Literature DB >> 19175084

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

Ke Nie1, Jeon-Hor Chen, Siwa Chan, Man-Kwun I Chau, Hon J Yu, Shadfar Bahri, Tiffany Tseng, Orhan Nalcioglu, Min-Ying Su.   

Abstract

Breast density has been established as an independent risk factor associated with the development of breast cancer. It is known that an increase of mammographic density is associated with an increased cancer risk. Since a mammogram is a projection image, different body position, level of compression, and the x-ray intensity may lead to a large variability in the density measurement. Breast MRI provides strong soft tissue contrast between fibroglandular and fatty tissues, and three-dimensional coverage of the entire breast, thus making it suitable for density analysis. To develop the MRI-based method, the first task is to achieve consistency in segmentation of the breast region from the body. The method included an initial segmentation based on body landmarks of each individual woman, followed by fuzzy C-mean (FCM) classification to exclude air and lung tissue, B-spline curve fitting to exclude chest wall muscle, and dynamic searching to exclude skin. Then, within the segmented breast, the adaptive FCM was used for simultaneous bias field correction and fibroglandular tissue segmentation. The intraoperator and interoperator reproducibility was evaluated using 11 selected cases covering a broad spectrum of breast densities with different parenchymal patterns. The average standard deviation for breast volume and percent density measurements was in the range of 3%-4% among three trials of one operator or among three different operators. The body position dependence was also investigated by performing scans of two healthy volunteers, each at five different positions, and found the variation in the range of 3%-4%. These initial results suggest that the technique based on three-dimensional MRI can achieve reasonable consistency to be applied in longitudinal follow-up studies to detect small changes. It may also provide a reliable method for evaluating the change of breast density for risk management of women, or for evaluating the benefits/risks when considering hormonal replacement therapy or chemoprevention.

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Year:  2008        PMID: 19175084      PMCID: PMC2673600          DOI: 10.1118/1.3002306

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


  23 in total

1.  Computerized image analysis: estimation of breast density on mammograms.

Authors:  C Zhou; H P Chan; N Petrick; M A Helvie; M M Goodsitt; B Sahiner; L M Hadjiiski
Journal:  Med Phys       Date:  2001-06       Impact factor: 4.071

2.  Quantitative assessment of mammographic breast density: relationship with breast cancer risk.

Authors:  Jennifer A Harvey; Viktor E Bovbjerg
Journal:  Radiology       Date:  2003-11-14       Impact factor: 11.105

Review 3.  Applications and literature review of the BI-RADS classification.

Authors:  S Obenauer; K P Hermann; E Grabbe
Journal:  Eur Radiol       Date:  2005-01-26       Impact factor: 5.315

4.  A longitudinal investigation of mammographic density: the multiethnic cohort.

Authors:  Gertraud Maskarinec; Ian Pagano; Galina Lurie; Laurence N Kolonel
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2006-04       Impact factor: 4.254

5.  Improved lesion detection in MR mammography: three-dimensional segmentation, moving voxel sampling, and normalized maximum intensity-time ratio entropy.

Authors:  Gökhan Ertaş; H Ozcan Gülçür; Mehtap Tunaci
Journal:  Acad Radiol       Date:  2007-02       Impact factor: 3.173

6.  Volumetric breast density estimation from full-field digital mammograms.

Authors:  Saskia van Engeland; Peter R Snoeren; Henkjan Huisman; Carla Boetes; Nico Karssemeijer
Journal:  IEEE Trans Med Imaging       Date:  2006-03       Impact factor: 10.048

7.  A preliminary study on computerized lesion localization in MR mammography using 3D nMITR maps, multilayer cellular neural networks, and fuzzy c-partitioning.

Authors:  Gokhan Ertas; H Ozcan Gulcur; Mehtap Tunaci; Onur Osman; Osman Nuri Ucan
Journal:  Med Phys       Date:  2008-01       Impact factor: 4.071

8.  Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching.

Authors:  Gökhan Ertaş; H Ozcan Gülçür; Onur Osman; Osman N Uçan; Mehtap Tunaci; Memduh Dursun
Journal:  Comput Biol Med       Date:  2007-09-12       Impact factor: 4.589

9.  A new method for quantitative analysis of mammographic density.

Authors:  Carri K Glide-Hurst; Neb Duric; Peter Littrup
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

10.  Analysis of dynamic MR breast images using a model of contrast enhancement.

Authors:  P Hayton; M Brady; L Tarassenko; N Moore
Journal:  Med Image Anal       Date:  1997-04       Impact factor: 8.545

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  75 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

3.  Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique.

Authors:  Jacob D Shea; Panagiotis Kosmas; Susan C Hagness; Barry D Van Veen
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

Review 4.  Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.

Authors:  Lei Wang; Teodora Chitiboi; Hans Meine; Matthias Günther; Horst K Hahn
Journal:  MAGMA       Date:  2016-01-11       Impact factor: 2.310

5.  Background parenchymal enhancement in the contralateral normal breast of patients undergoing neoadjuvant chemotherapy measured by DCE-MRI.

Authors:  Jeon-Hor Chen; Hon Yu; Muqing Lin; Rita S Mehta; Min-Ying Su
Journal:  Magn Reson Imaging       Date:  2013-08-29       Impact factor: 2.546

6.  Double-Blind Randomized 12-Month Soy Intervention Had No Effects on Breast MRI Fibroglandular Tissue Density or Mammographic Density.

Authors:  Anna H Wu; Darcy Spicer; Agustin Garcia; Chiu-Chen Tseng; Linda Hovanessian-Larsen; Pulin Sheth; Sue Ellen Martin; Debra Hawes; Christy Russell; Heather MacDonald; Debu Tripathy; Min-Ying Su; Giske Ursin; Malcolm C Pike
Journal:  Cancer Prev Res (Phila)       Date:  2015-08-14

7.  Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.

Authors:  Shandong Wu; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

8.  Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.

Authors:  Huanjun Ding; Travis Johnson; Muqing Lin; Huy Q Le; Justin L Ducote; Min-Ying Su; Sabee Molloi
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

9.  Template-based automatic breast segmentation on MRI by excluding the chest region.

Authors:  Muqing Lin; Jeon-Hor Chen; Xiaoyong Wang; Siwa Chan; Siping Chen; Min-Ying Su
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

10.  3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction.

Authors:  Yi Wang; Glen Morrell; Marta E Heibrun; Allison Payne; Dennis L Parker
Journal:  Acad Radiol       Date:  2012-10-23       Impact factor: 3.173

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