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dc.contributor.authorBurnside, Elizabeth S
dc.contributor.authorWarren, Lucy M
dc.contributor.authorMyles, Jonathan
dc.contributor.authorWilkinson, Louise S
dc.contributor.authorWallis, Matthew
dc.contributor.authorPatel, Mishal
dc.contributor.authorSmith, Robert A
dc.contributor.authorYoung, Kenneth C
dc.contributor.authorMassat, Nathalie J
dc.contributor.authorDuffy, Stephen W
dc.date.accessioned2021-09-14T15:41:02Z
dc.date.available2021-09-14T15:41:02Z
dc.date.issued2021-09
dc.date.submitted2020-11-07
dc.identifier.issn0007-0920
dc.identifier.others41416-021-01466-y
dc.identifier.other1466
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/327992
dc.descriptionFunder: Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601
dc.descriptionFunder: American Cancer Society NHPDCSGBR-GBRLONG Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601
dc.description.abstractBACKGROUND: This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers. METHODS: This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls. RESULTS: FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001). CONCLUSION: FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectArticle
dc.subject/631/67/1347
dc.subject/692/499
dc.subjectarticle
dc.titleQuantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study.
dc.typeArticle
dc.date.updated2021-09-14T15:41:01Z
prism.endingPage892
prism.issueIdentifier6
prism.publicationNameBr J Cancer
prism.startingPage884
prism.volume125
dc.identifier.doi10.17863/CAM.75443
dcterms.dateAccepted2021-06-10
rioxxterms.versionofrecord10.1038/s41416-021-01466-y
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBurnside, Elizabeth S [0000-0002-6600-435X]
dc.contributor.orcidSmith, Robert A [0000-0003-3344-2238]
dc.contributor.orcidMassat, Nathalie J [0000-0002-1095-994X]
dc.contributor.orcidDuffy, Stephen W [0000-0003-4901-7922]
dc.identifier.eissn1532-1827
pubs.funder-project-idFoundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) (K24CA194251)
cam.issuedOnline2021-06-24


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