Combined Acoustic Imaging and Viscoelastic Parameters Estimation in Breast Cancer

描述(申请人提供)：这项研究的目的是优化和评估一种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学(VA)和剪切波色散超声测振(SDUV)技术，在需要更高特异度的改进成像的特定应用中确定乳腺病变的特征。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特征。混合的VA-SDUV原型使用相同的超声探头提供组织的成像和粘弹性特性。建议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房成像方法更高的特异性；因此，它将减少不必要的乳房活检，并改善乳房患者的护理。本项目包括以下具体目标：(1)优化用于乳房成像和粘弹性测量的VA-SDUV样机。(2)确定联合应用VA和SDUV诊断乳腺病变的准确性；(3)联合应用VA和SDUV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施的VA-SDUV原型。对于目标2，我们将对MRI诊断为乳腺肿块的患者进行VA和SDUV。由于MRI对发现乳腺病变具有很高的敏感性，基于MRI的VA敏感性估计有望很好地估计其真实敏感性。然后我们将确定与乳腺癌病变相关的共同特征，如毛刺、结构扭曲、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度的定量估计，这是恶性肿瘤的重要标志。来自VA和SDUV的联合信息将用于区分乳房病变。该方法的灵敏度和特异度将根据MRI和病理结果进行计算。对于项目的目标3，我们将对计划进行术前化疗的新诊断的乳腺癌患者进行VA和SDUV。术中及术前化疗结束后重复进行VA显像和SDUV检查。将化疗前、化疗中和化疗后的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的变化相关。我们将利用MRI和活检的结果来验证结果。

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.