Literature DB >> 15125012

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images.

Jun Wei1, Heang-Ping Chan, Mark A Helvie, Marilyn A Roubidoux, Berkman Sahiner, Lubomir M Hadjiiski, Chuan Zhou, Sophie Paquerault, Thomas Chenevert, Mitchell M Goodsitt.   

Abstract

Previous studies have found that mammographic breast density is highly correlated with breast cancer risk. Therefore, mammographic breast density may be considered as an important risk factor in studies of breast cancer treatments. In this paper, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A dataset of 67 cases having MR images (coronal 3-D SPGR T1-weighted pre-contrast) and corresponding 4-view mammograms was used in this study. Mammographic breast density was estimated by an experienced radiologist and an automated image analysis tool, Mammography Density ESTimator (MDEST) developed previously in our laboratory. For the estimation of the percent volume of fibroglandular tissue in breast MR images, a semiautomatic method was developed to segment the fibroglandular tissue from each slice. The tissue volume was calculated by integration over all slices containing the breast. Interobserver variation was measured for 3 different readers. It was found that the correlation between every two of the three readers for segmentation of MR volumetric fibroglandular tissue was 0.99. The correlations between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by an experienced radiologist were both 0.91. The correlation between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by MDEST was 0.91 and 0.89, respectively. The root-mean-square (rms) residual ranged from 5.4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast.

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Year:  2004        PMID: 15125012     DOI: 10.1118/1.1668512

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  54 in total

1.  Consistency of breast density measured from the same women using different MR scanners.

Authors:  J-H Chen; S Chan; D H-E Chang; M Lin; M-Y Su
Journal:  Ann Oncol       Date:  2011-10-19       Impact factor: 32.976

2.  Comparison of breast density measured on MR images acquired using fat-suppressed versus nonfat-suppressed sequences.

Authors:  Daniel H-E Chang; Jeon-Hor Chen; Muqing Lin; Shadfar Bahri; Hon J Yu; Rita S Mehta; Ke Nie; David J B Hsiang; Orhan Nalcioglu; Min-Ying Su
Journal:  Med Phys       Date:  2011-11       Impact factor: 4.071

Review 3.  Breast tissue composition and susceptibility to breast cancer.

Authors:  Norman F Boyd; Lisa J Martin; Michael Bronskill; Martin J Yaffe; Neb Duric; Salomon Minkin
Journal:  J Natl Cancer Inst       Date:  2010-07-08       Impact factor: 13.506

4.  Initial clinical experience with microwave breast imaging in women with normal mammography.

Authors:  Paul M Meaney; Margaret W Fanning; Timothy Raynolds; Colleen J Fox; Qianqian Fang; Christine A Kogel; Steven P Poplack; Keith D Paulsen
Journal:  Acad Radiol       Date:  2007-02       Impact factor: 3.173

5.  Classification of breast computed tomography data.

Authors:  Thomas R Nelson; Laura I Cerviño; John M Boone; Karen K Lindfors
Journal:  Med Phys       Date:  2008-03       Impact factor: 4.071

6.  Mammographic density, MRI background parenchymal enhancement and breast cancer risk.

Authors:  M C Pike; C L Pearce
Journal:  Ann Oncol       Date:  2013-11       Impact factor: 32.976

7.  Differences in breast density assessment using mammography, tomosynthesis and MRI and their implications for practice.

Authors:  A Tagliafico; G Tagliafico; N Houssami
Journal:  Br J Radiol       Date:  2013-10-28       Impact factor: 3.039

8.  Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Authors:  Songfeng Li; Jun Wei; Heang-Ping Chan; Mark A Helvie; Marilyn A Roubidoux; Yao Lu; Chuan Zhou; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Phys Med Biol       Date:  2018-01-09       Impact factor: 3.609

9.  Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI.

Authors:  Ke Nie; Jeon-Hor Chen; Siwa Chan; Man-Kwun I Chau; Hon J Yu; Shadfar Bahri; Tiffany Tseng; Orhan Nalcioglu; Min-Ying Su
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

10.  Dietary Fat Intake During Adolescence and Breast Density Among Young Women.

Authors:  Seungyoun Jung; Olga Goloubeva; Catherine Klifa; Erin S LeBlanc; Linda G Snetselaar; Linda Van Horn; Joanne F Dorgan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2016-05-19       Impact factor: 4.254

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