Fractal Dimension and Lacunarity analysis of mammographic patterns in assessing breast cancer risk related to HRT treated population: A Longitudinal and Cross-sectional study Gopal Karemore 1, 2 , Mads Nielsen 1, 2 1. University of Copenhagen, Copenhagen, Denmark, 2. Nordic Bioscience A/S, Herlev, Denmark ABSTRACT Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and Lacunarity contribute to asses breast cancer risk. FD represents the complexity while the Lacunarity characterize the gappiness of a fractal. Our cross-sectional case-control study includes mammograms of 50 patients diagnosed with breast cancer in the subsequent 2-4 years and 50 matched controls. The longitudinal double blind placebo controlled HRT study includes 39 placebo and 36 HRT treated volunteers for two years. ROIs with same dimension (250*150 pixels) were created behind the nipple region on these radiographs. Box counting method was used to calculate the fractal dimension (FD) and the Lacunarity. Paired t-test and Pearson correlation coefficient were calculated. It was found that there were no differences between cancer and control group for FD (P=0.8) and Lacunarity (P=0.8) in cross- sectional study whereas earlier published heterogeneity examination of radiographs (BC-HER) breast cancer risk score separated groups (p=0.002). In the longitudinal study, FD decreased significantly (P<0.05) in the HRT treated population while Lacunarity remained insignificant (P=0.2). FD is negatively correlated to Lacunarity (-0.74, P<0.001), BIRADS (-0.34, P<0.001) and Percentage Density (-0.41, P<0.001). FD is invariant to the mammographic texture change from control to cancer population but marginally varying in HRT treated population. This study yields no evidence that lacunarity or FD are suitable surrogate markers of mammographic heterogeneity as they neither pick up breast cancer risk, nor show good sensitivity to HRT. Keywords: Mammogram, Image Analysis, Breast cancer risk, HRT, Fractal Dimension, Lacunarity 1. INTRODUCTION The breast composed of a mixture of epithelial and fibrogladular tissue together with fatty tissue [1]. There distribution on mammogram is refered to as the mammographic parenchymal pattern [2]. Fat is radiographically lucent and appears dark on the image, whereas fibroglandular tissue is radiographically dense and appears brighter[3]. Mammographic perenchymal patterns are being increasingly used as intermediate markers in studies investigating the etiology of breast cancer [4,5]. It is well established that women with radiologically dense breast are at higher risk of developing breast cancer than women whose breast are radiologically lucent [6,7] . The use of Hormone Replacement Therapy (HRT) is currently a subject of debate because of the possibility of an increase in the incidence of breast cancer and difficulties associated with breast cancer detection. Although HRT for post-menopausal women improves quality of life by relieving menopausal symptoms and is thought to have beneficial effects on osteoporosis, coronary heart disease, and brain function in long-term, the relationship between HRT and breast cancer is the major reason for women not considering or discontinuing HRT [8,9,10]. Mammographic density is a strong risk factor for breast cancer. However, whether changes in mammographic density are associated with risk remains unclear [6, 11]. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative computer assisted density measurements [12]. In past few years researchers are trying to develop a quantitative measure that can sense the tissue pattern change or density change in Medical Imaging 2009: Computer-Aided Diagnosis, edited by Nico Karssemeijer, Maryellen L. Giger Proc. of SPIE Vol. 7260, 72602F · © 2009 SPIE · CCC code: 1605-7422/09/$18 · doi: 10.1117/12.813699 Proc. of SPIE Vol. 7260 72602F-1