Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0110690
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dc.titleArea and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer
dc.contributor.authorCheddad A.
dc.contributor.authorCzene K.
dc.contributor.authorEriksson M.
dc.contributor.authorLi J.
dc.contributor.authorEaston D.
dc.contributor.authorHall P.
dc.contributor.authorHumphreys K.
dc.date.accessioned2019-11-07T05:06:14Z
dc.date.available2019-11-07T05:06:14Z
dc.date.issued2014
dc.identifier.citationCheddad A., Czene K., Eriksson M., Li J., Easton D., Hall P., Humphreys K. (2014). Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer. PLoS ONE 9 (10) : e110690. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0110690
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161766
dc.description.abstractIntroduction: Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.Methods: The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.Results: All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p< 1×10-6 for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).Conclusions: Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association. © 2014 Cheddad et al.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectadult
dc.subjectArticle
dc.subjectbreast cancer
dc.subjectcancer risk
dc.subjectcontrolled study
dc.subjectdigital mammography
dc.subjectfemale
dc.subjectgene
dc.subjectgenetic association
dc.subjectgenetic variability
dc.subjecthuman
dc.subjectimage analysis
dc.subjectimage processing
dc.subjectmammography system
dc.subjectprediction
dc.subjectrisk assessment
dc.subjectvolumetry
dc.subjectZNF365 gene
dc.subjectaged
dc.subjectbreast tumor
dc.subjectclinical trial
dc.subjectgenetics
dc.subjectimage processing
dc.subjectmale
dc.subjectmammography
dc.subjectmiddle aged
dc.subjectprocedures
dc.subjectradiography
dc.subjectrisk factor
dc.subjectDNA binding protein
dc.subjecttranscription factor
dc.subjectZNF365 protein, human
dc.subjectAdult
dc.subjectAged
dc.subjectBreast Neoplasms
dc.subjectDNA-Binding Proteins
dc.subjectFemale
dc.subjectHumans
dc.subjectImage Processing, Computer-Assisted
dc.subjectMale
dc.subjectMammography
dc.subjectMiddle Aged
dc.subjectRisk Factors
dc.subjectTranscription Factors
dc.typeArticle
dc.contributor.departmentDEPT OF SURGERY
dc.description.doi10.1371/journal.pone.0110690
dc.description.sourcetitlePLoS ONE
dc.description.volume9
dc.description.issue10
dc.description.pagee110690
dc.published.statePublished
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