Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm
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 reson...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Breast Cancer |
| Online Access: | http://dx.doi.org/10.1155/2014/961679 |
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| author | Fatima Nayeem Hyunsu Ju Donald G. Brunder Manubai Nagamani Karl E. Anderson Tuenchit Khamapirad Lee-Jane W. Lu |
| author_facet | Fatima Nayeem Hyunsu Ju Donald G. Brunder Manubai Nagamani Karl E. Anderson Tuenchit Khamapirad Lee-Jane W. Lu |
| author_sort | Fatima Nayeem |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7f4808d261f7436ea7a387dcbf8897d1 |
| institution | Kabale University |
| issn | 2090-3170 2090-3189 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Breast Cancer |
| spelling | doaj-art-7f4808d261f7436ea7a387dcbf8897d12025-02-03T01:26:05ZengWileyInternational Journal of Breast Cancer2090-31702090-31892014-01-01201410.1155/2014/961679961679Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical AlgorithmFatima Nayeem0Hyunsu Ju1Donald G. Brunder2Manubai Nagamani3Karl E. Anderson4Tuenchit Khamapirad5Lee-Jane W. Lu6Division of Human Nutrition, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, 700 Harborside Drive, Galveston, TX 77555-1109, USADivision of Biostatistics, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77550-1147, USADepartment of Academic Computing, The University of Texas Medical Branch, Galveston, TX 77555-1035, USADepartment of Obstetrics and Gynecology, The University of Texas Medical Branch, Galveston, TX 77555, USADivision of Human Nutrition, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, 700 Harborside Drive, Galveston, TX 77555-1109, USADepartment of Radiology, The University of Texas Medical Branch, Galveston, TX 77555, USADivision of Human Nutrition, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, 700 Harborside Drive, Galveston, TX 77555-1109, USAWomen 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/10.1155/2014/961679 |
| spellingShingle | Fatima Nayeem Hyunsu Ju Donald G. Brunder Manubai Nagamani Karl E. Anderson Tuenchit Khamapirad Lee-Jane W. Lu Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm International Journal of Breast Cancer |
| title | Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm |
| title_full | Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm |
| title_fullStr | Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm |
| title_full_unstemmed | Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm |
| title_short | Similarity of Fibroglandular Breast Tissue Content Measured from Magnetic Resonance and Mammographic Images and by a Mathematical Algorithm |
| title_sort | similarity of fibroglandular breast tissue content measured from magnetic resonance and mammographic images and by a mathematical algorithm |
| url | http://dx.doi.org/10.1155/2014/961679 |
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