Quantitative Three-dimensional High Definition Microvessel Imaging for characterization breast masses v1
Breast cancer, a major cause of morbidity and mortality in women, is highly dependent on angiogenesis for its growth and distant metastasis. Furthermore, new blood vessels in malignant tumors are structurally abnormal and different from benign. Imaging techniques that provide information regarding tumor microvasculature structures could aid cancer detection. This protocol is to advance the development and evaluate the performance of a quantitative 3D microvessel imaging technique to provide quantitative information of morphological features of tumor microvessel as new biomarker for differentiation of malignant and benign breast masses. Our team has developed a new 2D contrast-free ultrasound (US) microvessel imaging technique based on novel processing procedures for revealing and enhancing submillimeter size tumor microvessels and complemented this technique with novel quantification tools to quantify vessel morphology. The results of quantitative 2D-HDMI for differentiation of malignant and benign breast masses are promising; however, the 2D imaging method overlooks some important 3-dimensional (3D) morphological features, such as the connectivity of the blood microvessels, leading to under- or overestimation of these parameters. Here, we propose to advance the development of a new quantitative 3D High-Definition Microvascular Imaging (q3D-HDMI) to provide complementary diagnostic information to the conventional US. The new technique, q3D-HDMI, uses high frame rate ultrasound imaging and is based on novel processing and quantification procedures for 3D imaging to reveal and quantify microvessel morphology in tumor volume. In this protocol we evaluate the diagnostic performance of q3D-HDMI for characterization of breast masses in a population of pre-biopsy patients; correlate the results with pathology as the gold standard.We also compare q3D-HDMI and q2D-HDMI in differentiating malignant from benign breast lesions