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

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.

Mammographic evidence of microenvironment changes in tumorous breasts.

PURPOSE: The microenvironment of breast tumors plays a critical role in tumorigenesis. As long as the structural integrity of the microenvironment is upheld, the tumor is suppressed. If tissue structure is lost through disruptions in the normal cell cycle, the microenvironment may act as a tumor promoter. Therefore, the properties that distinguish between healthy and tumorous tissues may not be solely in the tumor characteristics but rather in surrounding non-tumor tissue. The goal of this paper was to show preliminary evidence that tissue disruption and loss of homeostasis in breast tissue microenvironment and breast bilateral asymmetry can be quantitatively and objectively assessed from mammography via a localized, wavelet-based analysis of the whole breast. METHODS: A wavelet-based multifractal formalism called the 2D Wavelet Transform Modulus Maxima (WTMM) method was used to quantitate density fluctuations from mammographic breast tissue via the Hurst exponent (H). Each entire mammogram was cut in hundreds of 360 × 360 pixel subregions in a gridding scheme of overlapping sliding windows, with each window boundary separated by 32 pixels. The 2D WTMM method was applied to each subregion individually. A data mining approach was set up to determine which metrics best discriminated between normal vs. cancer cases. These same metrics were then used, without modification, to discriminate between normal vs. benign and benign vs. cancer cases. RESULTS: The density fluctuations in healthy mammographic breast tissue are either monofractal anti-correlated (H < 1/2) for fatty tissue or monofractal long-range correlated (H>1/2) for dense tissue. However, tissue regions with H~1/2, as well as left vs. right breast asymetries, were found preferably in tumorous (benign or cancer) breasts vs. normal breasts, as quantified via a combination metric yielding a P-value ~ 0.0006. No metric considered showed significant differences between cancer vs. benign breasts. CONCLUSIONS: Since mammographic tissue regions associated with uncorrelated (H~1/2) density fluctuations were predominantly in tumorous breasts, and since the underlying physical processes associated with a H~1/2 signature are those of randomness, lack of spatial correlation, and free diffusion, it is hypothesized that this signature is also associated with tissue disruption and loss of tissue homeostasis.

Computational growth model of breast microcalcification clusters in simulated mammographic environments.

BACKGROUND: When screening for breast cancer, the radiological interpretation of mammograms is a difficult task, particularly when classifying precancerous growth such as microcalcifications (MCs). Biophysical modeling of benign vs. malignant growth of MCs in simulated mammographic backgrounds may improve characterization of these structures METHODS: A mathematical model based on crystal growth rules for calcium oxide (benign) and hydroxyapatite (malignant) was used in conjunction with simulated mammographic backgrounds, which were generated by fractional Brownian motion of varying roughness and quantified by the Hurst exponent to mimic tissue of varying density. Simulated MC clusters were compared by fractal dimension, average circularity of individual MCs, average number of MCs per cluster, and average cluster area. RESULTS: Benign and malignant clusters were distinguishable by average circularity, average number of MCs per cluster, and average cluster area with p<0.01 across all Hurst exponent values considered. Clusters were distinguishable by fractal dimension with p<0.05 in low Hurst exponent environments. As the Hurst exponent increased (tissue density increased) benign and malignant MCs became indistinguishable by fractal dimension. CONCLUSIONS: The fractal dimension of MCs changes with breast tissue density, which suggests tissue environment plays a role in regulating MC growth. Benign and malignant MCs are distinguishable in all types of tissue by shape, size, and area, which is consistent with findings in the literature. These results may help to better understand the effects of the tissue environment on tumor progression, and improve classification of MCs in mammograms via computer-aided diagnosis.