Characterizing Breast Phenotype with a Novel Measure of Fibroglandular Structure
Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, “orientated mammographic entropy”, via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H om , computed from this histogram. H om , shows good correlation between left and right breasts (r 2 = 0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r 2 = 0.07, N=974), and breast density, as estimated using Volpara TM software (r 2 = 0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model.
KeywordsBreast Cancer Risk Breast Density Pectoral Muscle Digital Mammogram Breast Region
Unable to display preview. Download preview PDF.
- 1.NHSBSP 61, Screening for Breast Cancer in England: Past and Future (February 2006) ISBN 1 84463 026 9, http://www.cancerscreening.nhs.uk/breastscreen/publications/nhsbsp61.pdf
- 2.Antoniou, A.C., Pharoah, P.P.D., Smith, P., Easton, D.F.: The boadicea model of genetic susceptibility to breast and ovarian cancer. British Journal of Cancer 91(8), 1580–1590 (2004)Google Scholar
- 3.Boehm, H.F., Schneider, T., Buhmann-Kirchhoff, S.M., Schlossbauer, T., Rjosk-Dendorfer, D., Britsch, S., Reiser, M.: Automated classification of breast parenchymal density: Topologic analysis of x-ray attenuation patterns depicted with digital mammography. American Journal of Roentgenology 191(6), W275–W282 (2008); Times Cited: 0Google Scholar
- 12.Nielsen, M., Karemore, G., Loog, M., Raundahl, J., Karssemeijer, N., Otten, J.D.M., Karsdal, M.A., Vachon, C.M., Christiansen, C.: A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer. Cancer Epidemiology 35(4), 381–387 (2011)CrossRefGoogle Scholar
- 14.Reiser, I., Sidky, E.Y., Nishikawa, R.M., Pan, X.: Development of an Analytic Breast Phantom for Quantitative Comparison of Reconstruction Algorithms for Digital Breast Tomosynthesis. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 190–196. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 15.Wolfe, J.N.: Breast patterns as an index of risk for developing breast-cancer. American Journal of Roentgenology 126(6), 1130–1139 (1976)Google Scholar