title: Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI.
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.
authors.affiliation: Tu & Yuen Center for Functional Onco-Imaging, University of California, Irvine, California 92697, USA.
authors.forename: Ke
authors.initials: K
authors.lastname: Nie
authors.forename: Jeon-Hor
authors.initials: JH
authors.lastname: Chen
authors.forename: Siwa
authors.initials: S
authors.lastname: Chan
authors.forename: Man-Kwun I
authors.initials: MK
authors.lastname: Chau
authors.forename: Hon J
authors.initials: HJ
authors.lastname: Yu
authors.forename: Shadfar
authors.initials: S
authors.lastname: Bahri
authors.forename: Tiffany
authors.initials: T
authors.lastname: Tseng
authors.forename: Orhan
authors.initials: O
authors.lastname: Nalcioglu
authors.forename: Min-Ying
authors.initials: MY
authors.lastname: Su
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods; D008875:Middle Aged; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted