Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-10

AUTHORS

Amanda Eng, Zoe Gallant, John Shepherd, Valerie McCormack, Jingmei Li, Mitch Dowsett, Sarah Vinnicombe, Steve Allen, Isabel dos-Santos-Silva

ABSTRACT

INTRODUCTION: Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM). METHODS: The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables. RESULTS: Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65). CONCLUSIONS: Fully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments. More... »

PAGES

439

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1

DOI

http://dx.doi.org/10.1186/s13058-014-0439-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003025389

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25239205


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Breast Density", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Breast Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Case-Control Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mammary Glands, Human", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Factors", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Massey University", 
          "id": "https://www.grid.ac/institutes/grid.148374.d", 
          "name": [
            "Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK", 
            "Centre for Public Health Research, Massey University, Wellington, New Zealand"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Eng", 
        "givenName": "Amanda", 
        "id": "sg:person.01175420503.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01175420503.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "London School of Hygiene & Tropical Medicine", 
          "id": "https://www.grid.ac/institutes/grid.8991.9", 
          "name": [
            "Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gallant", 
        "givenName": "Zoe", 
        "id": "sg:person.01140440740.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01140440740.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California, San Francisco", 
          "id": "https://www.grid.ac/institutes/grid.266102.1", 
          "name": [
            "Radiology and Biomedical Imaging, University of California, San Francisco, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shepherd", 
        "givenName": "John", 
        "id": "sg:person.01054355272.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054355272.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "International Agency For Research On Cancer", 
          "id": "https://www.grid.ac/institutes/grid.17703.32", 
          "name": [
            "Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McCormack", 
        "givenName": "Valerie", 
        "id": "sg:person.01367601343.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01367601343.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Genome Institute of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.418377.e", 
          "name": [
            "Human Genetics, Genome Institute of Singapore, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Jingmei", 
        "id": "sg:person.013760043547.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013760043547.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royal Marsden Hospital", 
          "id": "https://www.grid.ac/institutes/grid.424926.f", 
          "name": [
            "Academic Biochemistry, Royal Marsden Hospital, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dowsett", 
        "givenName": "Mitch", 
        "id": "sg:person.015776405257.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015776405257.84"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Division of Imaging and Technology, Ninewells Hospital Medical School, University of Dundee, Dundee, UK", 
            "Director of the Central and East London Breast Screening Service, CELBSS, Dundee, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vinnicombe", 
        "givenName": "Sarah", 
        "id": "sg:person.01201207612.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201207612.36"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royal Marsden NHS Foundation Trust", 
          "id": "https://www.grid.ac/institutes/grid.5072.0", 
          "name": [
            "Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Allen", 
        "givenName": "Steve", 
        "id": "sg:person.0646015100.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0646015100.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "London School of Hygiene & Tropical Medicine", 
          "id": "https://www.grid.ac/institutes/grid.8991.9", 
          "name": [
            "Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "dos-Santos-Silva", 
        "givenName": "Isabel", 
        "id": "sg:person.01230144105.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230144105.36"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-10-1150", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006543075"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr3238", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006829229", 
          "https://doi.org/10.1186/bcr3238"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr3238", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006829229", 
          "https://doi.org/10.1186/bcr3238"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.breast.2012.01.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009559360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ijc.25053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009604327"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ijc.25053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009604327"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djh106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011856821"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/92.13.1081", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013822681"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2796.2012.02525.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014798737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13244-011-0139-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016008868", 
          "https://doi.org/10.1007/s13244-011-0139-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s13244-011-0139-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016008868", 
          "https://doi.org/10.1007/s13244-011-0139-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2512081235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016130370"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr1829", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020053537", 
          "https://doi.org/10.1186/bcr1829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr1829", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020053537", 
          "https://doi.org/10.1186/bcr1829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa062790", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022154822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.1539038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023986662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/bjc.2014.82", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026804871", 
          "https://doi.org/10.1038/bjc.2014.82"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/bjc.2014.82", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026804871", 
          "https://doi.org/10.1038/bjc.2014.82"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr3451", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029534126", 
          "https://doi.org/10.1186/bcr3451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr3451", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029534126", 
          "https://doi.org/10.1186/bcr3451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-11-0423", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032007137"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr3110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032911992", 
          "https://doi.org/10.1186/bcr3110"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/bcr3110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032911992", 
          "https://doi.org/10.1186/bcr3110"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-09-0107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033629005"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-13666-5_55", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035237786", 
          "https://doi.org/10.1007/978-3-642-13666-5_55"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-13666-5_55", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035237786", 
          "https://doi.org/10.1007/978-3-642-13666-5_55"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10549-007-9581-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039114427", 
          "https://doi.org/10.1007/s10549-007-9581-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10549-007-9581-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039114427", 
          "https://doi.org/10.1007/s10549-007-9581-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djr079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040220198"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-09-1059", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040449857"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-07-0085", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042918440"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.acra.2006.06.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045174026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-10-0703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048852802"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1055-9965.epi-06-0034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049006559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djs254", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050861007"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1470-2045(05)70390-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052464959"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/39/10/008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059022537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/56/9/005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059029149"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/87.9.670", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059819855"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2005.862741", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061694821"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-10", 
    "datePublishedReg": "2014-10-01", 
    "description": "INTRODUCTION: Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM).\nMETHODS: The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables.\nRESULTS: Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD\u2009=\u20090.68, P\u2009=\u20090.05), and Quantra slightly lower (AUC\u2009=\u20090.63; P\u2009=\u20090.06), than Cumulus (AUC\u2009=\u20090.65).\nCONCLUSIONS: Fully-automated methods are valid alternatives to the labour-intensive \"gold standard\" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s13058-014-0439-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1022375", 
        "issn": [
          "1465-5411", 
          "1465-542X"
        ], 
        "name": "Breast Cancer Research", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "name": "Digital mammographic density and breast cancer risk: a case\u2013control study of six alternative density assessment methods", 
    "pagination": "439", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "0e68ea315ed192569ed9f6dbcbda1799def208e371a10a54be106fa52cb82500"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25239205"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100927353"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13058-014-0439-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1003025389"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13058-014-0439-1", 
      "https://app.dimensions.ai/details/publication/pub.1003025389"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000367_0000000367/records_88239_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2Fs13058-014-0439-1"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1'


 

This table displays all metadata directly associated to this object as RDF triples.

288 TRIPLES      21 PREDICATES      70 URIs      31 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13058-014-0439-1 schema:about N2247b7be7195439398d08061229e5cf7
2 N3c26039912ea49fe8b41edfb0d0ee43d
3 N5a85ee3689c9438bac9d117cb38d6a9b
4 N7923b91f6a6d464589edbc263ce8704d
5 Naa0885cb31384fc999fbbc65ae913822
6 Nb98e0489196a4793b33873ddb2f03298
7 Nbb54cf23dd6945199e621cc979a27b72
8 Nca316ce0031e44f798488b065168d5f3
9 Ncddc9ec1456a482c8179bfe92951f309
10 Nf5eddaa786284ec6996b5278542bde4c
11 anzsrc-for:11
12 anzsrc-for:1117
13 schema:author N1f0c605e1f0c44a3a2eef9b100e6f9b5
14 schema:citation sg:pub.10.1007/978-3-642-13666-5_55
15 sg:pub.10.1007/s10549-007-9581-0
16 sg:pub.10.1007/s13244-011-0139-7
17 sg:pub.10.1038/bjc.2014.82
18 sg:pub.10.1186/bcr1829
19 sg:pub.10.1186/bcr3110
20 sg:pub.10.1186/bcr3238
21 sg:pub.10.1186/bcr3451
22 https://doi.org/10.1002/ijc.25053
23 https://doi.org/10.1016/j.acra.2006.06.005
24 https://doi.org/10.1016/j.breast.2012.01.005
25 https://doi.org/10.1016/s1470-2045(05)70390-9
26 https://doi.org/10.1056/nejmoa062790
27 https://doi.org/10.1088/0031-9155/39/10/008
28 https://doi.org/10.1088/0031-9155/56/9/005
29 https://doi.org/10.1093/jnci/87.9.670
30 https://doi.org/10.1093/jnci/92.13.1081
31 https://doi.org/10.1093/jnci/djh106
32 https://doi.org/10.1093/jnci/djr079
33 https://doi.org/10.1093/jnci/djs254
34 https://doi.org/10.1109/tmi.2005.862741
35 https://doi.org/10.1111/j.1365-2796.2012.02525.x
36 https://doi.org/10.1118/1.1539038
37 https://doi.org/10.1148/radiol.2512081235
38 https://doi.org/10.1158/1055-9965.epi-06-0034
39 https://doi.org/10.1158/1055-9965.epi-07-0085
40 https://doi.org/10.1158/1055-9965.epi-09-0107
41 https://doi.org/10.1158/1055-9965.epi-09-1059
42 https://doi.org/10.1158/1055-9965.epi-10-0703
43 https://doi.org/10.1158/1055-9965.epi-10-1150
44 https://doi.org/10.1158/1055-9965.epi-11-0423
45 schema:datePublished 2014-10
46 schema:datePublishedReg 2014-10-01
47 schema:description INTRODUCTION: Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM). METHODS: The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables. RESULTS: Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65). CONCLUSIONS: Fully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments.
48 schema:genre research_article
49 schema:inLanguage en
50 schema:isAccessibleForFree true
51 schema:isPartOf Nd1e8c262e28b4fd2a21bf5c677c3dadd
52 Nd3c7149039f049afba792f6c0ed42caf
53 sg:journal.1022375
54 schema:name Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods
55 schema:pagination 439
56 schema:productId N02fae9c74b714805a8c7a788189a0f5a
57 N034bfad6130349ce9cdf98321f9ee67b
58 N5db48291d2ca46668565dd47415ca091
59 N611c1d4ce7c74f179f04bc8bfde6a257
60 Ndb4f005844f448678f1146f989291d73
61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003025389
62 https://doi.org/10.1186/s13058-014-0439-1
63 schema:sdDatePublished 2019-04-11T13:09
64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
65 schema:sdPublisher N713802dc68594cdcbc10957c90472399
66 schema:url http://link.springer.com/10.1186%2Fs13058-014-0439-1
67 sgo:license sg:explorer/license/
68 sgo:sdDataset articles
69 rdf:type schema:ScholarlyArticle
70 N02fae9c74b714805a8c7a788189a0f5a schema:name doi
71 schema:value 10.1186/s13058-014-0439-1
72 rdf:type schema:PropertyValue
73 N034bfad6130349ce9cdf98321f9ee67b schema:name dimensions_id
74 schema:value pub.1003025389
75 rdf:type schema:PropertyValue
76 N1f0c605e1f0c44a3a2eef9b100e6f9b5 rdf:first sg:person.01175420503.51
77 rdf:rest N6e60a05175ff450298ac94ee13620a4a
78 N2247b7be7195439398d08061229e5cf7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Aged
80 rdf:type schema:DefinedTerm
81 N3607b7db950f4f1cbb4c3f0ddcda24a9 rdf:first sg:person.0646015100.02
82 rdf:rest Nb87928a4e5c14f40a63c72444a8ba5ab
83 N3c26039912ea49fe8b41edfb0d0ee43d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Case-Control Studies
85 rdf:type schema:DefinedTerm
86 N4c2868ff1b254536a3e807df3522e69c rdf:first sg:person.015776405257.84
87 rdf:rest N4e12a5dcfcb74eb58eab15dba463598d
88 N4e12a5dcfcb74eb58eab15dba463598d rdf:first sg:person.01201207612.36
89 rdf:rest N3607b7db950f4f1cbb4c3f0ddcda24a9
90 N5a85ee3689c9438bac9d117cb38d6a9b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Breast Neoplasms
92 rdf:type schema:DefinedTerm
93 N5db48291d2ca46668565dd47415ca091 schema:name pubmed_id
94 schema:value 25239205
95 rdf:type schema:PropertyValue
96 N611c1d4ce7c74f179f04bc8bfde6a257 schema:name nlm_unique_id
97 schema:value 100927353
98 rdf:type schema:PropertyValue
99 N6e60a05175ff450298ac94ee13620a4a rdf:first sg:person.01140440740.75
100 rdf:rest Nd2dd821bb6fc4107bb54656968c83c50
101 N713802dc68594cdcbc10957c90472399 schema:name Springer Nature - SN SciGraph project
102 rdf:type schema:Organization
103 N7923b91f6a6d464589edbc263ce8704d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Female
105 rdf:type schema:DefinedTerm
106 N93a6c01334334b44998c9120b36e3bc6 rdf:first sg:person.01367601343.61
107 rdf:rest Ne5160076522b42e78569a44493874311
108 Naa0885cb31384fc999fbbc65ae913822 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Mammary Glands, Human
110 rdf:type schema:DefinedTerm
111 Nb87928a4e5c14f40a63c72444a8ba5ab rdf:first sg:person.01230144105.36
112 rdf:rest rdf:nil
113 Nb98e0489196a4793b33873ddb2f03298 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Middle Aged
115 rdf:type schema:DefinedTerm
116 Nbb54cf23dd6945199e621cc979a27b72 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Humans
118 rdf:type schema:DefinedTerm
119 Nca1ac2790ec94549bea59ea88deac9da schema:name Director of the Central and East London Breast Screening Service, CELBSS, Dundee, UK
120 Division of Imaging and Technology, Ninewells Hospital Medical School, University of Dundee, Dundee, UK
121 rdf:type schema:Organization
122 Nca316ce0031e44f798488b065168d5f3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Breast Density
124 rdf:type schema:DefinedTerm
125 Ncddc9ec1456a482c8179bfe92951f309 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Risk Factors
127 rdf:type schema:DefinedTerm
128 Nd1e8c262e28b4fd2a21bf5c677c3dadd schema:issueNumber 5
129 rdf:type schema:PublicationIssue
130 Nd2dd821bb6fc4107bb54656968c83c50 rdf:first sg:person.01054355272.00
131 rdf:rest N93a6c01334334b44998c9120b36e3bc6
132 Nd3c7149039f049afba792f6c0ed42caf schema:volumeNumber 16
133 rdf:type schema:PublicationVolume
134 Ndb4f005844f448678f1146f989291d73 schema:name readcube_id
135 schema:value 0e68ea315ed192569ed9f6dbcbda1799def208e371a10a54be106fa52cb82500
136 rdf:type schema:PropertyValue
137 Ne5160076522b42e78569a44493874311 rdf:first sg:person.013760043547.76
138 rdf:rest N4c2868ff1b254536a3e807df3522e69c
139 Nf5eddaa786284ec6996b5278542bde4c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Radiographic Image Interpretation, Computer-Assisted
141 rdf:type schema:DefinedTerm
142 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
143 schema:name Medical and Health Sciences
144 rdf:type schema:DefinedTerm
145 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
146 schema:name Public Health and Health Services
147 rdf:type schema:DefinedTerm
148 sg:journal.1022375 schema:issn 1465-5411
149 1465-542X
150 schema:name Breast Cancer Research
151 rdf:type schema:Periodical
152 sg:person.01054355272.00 schema:affiliation https://www.grid.ac/institutes/grid.266102.1
153 schema:familyName Shepherd
154 schema:givenName John
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054355272.00
156 rdf:type schema:Person
157 sg:person.01140440740.75 schema:affiliation https://www.grid.ac/institutes/grid.8991.9
158 schema:familyName Gallant
159 schema:givenName Zoe
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01140440740.75
161 rdf:type schema:Person
162 sg:person.01175420503.51 schema:affiliation https://www.grid.ac/institutes/grid.148374.d
163 schema:familyName Eng
164 schema:givenName Amanda
165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01175420503.51
166 rdf:type schema:Person
167 sg:person.01201207612.36 schema:affiliation Nca1ac2790ec94549bea59ea88deac9da
168 schema:familyName Vinnicombe
169 schema:givenName Sarah
170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201207612.36
171 rdf:type schema:Person
172 sg:person.01230144105.36 schema:affiliation https://www.grid.ac/institutes/grid.8991.9
173 schema:familyName dos-Santos-Silva
174 schema:givenName Isabel
175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230144105.36
176 rdf:type schema:Person
177 sg:person.01367601343.61 schema:affiliation https://www.grid.ac/institutes/grid.17703.32
178 schema:familyName McCormack
179 schema:givenName Valerie
180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01367601343.61
181 rdf:type schema:Person
182 sg:person.013760043547.76 schema:affiliation https://www.grid.ac/institutes/grid.418377.e
183 schema:familyName Li
184 schema:givenName Jingmei
185 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013760043547.76
186 rdf:type schema:Person
187 sg:person.015776405257.84 schema:affiliation https://www.grid.ac/institutes/grid.424926.f
188 schema:familyName Dowsett
189 schema:givenName Mitch
190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015776405257.84
191 rdf:type schema:Person
192 sg:person.0646015100.02 schema:affiliation https://www.grid.ac/institutes/grid.5072.0
193 schema:familyName Allen
194 schema:givenName Steve
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0646015100.02
196 rdf:type schema:Person
197 sg:pub.10.1007/978-3-642-13666-5_55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035237786
198 https://doi.org/10.1007/978-3-642-13666-5_55
199 rdf:type schema:CreativeWork
200 sg:pub.10.1007/s10549-007-9581-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039114427
201 https://doi.org/10.1007/s10549-007-9581-0
202 rdf:type schema:CreativeWork
203 sg:pub.10.1007/s13244-011-0139-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016008868
204 https://doi.org/10.1007/s13244-011-0139-7
205 rdf:type schema:CreativeWork
206 sg:pub.10.1038/bjc.2014.82 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026804871
207 https://doi.org/10.1038/bjc.2014.82
208 rdf:type schema:CreativeWork
209 sg:pub.10.1186/bcr1829 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020053537
210 https://doi.org/10.1186/bcr1829
211 rdf:type schema:CreativeWork
212 sg:pub.10.1186/bcr3110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032911992
213 https://doi.org/10.1186/bcr3110
214 rdf:type schema:CreativeWork
215 sg:pub.10.1186/bcr3238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006829229
216 https://doi.org/10.1186/bcr3238
217 rdf:type schema:CreativeWork
218 sg:pub.10.1186/bcr3451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029534126
219 https://doi.org/10.1186/bcr3451
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1002/ijc.25053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009604327
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1016/j.acra.2006.06.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045174026
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1016/j.breast.2012.01.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009559360
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1016/s1470-2045(05)70390-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052464959
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1056/nejmoa062790 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022154822
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1088/0031-9155/39/10/008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059022537
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1088/0031-9155/56/9/005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059029149
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1093/jnci/87.9.670 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059819855
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1093/jnci/92.13.1081 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013822681
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1093/jnci/djh106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011856821
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1093/jnci/djr079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040220198
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1093/jnci/djs254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050861007
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1109/tmi.2005.862741 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694821
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1111/j.1365-2796.2012.02525.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014798737
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1118/1.1539038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023986662
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1148/radiol.2512081235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016130370
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1158/1055-9965.epi-06-0034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049006559
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1158/1055-9965.epi-07-0085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042918440
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1158/1055-9965.epi-09-0107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033629005
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1158/1055-9965.epi-09-1059 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040449857
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1158/1055-9965.epi-10-0703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048852802
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1158/1055-9965.epi-10-1150 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006543075
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1158/1055-9965.epi-11-0423 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032007137
266 rdf:type schema:CreativeWork
267 https://www.grid.ac/institutes/grid.148374.d schema:alternateName Massey University
268 schema:name Centre for Public Health Research, Massey University, Wellington, New Zealand
269 Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
270 rdf:type schema:Organization
271 https://www.grid.ac/institutes/grid.17703.32 schema:alternateName International Agency For Research On Cancer
272 schema:name Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
273 rdf:type schema:Organization
274 https://www.grid.ac/institutes/grid.266102.1 schema:alternateName University of California, San Francisco
275 schema:name Radiology and Biomedical Imaging, University of California, San Francisco, USA
276 rdf:type schema:Organization
277 https://www.grid.ac/institutes/grid.418377.e schema:alternateName Genome Institute of Singapore
278 schema:name Human Genetics, Genome Institute of Singapore, Singapore, Singapore
279 rdf:type schema:Organization
280 https://www.grid.ac/institutes/grid.424926.f schema:alternateName Royal Marsden Hospital
281 schema:name Academic Biochemistry, Royal Marsden Hospital, London, UK
282 rdf:type schema:Organization
283 https://www.grid.ac/institutes/grid.5072.0 schema:alternateName Royal Marsden NHS Foundation Trust
284 schema:name Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
285 rdf:type schema:Organization
286 https://www.grid.ac/institutes/grid.8991.9 schema:alternateName London School of Hygiene & Tropical Medicine
287 schema:name Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
288 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...