Quantifying Breast Tumor Texture and Microvasculature in 3D using Ultrasound Imaging

The goal of this study is to quantify breast tumor microvasculature and texture in three dimensions using ultrasound (US) imaging. By enhancing our understanding of the tumor microenvironment and its correlation with histopathology, this research could lead to improved therapy outcomes and better patient care.

Breast cancer is one of the most prevalent cancers and the leading cause of women’s mortality worldwide, is categorized by histological and molecular subtypes. Breast tumor heterogeneity due to the tumor microenvironment can vary across the tumor volume or between different tumor types. Intratumor heterogeneity is the coexistence of distinct subpopulations of tumor cells within the primary tumor. The utilization of quantitative US parameters derived from the statistical distribution of backscatter (Nakagami distribution, entropy, GLCM, etc.) and speed of sound (echo-time shift methods, spectral approaches, etc.), attenuation (spectral difference) can offer insights into the underlying tissue microstructures. However, quantifying B-mode US images adds more value since it is easily accessible and is cost-effective in understanding breast tumors. Quantitative B-mode US can be effective for point-of-care breast tumor assessment. While histopathological evaluation of breast tumors is the gold standard to classify tumors, the ability to provide a global quantitative measure of heterogeneity is key to helping better understand heterogeneous breast tumors.

Three-dimensional US can serve as a surrogate measure to assess the spatial heterogeneity of the tumor and the tissue surrounding the tumor. Texture analysis employs mathematical techniques to assess the spatial distribution of gray-level intensities, serving as an imaging biomarker to quantify spatial heterogeneity within tumors in tissue characterization applications. Assessing intratumor and peritumoral heterogeneity, manifested as textural variations in US images, is crucial for identifying refractory tumor regions that may exhibit therapeutic resistance. In addition, microvascular flow imaging (MVFI) is another Doppler-based US technique that is highly sensitive to detecting low-velocity blood flow. MVFI uses a multi-dimensional wall filter to separate low-velocity flow signals from clutter artifacts, allowing better visualization of micro vessels within breast lesions than conventional Doppler US. MVFI maps the intra tumoral microvasculature in 3D using US imaging without needing contrast agents or dyes, which can help understand the degree of angiogenesis, growth, and invasion.

The study aims to register B-mode US volume with volumetric microvascular data and visualize them together, enhancing the understanding of the structural and morphological evolution of breast tumors in 3D. Data from this trial can potentially help lead into understanding therapy response and tailoring treatment regimen for patients suffering from breast tumors.