Combined Acoustic Imaging and Viscoelastic Parameters Estimation in Breast Cancer

描述（由申请人提供）：本研究的目的是优化和评价用于乳腺癌检测和监测的混合成像和定量粘弹性测量工具的有效性。该项目的总体目标是应用振动声成像（VA）和剪切波色散超声振动测量（SDUV）技术，在需要更高特异性的改进成像的特定应用中表征乳腺病变。我们的假设是，通过联合使用VA-SDUV，我们将能够改善活检前乳腺病变的表征。混合VA-SDUV原型使用相同的超声探头提供成像以及组织的粘弹性特性。所提出的方法将是一种低成本的非侵入性工具，预计将提供比传统乳腺成像方法更高的特异性;因此，它将减少不必要的乳腺活检，并改善乳腺患者的护理。本课题的具体目标包括：（1）优化VA-SDUV原型，使其能够用于乳腺成像和粘弹性测量。(2)确定联合使用VA和SDUV在乳腺病变检测中的诊断准确性;（3）使用VA和SDUV的联合信息评估乳腺癌患者对术前化疗的反应。方法：对于目标1，我们将优化在可编程超声系统上实现的VA-SDUV原型。对于目标2，我们将对MRI上发现的乳腺肿块患者进行VA和SDUV。由于MRI在检测乳腺病变方面具有较高的灵敏度，因此基于MRI的VA灵敏度估计有望很好地估计其真实灵敏度。然后，我们将确定与乳腺癌病变相关的共同特征，如毛刺，建筑变形，微分叶病变或边界不清的病变，以及多形性钙化的存在。SDUV提供了病变弹性和粘性的定量估计，这是恶性肿瘤的重要标志。VA和SDUV的组合信息将用于区分乳腺病变。将根据MRI和病理结果计算拟定方法的灵敏度和特异性。对于该项目的目标3，我们将对计划进行术前化疗的新诊断乳腺癌患者进行VA和SDUV。在术前化疗中期和完成后重复VA成像和SDUV。将化疗前、化疗中和化疗后VA图像与相应的MRI图像进行比较，以验证VA在估计病变缩小方面的能力。病变外观和粘弹性参数的变化将与病变大小变化相关。我们将使用MRI和活检结果来验证结果。 公共卫生相关性：该项目的目标是开发一种用于检测和评估乳腺肿块的新工具。具体而言，新的联合方法将对乳腺癌护理产生重大影响，减少不必要的乳腺活检次数，提高乳腺摄影致密实质的高危女性的检测能力，并更好地评估乳腺癌患者对术前化疗的反应。这项研究的成功完成将导致乳腺癌检测和监测的工具。

DESCRIPTION (provided by applicant): The purpose of this research is to optimize and evaluate the efficacy of a hybrid imaging and quantitative viscoelasticity measurement tool for breast cancer detection and monitoring. The general goal of this project is to apply vibroacoustography (VA) and the Shearwave Dispersion Ultrasound Vibrometry (SDUV) technique for characterizing breast lesions in specific applications where there is a need for improved imaging with higher specificity. Our hypothesis is that by using combined VA-SDUV, we will be able to improve pre biopsy breast lesion characterization. The hybrid VA-SDUV prototype provides imaging as well as viscoelasticity properties of tissue using the same ultrasound probe. The proposed method will be a low-cost noninvasive tool that is anticipated to offer higher specificity than conventional breast imaging methods; thus, it will reduce unnecessary breast biopsies and improve breast patient care. This project includes the following Specific Aims: (1) Optimize the VA-SDUV prototype for breast imaging and viscoelasticity measurement. (2) Determine diagnostic accuracy of combined use of VA and SDUV in breast lesion detection and, (3) Assess the response to preoperative chemotherapy in breast cancer patients using the combined information from VA and SDUV. Approach: For Aim 1, we will optimize the VA-SDUV prototype that is implemented on a programmable ultrasound system. For Aim 2, we will perform VA and SDUV on patients with breast masses identified on MRI. Since MRI has a high sensitivity in detecting breast lesions, estimation of VA sensitivity based on MRI is expected to be a good estimation of its true sensitivity. We will then identify common features associated with breast cancer lesions, such as spiculation, architectural distortion, microlobulated lesion or ill defined lesion, and presence of pleomorphic calcifications. SDUV provides quantitative estimate of lesion elasticity and viscosity, which are important markers of malignancy. Combined information from VA and SDUV will be used to differentiate breast lesions. Sensitivity and specificity of the proposed method will be calculated based on MRI and the result of pathology. For Aim 3 of the project, we will perform VA and SDUV on newly diagnosed breast cancer patients who are scheduled for preoperative chemotherapy. VA imaging and SDUV will be repeated in the middle and after completion of the preoperative chemotherapy. The pre-, mid- and post- chemotherapy VA images will be compared to the corresponding MRI images to verify the capability of VA in estimating lesion downsizing. Changes in lesion appearance and viscoelastic parameters will be correlated to lesion size changes. We will verify the results using the results of MRI and biopsy. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop a new tool for detection and evaluation of breast masses. Specifically, the new combined method will have a significant impact on breast cancer care by reducing the numbers of unnecessary breast biopsies, improving detection in high risk women who have mammographically dense parenchyma, and better assessing the response to preoperative chemotherapy in breast cancer patients. Successful completion of this research will lead to a tool for breast cancer detection and monitoring.