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.
Highlights
Breast cancer is the second-most occurring cancer type, and is ranked as the fifth in terms of mortality (Siegel et al, 2015; Bray et al, 2018)
Tissue disruption is a term used in this paper and in Marin et al (2017) to characterize what we infer as a larger-scale tissue architecture alteration caused by loss of tissue homeostasis
We perform a computational analysis on the mediolateral oblique (MLO) mammographic views from 81 patients with a malignant tumor (43 invasive lobular carcinomas (ILC) and 38 invasive ductal carcinomas (IDC)) and 23 patients with a benign tumor (12 fibroadenoma and 11 fibrocystic mastopathy)
Summary
Breast cancer is the second-most occurring cancer type, and is ranked as the fifth in terms of mortality (Siegel et al, 2015; Bray et al, 2018). Microcalcifications can occur in such benign processes as sclerosing adenosis or some fibroadenomas (Fischmann, 2008; Henrot et al, 2014) These findings are driving us to “think outside of the tumor” (Gerasimova-Chechkina et al, 2016) and to develop a computational approach to study and quantitatively characterize tissue microenvironment throughout the whole breast (Marin et al, 2017). We perform a computational analysis on the mediolateral oblique (MLO) mammographic views from 81 patients with a malignant tumor (43 ILC and 38 IDC) and 23 patients with a benign tumor (12 fibroadenoma and 11 fibrocystic mastopathy)
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