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dc.contributor.authorBusana, MC
dc.contributor.authorEng, A
dc.contributor.authorDenholm, R
dc.contributor.authorDowsett, M
dc.contributor.authorVinnicombe, S
dc.contributor.authorAllen, S
dc.contributor.authorDos-Santos-Silva, I
dc.date.accessioned2017-02-28T10:26:55Z
dc.date.issued2016-09-26
dc.identifier.citationBreast cancer research : BCR, 2016, 18 (1), pp. 96 - ?
dc.identifier.issn1465-5411
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/408
dc.identifier.eissn1465-542X
dc.identifier.doi10.1186/s13058-016-0756-7
dc.description.abstractBackground Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known.Methods We compared the performance of the semi-automated Cumulus method on digital raw, "analogue-like" raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk.Results Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and "analogue-like" raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64).Conclusions The findings showed that Cumulus can be used to measure density on all types of digital images. They also indicate that LIBRA may provide a valid fully automated alternative to the more labour-intensive Cumulus. However, the same digital image type and assessment method should be used when examining mammographic density across populations, or longitudinal changes in density within a single population.
dc.formatElectronic
dc.format.extent96 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectMammography
dc.subjectOdds Ratio
dc.subjectRisk Assessment
dc.subjectRisk Factors
dc.subjectCase-Control Studies
dc.subjectImage Processing, Computer-Assisted
dc.subjectAged
dc.subjectMiddle Aged
dc.subjectFemale
dc.subjectBreast Density
dc.titleImpact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study.
dc.typeJournal Article
dcterms.dateAccepted2016-09-08
rioxxterms.versionofrecord10.1186/s13058-016-0756-7
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2016-09-26
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBreast cancer research : BCR
pubs.issue1
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology/Endocrinology (hon.)
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology/Endocrinology (hon.)
pubs.publication-statusPublished
pubs.volume18
pubs.embargo.termsNo embargo
icr.researchteamEndocrinologyen_US
dc.contributor.icrauthorDowsett, Mitchen


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