The purpose of this research is to conduct a clinical study to evaluate the efficacy of a noninvasive and quantitative tool for classification/diagnosis of breast masses. The general goal of this project is to apply a technique called Sub-Hertz Analysis of Visco-Elasticity (SAVE), to increase specificity in the characterization of breast masses. This technique is based on measurement of the strain retardance-time parameter, which characterizes slow deformation of tissue due to a constant force. This parameter is representative of tissue viscosity and elasticity. The novelty of this proposal lies in the fact tht, in contrast to other existing elasticity imaging methods, the SAVE technique explores tissue dynamic response in the frequency range below 1 Hz. To our knowledge, our sub-Hertz method is the only elasticity imaging approach that explores this range of frequency spectrum, and the proposed research will be the first study in which this technique will be tested on a large group of patients. Our preliminary results have shown that SAVE provides reliable discrimination of benign lesions (fibrocystic change, fibroadenoma) from focal malignancies (infiltrating ductal and lobular cancers) with a remarkable specificity of 100% in a relatively small group of patients with non-palpable masses. Our general hypothesis is that by using SAVE, we will be able to improve pre-biopsy breast lesion classification in select groups of patients. This project includes the following Specific Aims: (1) Determine the specificity of SAVE by correlating its results with pathology in a population of patients with suspicious breast masses (BIRADS 4 or 5 lesions); (2) Determine the efficacy of SAVE in classifying non-specific masses in a group of breast patients on the follow- up list (BIRADS 3 lesions).
Specific Aim 1 is designed to examine the efficacy of the SAVE method in patients with BIRADS 4 and 5 category lesions. In this Aim, the results of SAVE will be correlated to the biopsy results to evaluate the specificity of the proposed method in the high-risk group.
Specific Aim 2 examines the patient population with probable benign breast masses, categorized as BIRADS 3 with recommendations for short-term follow up at 6-months intervals. This category is particularly challenging for both the clinician and the patient due to potentially prolonged diagnosis, stressful wait time, and the associated cost. In this aim, the results of SAVE will be correlated with the clinical results, which may include biopsy, at thei re-evaluation visit(s). Successful completion of this research will open the way for a new clinical tool that may be used for classification of breast masses. The proposed method is noninvasive, low cost, easy to use, and compatible with current ultrasound technology, which means that this technology can be readily translated to the clinic and become available to a wide range of breast patients. Consequently, this research has the potential to provide significant impact in breast cancer diagnosis and in reducing unnecessary biopsies.

Public Health Relevance

Background: 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 than the benign ones. Therefore, scientists have been trying to develop imaging tools that are sensitive to tissue stiffness. This research project is about evaluating breast masses by a new method called 'Sub-Hertz Analysis of Visco-Elasticity (SAVE)', which is sensitive to breast stiffness and viscosity. 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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
4R01CA168575-04
Application #
9057008
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Baker, Houston
Project Start
2013-04-01
Project End
2018-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
4
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Kumar, Viksit; Denis, Max; Gregory, Adriana et al. (2018) Viscoelastic parameters as discriminators of breast masses: Initial human study results. PLoS One 13:e0205717
Bayat, Mahdi; Nabavizadeh, Alireza; Kumar, Viksit et al. (2018) Automated In Vivo Sub-Hertz Analysis of Viscoelasticity (SAVE) for Evaluation of Breast Lesions. IEEE Trans Biomed Eng 65:2237-2247
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
Zhang, HongMei; Zhang, QingZhe; Ruan, LiTao et al. (2018) Modeling Ramp-hold Indentation Measurements based on Kelvin-Voigt Fractional Derivative Model. Meas Sci Technol 29:
Zhang, Hong Mei; Wang, Yue; Fatemi, Mostafa et al. (2017) Assessing composition and structure of soft biphasic media from Kelvin-Voigt fractional derivative model parameters. Meas Sci Technol 28:
Abbey, Craig K; Zhu, Yang; Bahramian, Sara et al. (2017) Linear System Models for Ultrasonic Imaging: Intensity Signal Statistics. IEEE Trans Ultrason Ferroelectr Freq Control 64:669-678
Hoerig, Cameron; Ghaboussi, Jamshid; Insana, Michael F (2017) An information-based machine learning approach to elasticity imaging. Biomech Model Mechanobiol 16:805-822
Nabavizadeh, Alireza; Kinnick, Randall R; Bayat, Mahdi et al. (2017) Automated Compression Device for Viscoelasticity Imaging. IEEE Trans Biomed Eng 64:1535-1546
Zhang, HongMei; Wang, Yue; Insana, Michael F (2016) Ramp-hold relaxation solutions for the KVFD model applied to soft viscoelastic media. Meas Sci Technol 27:

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