Classification of simulated hyperplastic stages in the breast ducts based on ultrasound RF echo

Abstract

Visual inspection of ultrasound is diagnostically limited for characterizing breast tissue, in particular when it comes to visually detecting hyperplasia that forms in the ducts at its early formation (at submillimeter resolution) stages. It can, of course, be seen using biopsies. But this will not be done unless the areas have been flagged using noninvasive modalities. The aim of this paper is to draw to the attention of the medical community (albeit through simulations) that the continuous wavelet transform decomposition (CWTD) that was proven in vivo for tissue characterization before has the potential to flag out simulated hyperplasia data at submillimeter resolutions. And it might be an excellent candidate for detecting in vivo hyperplastic changes in the breast. To the best of our knowledge, this is the first attempt at studying the potential of detecting cell growth in breast ducts using ultrasound. The stochastic decomposition model (the CWTD) of the RF echo with its coherent and diffuse components, yields image parameters that correlate closely with the structural parameters of the (simulated) hyperplastic stages of the breast tissue. The discrimination power of the various parameters is studied under a host of conditions, such as varying resolution, depth, and coherent to diffuse energy ratio (CDR) values using a point-scatterer model simulator that mimics epithelium hyperplastic growth in the breast ducts. These are shown to be useful for detecting the various types of simulated hyperplastic data. Careful analysis shows that three parameters, in particular the number of coherent scatterers, the Rayleigh scattering degree, and the energy of the diffuse scatterers, are most sensitive to variations in the hyperplastic simulated data. And they show very high ability to discriminate between various stages of simulated hyperplasia, even in cases of low resolution and low CDR values. Using the area under the receiver operating characteristics (ROC) curve (A(z)) as the performance metric, values of A(z) > 0.942 are obtained when discriminating between stages for resolution <or= 0.4 mm, even for low CDR values. Then it drops below the 0.9 range as the resolution exceeds the 0.4-mm range. A nonparametric segmentation method to extract ductal areas from breast scans is presented to be used as a pre-step before classification of hyperplastic stages in breast ducts. This is a necessary step for segmenting the RF scan into ductal versus nonductal areas from breast scans. This is tested using breast tissue mimicking phantom data resulting values of A(z) > 0.948 for different duct densities.

On‐the‐fly detection of changes on and below the surface in epithelium mucosal tissue architecture from scattered light

“…The SDM model extracted parameters relate to the morphology of the examined tissue and are sensitive to changes in the tissue structure. A variant of that general stochastic decomposition method have been successfully proposed and used by us in our breast tissue characterization and cancer detection [32][33][34] using ultrasound, with sensitivity and specificity reaching over 90%. We propose the SDM framework to potentially model 1-D scan of light scattering data from epithelium mucosa tissue.…”

“…(3). What determines p is the memory of the process, namely, how many previous values of the process enter in the determination of the conditional mean value of the process at time n. The value of 5 for p is determined using our previous work in [26,32,34]. The AR process is parameterized by the set…”

Can we see epithelium tissue structure below the surface using an optical probe?

“…3. What determines p is the memory of the process, namely, how many previous values of the process enter in the determination of the conditional mean value of the process at time n. The value of 5 for p is determined using our previous work in [23,43,44]. The AR process is parameterized by the set [b 1 (k), b 2 (k),…, b p (k), r 2 (k) = (b(k), r 2 (k)].…”

Stochastic decomposition method for modeling the scattered signal reflected of mucosal tissues

“…averaged wavelet power around its mean value are the locations of the coherent scatterers in the probed region under study. 33,34 The number of the fluctuations that exceed a threshold is the number of coherent scatterers in the tissue. The number of coherent scatterers is related to the number of resolvable scatterers in the tissue (single scatterers).…”

“…1 Our goal is to establish a textural model that models the reflected scattered signal and shows its sensitivity to differential changes in the morphology and physical characteristics of the tissue being optically scanned. Textural models have been successfully used by us to model radio frequency ͑rf͒ ultrasound images of breast tissue, [32][33][34] where we have proven the model to be quite effective for discrimination between benign, normal, and malignant breast tissues ͑the A z values were in the range of 0.862 to 0.999͒. Similar to our work on rf modeling of breast tissue, we adopt a stochastic decomposition method ͑SDM͒ to model the scattered signal optically reflected from mucosal tissues to track down differential changes in the morphology and physical characteristics of the imaged ͑scanned͒ tissue.…”