Loss of Mammographic Tissue Homeostasis in Invasive Lobular and Ductal Breast Carcinomas vs. Benign Lesions

Abstract

The 2D wavelet transform modulus maxima (WTMM) method is used to perform a comparison of the spatial fluctuations of mammographic breast tissue from patients with invasive lobular carcinoma, those with invasive ductal carcinoma, and those with benign lesions. We follow a procedure developed and validated in a previous study, in which a sliding window protocol is used to analyze thousands of small subregions in a given mammogram. These subregions are categorized according to their Hurst exponent values (H): fatty tissue (H ≤ 0.45), dense tissue (H ≥ 0.55), and disrupted tissue potentially linked with tumor-associated loss of homeostasis (0.45 < H < 0.55). Following this categorization scheme, we compare the mammographic tissue composition of the breasts. First, we show that cancerous breasts are significantly different than breasts with a benign lesion (p-value ∼ 0.002). Second, the asymmetry between a patient’s cancerous breast and its contralateral counterpart, when compared to the asymmetry from patients with benign lesions, is also statistically significant (p-value ∼ 0.006). And finally, we show that lobular and ductal cancerous breasts show similar levels of disruption and similar levels of asymmetry. This study demonstrates reproducibility of the WTMM sliding-window approach to help detect and characterize tumor-associated breast tissue disruption from standard mammography. It also shows promise to help with the detection lobular lesions that typically go undetected via standard screening mammography at a much higher rate than ductal lesions. Here both types are assessed similarly.

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method

“…Although several applications may benefit from this rescue method, as discussed in the introduction, an immediate benefit is for the sliding-window analysis of mammograms ( Marin et al, 2017 ; Gerasimova-Chechkina et al, 2021 ). To offer the best possible computational aid to the radiologists in their interpretation of these mammograms, it is critical to extract as much quantitative information as possible from each subregion of each mammogram.…”

“…In this study, only positive values of the Hurst exponent were considered for the calibration fBm surfaces ( H = 0.1, 0.3, 0.5, 0.7), which is motivated, and justified, by the Hurst values measured in mammogram subregions ( Marin et al, 2017 ; Gerasimova-Chechkina et al, 2021 ). Despite only using four Hurst values, the effects are similar, which precludes the need to investigate smaller intervals.…”

“…The multifractal analysis of self-affine rough surfaces ( Arneodo et al, 2000 ; Decoster et al, 2000 ; Arneodo et al, 2003 ; Wendt et al, 2009 ) is very valuable in many applications with underlying multiscale non-linear variability, from astrophysics ( Khalil et al, 2006 ; Kestener et al, 2010 ), geophysics ( Roux et al, 2000 ), texture-based segmentation ( Pascal et al, 2018 ), to biomedical imaging ( Kestener et al, 2001 ; Marin et al, 2017 ; Gerasimova-Chechkina et al, 2021 ).…”

“…The 2D Wavelet Transform Modulus Maxima (WTMM) method has been adapted and applied in three different forms: a multiscale segmentation method ( Kestener et al, 2001 ; Khalil et al, 2007 ; Roland et al, 2009 ; Grant et al, 2010 ; Kestener et al, 2010 ; McAteer et al, 2010 ; Batchelder et al, 2014 ; Marin et al, 2018 ; Liu J. et al, 2021 ), a multiscale anisotropy method ( Khalil et al, 2006 , 2009 ; Tilbury et al, 2021 ), and a multifractal formalism ( Arneodo et al, 2000 ; Decoster et al, 2000 ; Roux et al, 2000 ; Arneodo et al, 2003 ; Khalil et al, 2006 ). The 2D WTMM multifractal method is a multiscale formalism perfectly suited for the analysis of self-affine rough surfaces such as mammograms by identifying density fluctuations and spatial correlations within these surfaces ( Batchelder et al, 2014 ; Plourde et al, 2016 ; Marin et al, 2017 ; Gerasimova-Chechkina et al, 2021 ).…”

Longitudinal Case-Control Study of Active and Passive Dense Mammographic Breast Tissue

“…The 2D Wavelet-Transform Modulus Maxima (WTMM) method has been used in several fields to analyze complex signals to extract features and quantify spatial structure to gain insights into the underlying mechanisms of complex organizations [26][27][28][29][30][31]. In previous studies, the 2D WTMM method was employed to capture the structural organization of mammographic tissue, via the Hurst exponent (H), and the calculated organization was inferred to be linked to the structure of the tumor microenvironment at the time of diagnosis [32,33]. The method allows for segmenting dense breast tissue into regions of active dense tissue, i.e., regions that show structural reorganization occurring and are inferred to be linked to the dynamics of cancer onset and progression, and regions of passive dense tissue.…”