Assessment of breast masses is a critical step in selection of masses to be biopsied. The low specificity of traditional imaging methods, e.g., mammography and ultrasound, leads to a great number of unnecessary (i.e. benign) biopsies, resulting in high cost as well as significant trauma and anxiety for the patients due to this invasive procedure. In recent years, ultrasound elasticity imaging has been of interest as an adjunct to breast sonography to improve the specificity. This method is based on the premise that malignant masses are often significantly stiffer than benign masses and normal tissue. However, evidence shows that elasticity alone is not always specific, and there are significant overlaps in the elasticity of benign and malignant masses. This poses a fundamental limitation of the diagnostic value of elasticity parameter, i.e., elasticity information alone is not specific enough to completely differentiate between benign and malignant masses. Therefore, to improve differentiation of breast masses, it is imperative to develop new methods that provide additional and complementary information. To accomplish this task, our vision is to employ a multi-parameter approach. As we will show in our promising preliminary studies, nonlinearity of elasticity and heterogeneity in elasticity distribution are two parameters that are relevant to the breast malignancy and can be used to differentiate breast masses. Our goal is to further develop and test a novel multi-parameter technique for assessment of breast masses. In this technique, which is called Nonlinear Elasticity Mapping (NEM), we quantitatively measure 3 parameters: the (linear) elasticity, the nonlinear elasticity parameter, and the heterogeneity of elasticity distribution. In this project, we plan to conduct a clinical study to evaluate the efficcy of a NEM technology for classification/diagnosis of breast masses. To our knowledge, our proposed method is the only breast evaluation method that explores the combination of the 3 above-mentioned parameters for differentiation of breast masses.
Our Specific Aims i nclude: (1) Determine the diagnostic performance of the combination of linear and nonlinear elasticity parameters by correlating its results with pathology, and (2) Determine the diagnostic performance of elasticity heterogeneity in combination with linear and nonlinear elasticity parameters.
Both Aims are tested in a population of patients with suspicious breast masses. This proposal is the result of collaboration among several experts in the field. Also, the projects benefits from the world-class clinical research and facilities at the Mayo Clinic. Successful completion of this research will open the door for a new technology for differentiation of breast masses in clinic. NEM is noninvasive, low cost, easy to use, and compatible with current ultrasound technology, which means that this technology can be readily translated to clinic and become available to a wide range of breast patients. Consequently, this research has potential to provide significant impact in breast cancer diagnosis and in reducing unnecessary biopsies.

Public Health Relevance

Early diagnosis of breast cancer is critical for favorable clinical outcomes. Breast cancer remains the second-leading cause of cancer deaths in women, and over 200,000 new cases of invasive breast cancer are expected in the USA this year alone. As suggested by The American Cancer Society, breast self-examination and clinical breast examination (palpation) are the most frequently used diagnostic tools for detecting breast abnormalities. It is known that cancerous masses are harder and have more irregular shape than the benign ones. Therefore, scientists have been trying to develop new imaging tools that are sensitive to these characteristics of breast masses. This research project is about evaluating breast masses by a new method called 'Nonlinear Elasticity Mapping (NEM)', which is sensitive to mass stiffness, the pattern by which the stiffness changes under compression, and to the structural irregularities of such masses. This noninvasive method is based on ultrasound and is easy to use. The goal of this project is to test the new method on breast patients and determine if it can improve differentiation of cancerous and noncancerous breast masses. Potential Outcomes and Benefits: Successful completion of this research will open the way for a new class of low-cost noninvasive and quantitative tools for improved differentiation of breast masses, which will eventually help in better diagnosis and monitoring of breast cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Zhang, Huiming
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Mayo Clinic, Rochester
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Kumar, Viksit; Denis, Max; Gregory, Adriana et al. (2018) Viscoelastic parameters as discriminators of breast masses: Initial human study results. PLoS One 13:e0205717
Nayak, Rohit; Kumar, Viksit; Webb, Jeremy et al. (2018) Non-contrast agent based small vessel imaging of human thyroid using motion corrected power Doppler imaging. Sci Rep 8:15318
Gregory, Adriana; Bayat, Mahdi; Kumar, Viksit et al. (2018) Differentiation of Benign and Malignant Thyroid Nodules by Using Comb-push Ultrasound Shear Elastography: A Preliminary Two-plane View Study. Acad Radiol 25:1388-1397