Abstract P6-06-04: Exploratory computational longitudinal analysis of mammographic microenvironment disruption preceding breast tumorigenesis
Authors: Khalil Andre , Toner Brian 
Published: March 2020

Abstract
Despite an aggressive emphasis on screening mammography, there has been no change in the number of initial metastatic breast cancer diagnosis since 1975. Even more alarming is the lack of effective screening to differentiate dormant cancerous pathology that may spontaneously ignite into an aggressive metastatic cancer. In this context, screening mammography is ineffective and biased towards a reactive diagnosis, when cancer is already present. We seek to develop a predictive algorithm that is sensitive to currently undetectable alterations of the microenvironment that precede tumorigenesis.
Histological features such as tissue disruption and loss of tissue homeostasis, which are critical early indicators of breast cancer, unfortunately fall well outside the detection abilities of a trained radiologist or current computer-aided detection/diagnostic methods. In a recent publication using a powerful wavelet-based multifractal image analysis method, we extracted quantitative metrics for tissue disruption from standard mammography, and inferred a correlation with loss of tissue homeostasis. The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to quantify density fluctuations from mammographic breast tissue via a statistical roughness exponent called the Hurst exponent (H). The two mammographic tissue radiological states, i.e., fatty and dense, are quantitatively characterized with this Hurst exponent: H&gt;1/2 (correlated spatial density fluctuations) for dense tissue; and H&lt;1/2 (anti-correlated spatial density fluctuations) for fatty tissue. However, tissue regions that are neither dense nor fatty, with H~1/2 (uncorrelated spatial density fluctuations), were found preferably in patients with tumorous breasts vs. normal breasts (p-value = 0.0006) [Marin et al. Medical Physics, 2017]. The underlying physical processes associated with a H=1/2 signature are those of randomness and disorganization. Indeed, incoherent angular motion and the consequential adoption of randomized cellular motility are associated with malignant growth in breast tissue. We hypothesize that this H~1/2 signature accompanies or even preceeds tumorigenesis.
In an exploratory analysis aimed at verifying this hypothesis, we studied, for 22 breast cancer patients, the progression of healthy breast tissue microenvironment towards a disrupted state by performing a computational analysis of longitudinal sequences of mammograms multiple years prior to, and leading to diagnostic. Preliminary results indicate a measurable change in mammographic density fluctuations (and therefore, potentially loss of tissue homeostasis), between 1 and 3 years prior to radiologic detection / diagnostic. An eventual larger-scale clinical validation of such an analysis could lead to the routine implementation of predictive algorithms based on the multifractal characterization of tissue disruption that precedes tumorigenesis. Such predictive diagnostics empowers both patient and clinician to collaboratively assess all ramification of the disease and select optimal therapeutics.
Citation Format: Andre Khalil, Brian Toner. Exploratory computational longitudinal analysis of mammographic microenvironment disruption preceding breast tumorigenesis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-06-04.