Longitudinal study of mammographic density measures that predict breast cancer risk
Identifieur interne : 001F55 ( Pmc/Curation ); précédent : 001F54; suivant : 001F56Longitudinal 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 :
- Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology [ 1055-9965 ] ; 2017.
Abstract
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.
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.
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.
The mammographic measures that predict breast cancer risk are highly correlated over time.
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.
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DOI: 10.1158/1055-9965.EPI-16-0499
PubMed: 28062399
PubMed Central: 5380555
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Simpson</name><affiliation wicri:level="1"><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></affiliation></author><author><name sortKey="Severi, Gianluca" sort="Severi, Gianluca" uniqKey="Severi G" first="Gianluca" last="Severi">Gianluca Severi</name><affiliation wicri:level="1"><nlm:aff id="A4">Human Genetics Foundation, Torino, Italy</nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea>Human Genetics Foundation, Torino</wicri:regionArea></affiliation></author><author><name sortKey="Evans, Christopher F" sort="Evans, Christopher F" uniqKey="Evans C" first="Christopher F" last="Evans">Christopher F. Evans</name><affiliation wicri:level="1"><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></affiliation></author><author><name sortKey="Macinnis, Robert J" sort="Macinnis, Robert J" uniqKey="Macinnis R" first="Robert J" last="Macinnis">Robert J. Macinnis</name><affiliation wicri:level="1"><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></affiliation><affiliation wicri:level="1"><nlm:aff id="A2">Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne</wicri:regionArea></affiliation></author><author><name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G" last="Giles">Graham G. Giles</name><affiliation wicri:level="1"><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></affiliation><affiliation wicri:level="1"><nlm:aff id="A2">Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="A5">Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Department of Epidemiology and Preventive Medicine, Monash University, Melbourne</wicri:regionArea></affiliation></author><author><name sortKey="Apicella, Carmel" sort="Apicella, Carmel" uniqKey="Apicella C" first="Carmel" last="Apicella">Carmel Apicella</name><affiliation wicri:level="1"><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></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="1"><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></affiliation><affiliation wicri:level="1"><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></affiliation><affiliation wicri:level="1"><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></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><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9200608</journal-id><journal-id journal-id-type="pubmed-jr-id">2299</journal-id><journal-id journal-id-type="nlm-ta">Cancer Epidemiol Biomarkers Prev</journal-id><journal-id journal-id-type="iso-abbrev">Cancer Epidemiol. Biomarkers Prev.</journal-id><journal-title-group><journal-title>Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology</journal-title></journal-title-group><issn pub-type="ppub">1055-9965</issn><issn pub-type="epub">1538-7755</issn></journal-meta><article-meta><article-id pub-id-type="pmid">28062399</article-id><article-id pub-id-type="pmc">5380555</article-id><article-id pub-id-type="doi">10.1158/1055-9965.EPI-16-0499</article-id><article-id pub-id-type="manuscript">NIHMS838141</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Longitudinal study of mammographic density measures that predict breast cancer risk</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="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Baglietto</surname><given-names>Laura</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref><xref ref-type="aff" rid="A6">6</xref><xref ref-type="aff" rid="A7">7</xref></contrib><contrib contrib-type="author"><name><surname>Stone</surname><given-names>Jennifer</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A3">3</xref></contrib><contrib contrib-type="author"><name><surname>Simpson</surname><given-names>Julie A</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Severi</surname><given-names>Gianluca</given-names></name><xref ref-type="aff" rid="A4">4</xref></contrib><contrib contrib-type="author"><name><surname>Evans</surname><given-names>Christopher F</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>MacInnis</surname><given-names>Robert J</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Giles</surname><given-names>Graham G</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref><xref ref-type="aff" rid="A5">5</xref></contrib><contrib contrib-type="author"><name><surname>Apicella</surname><given-names>Carmel</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>Hopper</surname><given-names>John L</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A8">8</xref><xref ref-type="aff" rid="A9">9</xref></contrib></contrib-group><aff id="A1"><label>1</label>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Australia</aff><aff id="A2"><label>2</label>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia</aff><aff id="A3"><label>3</label>Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia</aff><aff id="A4"><label>4</label>Human Genetics Foundation, Torino, Italy</aff><aff id="A5"><label>5</label>Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia</aff><aff id="A6"><label>6</label>CESP - U1018 Inserm, Facultés de medicine Université Paris-Sud, UVSQ, Université Paris-Saclay, Gustave Roussy, 94805, Villejuif, France</aff><aff id="A7"><label>7</label>Department of Clinical and Experimental Medicine, University of Pisa, Italy</aff><aff id="A8"><label>8</label>Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea</aff><aff id="A9"><label>9</label>Institute of Health and Environment, Seoul National University, Seoul, Korea</aff><author-notes><corresp id="FN1">Author for correspondence: Professor John L. Hopper, University of Melbourne, School of Population and Global Health, Level 3, 207 Bouverie Street, Carlton, Victoria 3053, Australia, Tel: +61 3 8344 0697, Fax: +61 3 9349 5815, <email>j.hopper@unimelb.edu.au</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>29</day><month>12</month><year>2016</year></pub-date><pub-date pub-type="epub"><day>06</day><month>1</month><year>2017</year></pub-date><pub-date pub-type="ppub"><month>4</month><year>2017</year></pub-date><pub-date pub-type="pmc-release"><day>01</day><month>10</month><year>2017</year></pub-date><volume>26</volume><issue>4</issue><fpage>651</fpage><lpage>660</lpage><pmc-comment>elocation-id from pubmed: 10.1158/1055-9965.EPI-16-0499</pmc-comment>
<abstract><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></abstract><kwd-group><kwd>mammographic density</kwd><kwd>breast cancer</kwd><kwd>longitudinal</kwd><kwd>tracking</kwd><kwd>correlation</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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