Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk.
Identifieur interne : 000E75 ( PubMed/Curation ); précédent : 000E74; suivant : 000E76Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk.
Auteurs : Kavitha Krishnan [Australie] ; Laura Baglietto [Australie] ; 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]Source :
- Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology [ 1538-7755 ] ; 2017.
Abstract
Background: After adjusting for age and body mass index (BMI), mammographic measures-dense area (DA), percent dense area (PDA), and nondense 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 normalized using the Box-Cox method. Correlations in the normalized risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modeling of Gompertz curves.Results: Mean normalized 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 onward. Mean normalized NDA increased nonlinearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalized 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.Conclusions: 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. Cancer Epidemiol Biomarkers Prev; 26(4); 651-60. ©2017 AACR.
DOI: 10.1158/1055-9965.EPI-16-0499
PubMed: 28062399
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Hopper</name><affiliation wicri:level="1"><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. j.hopper@unimelb.edu.au.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:28062399</idno><idno type="pmid">28062399</idno><idno type="doi">10.1158/1055-9965.EPI-16-0499</idno><idno type="wicri:Area/PubMed/Corpus">000E78</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000E78</idno><idno type="wicri:Area/PubMed/Curation">000E75</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000E75</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk.</title><author><name sortKey="Krishnan, Kavitha" sort="Krishnan, Kavitha" uniqKey="Krishnan K" first="Kavitha" last="Krishnan">Kavitha Krishnan</name><affiliation wicri:level="1"><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</wicri:regionArea></affiliation></author><author><name sortKey="Baglietto, Laura" sort="Baglietto, Laura" uniqKey="Baglietto L" first="Laura" last="Baglietto">Laura Baglietto</name><affiliation wicri:level="1"><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</wicri:regionArea></affiliation></author><author><name sortKey="Stone, Jennifer" sort="Stone, Jennifer" uniqKey="Stone J" first="Jennifer" last="Stone">Jennifer Stone</name><affiliation wicri:level="1"><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</wicri:regionArea></affiliation></author><author><name sortKey="Simpson, Julie A" sort="Simpson, Julie A" uniqKey="Simpson J" first="Julie A" last="Simpson">Julie A. Simpson</name><affiliation wicri:level="1"><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</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:affiliation>Human Genetics Foundation, Torino, Italy.</nlm:affiliation><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:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</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:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</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:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</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:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</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:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. j.hopper@unimelb.edu.au.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria</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="eISSN">1538-7755</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Background: After adjusting for age and body mass index (BMI), mammographic measures-dense area (DA), percent dense area (PDA), and nondense 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 normalized using the Box-Cox method. Correlations in the normalized risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modeling of Gompertz curves.Results: Mean normalized 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 onward. Mean normalized NDA increased nonlinearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalized 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.Conclusions: 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. Cancer Epidemiol Biomarkers Prev; 26(4); 651-60. ©2017 AACR.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">28062399</PMID><DateCreated><Year>2017</Year><Month>01</Month><Day>07</Day></DateCreated><DateRevised><Year>2017</Year><Month>10</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1538-7755</ISSN><JournalIssue CitedMedium="Internet"><Volume>26</Volume><Issue>4</Issue><PubDate><Year>2017</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology</Title><ISOAbbreviation>Cancer Epidemiol. Biomarkers Prev.</ISOAbbreviation></Journal><ArticleTitle>Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk.</ArticleTitle><Pagination><MedlinePgn>651-660</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1158/1055-9965.EPI-16-0499</ELocationID><Abstract><AbstractText>Background: After adjusting for age and body mass index (BMI), mammographic measures-dense area (DA), percent dense area (PDA), and nondense 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 normalized using the Box-Cox method. Correlations in the normalized risk-predicting measures over time intervals of different lengths were estimated using nonlinear mixed-effects modeling of Gompertz curves.Results: Mean normalized 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 onward. Mean normalized NDA increased nonlinearly with age. After adjusting for age and BMI, the within-woman correlation estimates for normalized 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.Conclusions: 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. Cancer Epidemiol Biomarkers Prev; 26(4); 651-60. ©2017 AACR.</AbstractText><CopyrightInformation>©2017 American Association for Cancer Research.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Krishnan</LastName><ForeName>Kavitha</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Baglietto</LastName><ForeName>Laura</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CESP, INSERM, Facultés de Medicine Université Paris-Sud, Villejuif, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Clinical and Experimental Medicine, University of Pisa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stone</LastName><ForeName>Jennifer</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Simpson</LastName><ForeName>Julie A</ForeName><Initials>JA</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Severi</LastName><ForeName>Gianluca</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Human Genetics Foundation, Torino, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Evans</LastName><ForeName>Christopher F</ForeName><Initials>CF</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>MacInnis</LastName><ForeName>Robert J</ForeName><Initials>RJ</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Giles</LastName><ForeName>Graham G</ForeName><Initials>GG</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Apicella</LastName><ForeName>Carmel</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hopper</LastName><ForeName>John L</ForeName><Initials>JL</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. j.hopper@unimelb.edu.au.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute of Health and Environment, Seoul National University, Seoul, Korea.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>UM1 CA164920</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>01</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Cancer Epidemiol Biomarkers Prev</MedlineTA><NlmUniqueID>9200608</NlmUniqueID><ISSNLinking>1055-9965</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Menopause. 2007 Mar-Apr;14 (2):208-15</RefSource><PMID Version="1">17091098</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Am J Epidemiol. 2008 May 1;167(9):1027-36</RefSource><PMID Version="1">18385204</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Am J Epidemiol. 2014 Feb 15;179(4):475-83</RefSource><PMID Version="1">24169466</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Breast. 2004 Feb;13(1):56-60</RefSource><PMID Version="1">14759717</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>IARC Sci Publ. 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|type= RBID
|clé= pubmed:28062399
|texte= Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk.
}}
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HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Curation/RBID.i -Sk "pubmed:28062399" \
| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Curation/biblio.hfd \
| NlmPubMed2Wicri -a AustralieFrV1
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