Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0110690
Title: Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer
Authors: Cheddad A.
Czene K.
Eriksson M.
Li J. 
Easton D.
Hall P.
Humphreys K.
Keywords: adult
Article
breast cancer
cancer risk
controlled study
digital mammography
female
gene
genetic association
genetic variability
human
image analysis
image processing
mammography system
prediction
risk assessment
volumetry
ZNF365 gene
aged
breast tumor
clinical trial
genetics
image processing
male
mammography
middle aged
procedures
radiography
risk factor
DNA binding protein
transcription factor
ZNF365 protein, human
Adult
Aged
Breast Neoplasms
DNA-Binding Proteins
Female
Humans
Image Processing, Computer-Assisted
Male
Mammography
Middle Aged
Risk Factors
Transcription Factors
Issue Date: 2014
Citation: Cheddad 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
Rights: Attribution 4.0 International
Abstract: Introduction: 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.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/161766
ISSN: 19326203
DOI: 10.1371/journal.pone.0110690
Rights: Attribution 4.0 International
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