone, Jennifer" sort="Stone, Jennifer" uniqKey="Stone J" first="Jennifer" last="Stone">Jennifer Stone</name>
</country>
<country name="France">
<region name="Île-de-France">
<name sortKey="Baglietto, Laura" sort="Baglietto, Laura" uniqKey="Baglietto L" first="Laura" last="Baglietto">Laura Baglietto</name>
</region>
</country>
<country name="Italie">
<region name="Toscane">
<name sortKey="Baglietto, Laura" sort="Baglietto, Laura" uniqKey="Baglietto L" first="Laura" last="Baglietto">Laura Baglietto</name>
</region>
<name sortKey="Severi, Gianluca" sort="Severi, Gianluca" uniqKey="Severi G" first="Gianluca" last="Severi">Gianluca Severi</name>
</country>
<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>
</region>
<name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L" last="Hopper">John L. Hopper</name>
</country>
</tree>
</affiliations>
</record>

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