@article{c2f99bcc44864d9aaa6f694e11b5e8be,
title = "Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm",
abstract = "Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD.",
author = "Fatima Nayeem and Hyunsu Ju and Brunder, {Donald G.} and Manubai Nagamani and Anderson, {Karl E.} and Tuenchit Khamapirad and Lu, {Lee Jane W.}",
note = "Funding Information: http://orcid.org/0000-0001-7339-4934 Nayeem Fatima fanayeem@utmb.edu 1 Ju Hyunsu hyju@utmb.edu 2 Brunder Donald G. dbrunder@utmb.edu 3 http://orcid.org/0000-0003-0172-6498 Nagamani Manubai mnagaman@gmail.com 4, 5 Anderson Karl E. kanderso@utmb.edu 1 Khamapirad Tuenchit tkhamapirad@houstonmethodist.org 6, 7 Lu Lee-Jane W. llu@utmb.edu 1 Fentiman Ian S. 1 Division of Human Nutrition Department of Preventive Medicine and Community Health The University of Texas Medical Branch 700 Harborside Drive Galveston, TX 77555-1109 USA utmb.edu 2 Division of Biostatistics Department of Preventive Medicine and Community Health The University of Texas Medical Branch Galveston, TX 77550-1147 USA utmb.edu 3 Department of Academic Computing The University of Texas Medical Branch Galveston, TX 77555-1035 USA utmb.edu 4 Department of Obstetrics and Gynecology The University of Texas Medical Branch Galveston TX 77555 USA utmb.edu 5 Houston Bay Area Fertility Center 9C Professional Park Drive Webster, TX 77598 USA houstonbayareafertilitycenter.com 6 Department of Radiology The University of Texas Medical Branch Galveston TX 77555 USA utmb.edu 7 Breast Center The Methodist Willowbrook Hospital Houston, TX 77070 USA houstonmethodist.org 2014 15 7 2014 2014 15 04 2014 02 06 2014 03 06 2014 15 7 2014 2014 Copyright {\textcopyright} 2014 Fatima Nayeem et al. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD. http://dx.doi.org/10.13039/100000182 Medical Research and Materiel Command, U.S. Army Medical Department DADM17-01-1-0417 http://dx.doi.org/10.13039/100000002 National Institutes of Health CA95545 http://dx.doi.org/10.13039/100000002 National Institutes of Health CA65628 http://dx.doi.org/10.13039/100006108 National Center for Advancing Translational Sciences UL1TR000071 http://dx.doi.org/10.13039/100000066 National Institute of Environmental Health Sciences 2 P30 ES06676 ",
year = "2014",
doi = "10.1155/2014/961679",
language = "English (US)",
volume = "2014",
journal = "International Journal of Breast Cancer",
issn = "2090-3170",
publisher = "Hindawi Publishing Corporation",
}