Abstract
Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density.
No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. For serial, temporal analysis it is necessary to be able to detect more subtle changes.
In previous work, a method for measuring the effect of HRT w.r.t. changes in biological density in the breast is described. The method provides structural information orthogonal to intensity-based methods. Hessian-based features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined.
We have previously shown that this method can separate patients receiving HRT from patients receiving placebo. In this work, the focus is on deeper understanding of the methodology using tests on sets of artificial images of regular elongated structures.
Keywords
- Breast Cancer Risk
- Hormone Replacement Therapy
- Mammographic Density
- Mammographic Breast Density
- Hormone Replacement Therapy Group
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Boyd, N.F., Byng, J.W., Jong, R.A., Fishell, E.K., Little, L.E., Miller, A.B., Lockwood, G.A., Trichler, D.L., Yaffe, M.J.: Quantitative classification of mammographic densities and breast cancer risk: Results from the canadian national breast screening study. Academic Radiology 87(9), 670–675 (1995)
Boyd, N.F., O’Sullivan, B., Campbell, J.E., et al.: Mammographic signs as risk factors for breast cancer. British Journal of Cancer 45, 185–193 (1982)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergeve, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Lindeberg, T.: Scale-space: A framework for handling image structures at multiple scales. In: CERN School of Computing (September 1996)
Raundahl, J., Loog, M., Nielsen, M.: Mammographic density measured as changes in tissue structure caused by hrt. In: SPIE Medical Imaging (2006)
van der Heiden, F., Duin, R.P.W., de Ridder, D., Tax, D.M.J.: Classification, Parameter Estimation, State Estimation: An Engineering Approach Using MatLab. Wiley, New York (2004)
van Gils, C.H., Hendriks, J.H.C.L., Holland, R., Karssemeijer, N., Otten, J.D.M., Straatman, H., Verbeek, A.L.M.: Changes in mammographic breast density and concomitant changes in breast cancer risk. European Journal of Cancer Prevention 8, 509–515 (1999)
Warming, L., Ravn, P., Spielman, D., Delmas, P., Christiansen, C.: Trimegestone in a low-dose, continuous-combined hormone therapy regimen prevents bone loss in osteopenic postmenopausal women. Menopause 11(3), 337–342 (2004)
Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2498 (1976)
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© 2006 Springer-Verlag Berlin Heidelberg
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Raundahl, J., Loog, M., Nielsen, M. (2006). Understanding Hessian-Based Density Scoring. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_60
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DOI: https://doi.org/10.1007/11783237_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
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