Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.
Identifieur interne : 001D78 ( PubMed/Corpus ); précédent : 001D77; suivant : 001D79Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.
Auteurs : Kavitha Krishnan ; Laura Baglietto ; Carmel Apicella ; Jennifer Stone ; Melissa C. Southey ; Dallas R. English ; Graham G. Giles ; John L. HopperSource :
- Breast cancer research : BCR [ 1465-542X ] ; 2016.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Breast Neoplasms.
- methods : Mammography.
- pathology : Breast Neoplasms.
- Adult, Aged, Body Mass Index, Breast Density, Case-Control Studies, Early Detection of Cancer, Female, Humans, Incidence, Middle Aged, Risk, Risk Factors, Tumor Burden.
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.
DOI: 10.1186/s13058-016-0722-4
PubMed: 27316945
Links to Exploration step
pubmed:27316945Le document en format XML
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Southey</name><affiliation><nlm:affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="English, Dallas R" sort="English, Dallas R" uniqKey="English D" first="Dallas R" last="English">Dallas R. English</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></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><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></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><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia. j.hopper@unimelb.edu.au.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27316945</idno><idno type="pmid">27316945</idno><idno type="doi">10.1186/s13058-016-0722-4</idno><idno type="wicri:Area/PubMed/Corpus">001D78</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001D78</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.</title><author><name sortKey="Krishnan, Kavitha" sort="Krishnan, Kavitha" uniqKey="Krishnan K" first="Kavitha" last="Krishnan">Kavitha Krishnan</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Baglietto, Laura" sort="Baglietto, Laura" uniqKey="Baglietto L" first="Laura" last="Baglietto">Laura Baglietto</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Apicella, Carmel" sort="Apicella, Carmel" uniqKey="Apicella C" first="Carmel" last="Apicella">Carmel Apicella</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Stone, Jennifer" sort="Stone, Jennifer" uniqKey="Stone J" first="Jennifer" last="Stone">Jennifer Stone</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Southey, Melissa C" sort="Southey, Melissa C" uniqKey="Southey M" first="Melissa C" last="Southey">Melissa C. Southey</name><affiliation><nlm:affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="English, Dallas R" sort="English, Dallas R" uniqKey="English D" first="Dallas R" last="English">Dallas R. English</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></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><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</nlm:affiliation></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><nlm:affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia. j.hopper@unimelb.edu.au.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Breast cancer research : BCR</title><idno type="eISSN">1465-542X</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Aged</term><term>Body Mass Index</term><term>Breast Density</term><term>Breast Neoplasms (epidemiology)</term><term>Breast Neoplasms (pathology)</term><term>Case-Control Studies</term><term>Early Detection of Cancer</term><term>Female</term><term>Humans</term><term>Incidence</term><term>Mammography (methods)</term><term>Middle Aged</term><term>Risk</term><term>Risk Factors</term><term>Tumor Burden</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Mammography</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Body Mass Index</term><term>Breast Density</term><term>Case-Control Studies</term><term>Early Detection of Cancer</term><term>Female</term><term>Humans</term><term>Incidence</term><term>Middle Aged</term><term>Risk</term><term>Risk Factors</term><term>Tumor Burden</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27316945</PMID><DateCreated><Year>2016</Year><Month>06</Month><Day>18</Day></DateCreated><DateCompleted><Year>2017</Year><Month>08</Month><Day>07</Day></DateCompleted><DateRevised><Year>2017</Year><Month>08</Month><Day>07</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1465-542X</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>1</Issue><PubDate><Year>2016</Year><Month>Jun</Month><Day>18</Day></PubDate></JournalIssue><Title>Breast cancer research : BCR</Title><ISOAbbreviation>Breast Cancer Res.</ISOAbbreviation></Journal><ArticleTitle>Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.</ArticleTitle><Pagination><MedlinePgn>63</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s13058-016-0722-4</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="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.</AbstractText><AbstractText Label="METHODS" NlmCategory="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.</AbstractText><AbstractText Label="RESULTS" NlmCategory="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.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="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.</AbstractText></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, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, 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, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Gustave Roussy, F-94805, Villejuif, France.</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, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</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, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Crawley, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Southey</LastName><ForeName>Melissa C</ForeName><Initials>MC</Initials><AffiliationInfo><Affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>English</LastName><ForeName>Dallas R</ForeName><Initials>DR</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, 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, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, 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>Hopper</LastName><ForeName>John L</ForeName><Initials>JL</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia. j.hopper@unimelb.edu.au.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea. j.hopper@unimelb.edu.au.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute of Health and Environment, Seoul National University, Seoul, Korea. j.hopper@unimelb.edu.au.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>06</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Breast Cancer Res</MedlineTA><NlmUniqueID>100927353</NlmUniqueID><ISSNLinking>1465-5411</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Clin Oncol. 2015 Mar 20;33(9):1030-7</RefSource><PMID Version="1">25646195</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Trends Endocrinol Metab. 2012 Feb;23(2):83-9</RefSource><PMID 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Version="1">23360535</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015992" MajorTopicYN="N">Body Mass Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000071060" MajorTopicYN="Y">Breast Density</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001943" MajorTopicYN="N">Breast Neoplasms</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000473" MajorTopicYN="Y">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016022" MajorTopicYN="N">Case-Control Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055088" MajorTopicYN="N">Early Detection of Cancer</DescriptorName></MeshHeading><MeshHeading><DescriptorName 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