Neoadjuvant chemotherapy is the standard of care for treatment of locally advanced breast cancer, which is a major clinical issue. Access to inexpensive and noninvasive methods to determine early treatment response are essential to determine if a chosen anticancer therapeutic regimen is efficacious. Tumor angiogenesis is a key biomarker of breast cancer growth and metastasis. This tumor microvascularity is known to exhibit distinct perfusion characteristics and morphologic features during the early stages of breast tumor development which fundamentally change during a positive response to neoadjuvant treatment. The overarching goal of this research project is to develop an innovative three-dimensional (3D) super-resolution (SR-US) imaging system and new image processing solutions to considerably improve our ability to perform in vivo quantitative analysis of tumor angiogenic networks.
The first aim of this project involves optimization of 3D SR-US imaging functionality on a programmable US scanner equipped with a custom 1024-element (32 x 32) matrix array transducer.
The second aim i nvolves the development of new open-source SR-US image processing software for performing motion correction and quantitative analysis of tumor perfusion and microvascular morphology features in 3D space. In the third aim, we will evaluate the use of angiogenic biomarkers extracted from 3D SR- US images as a quantitative basis for distinguishing healthy from diseased tissue volumes in a transgenic animal model of breast cancer. We will also assess the use of in vivo 3D SR-US imaging for detection of early tumor response to neoadjuvant treatment using the same animal model.
The goal of this research project is to develop a new three-dimensional super-resolution ultrasound imaging system and image processing algorithms to improve breast cancer detection and assessment of early response to neoadjuvant treatment.
