Evaluation of MRI Biomarkers of Breast Cancer Response

描述（由申请人提供）：目前，通过体格检查、乳房X线摄影和/或超声测量肿瘤形态的明显变化来监测乳腺肿瘤对化疗的反应。对治疗效果的临床判断是主观的，容易出错。一种可重复的，非侵入性的成像方法，可以可靠地评估肿瘤的反应将大大改善临床乳腺癌护理。动态对比增强MRI（DCE-MRI）和弥散加权MRI（DW-MRI）的专门磁共振成像（MRI）方法已经成熟到可以提供有关肿瘤状态的独特信息的程度。DCE-MRI报告了相关的生理参数，包括血管灌注、血管壁通透性、血管外细胞外体积分数和（最近）细胞大小。DW-MRI可以提供关于组织细胞结构的详细信息。我们建议结合联合收割机的一种新的分析DCE-MRI数据与DW-MRI数据在3T获得的乳腺癌治疗反应提供功能评估。我们假设，整合这些定量MRI方法将提供第一个治疗周期后肿瘤反应的准确和预测性测量。此外，我们将通过将体内MR图像与离体乳房切除术标本的组织病理学染色进行定量共配准来验证成像指标。为了验证这些假设，我们将追求三个具体目标：1。在通过特定治疗方案选择的患者中，通过肿瘤血管血流和完整性、组织体积分数和肿瘤细胞密度的差异来区分应答者与非应答者。2.在血流、血管灌注、血管外细胞外容积分数、细胞内水寿命和细胞密度之间进行单变量和多变量相关性分析，以更全面地了解乳腺肿瘤环境。3.在体内MR图像和离体组织学标本之间进行定量配准，以确认MRI测量。拟议中的研究将联合收割机几种新的成像方法，以获得有关乳腺肿瘤如何对治疗作出反应的定量信息。我们假设，这将使我们在治疗过程的早期区分反应者和非反应者，以便在个体基础上优化治疗。 公共卫生相关性：拟议中的研究将联合收割机专门的磁共振成像（MRI）方法，以获得人类乳腺肿瘤如何对治疗作出反应的定量信息。开发可以在治疗早期应用以评估反应的肿瘤表征方法将对许多患者的管理产生深远的影响。我们假设对比增强MRI和扩散MRI数据的联合分析将提供肿瘤对治疗反应的预测性、非侵入性测量。

DESCRIPTION (provided by applicant): Currently, breast tumor response to chemotherapy is monitored by frank changes in tumor morphology as measured by physical exam, mammography and/or ultrasound. Clinical judgments of the effectiveness of treatments are subjective and prone to error. A repeatable, non-invasive imaging method which can reliably assess tumor response would greatly improve clinical breast cancer care. The specialized magnetic resonance imaging (MRI) methods of dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) have matured to the point where they offer unique information on tumor status. DCE-MRI reports on relevant physiological parameters including vessel perfusion, vessel wall permeability, extravascular extracellular volume fraction, and (recently) cell size. DW-MRI can provide detailed information on tissue cellularity. We propose to combine a novel analysis of DCE-MRI data with DW-MRI data obtained at 3T to provide functional assessments of the response of breast cancer to treatment. We hypothesize that integrating these quantitative MRI methods will provide accurate and predictive measurements of tumor response after the first cycle of treatment. Furthermore, we will validate the imaging metrics by performing quantitative co- registering the in vivo MR images to histopathological staining of ex vivo mastectomy specimens. To test these hypotheses we will pursue three specific aims: 1. In patients selected by a specific treatment protocol, differentiate responders vs. non-responders by the differences in tumor vessel blood flow and integrity, tissue volume fractions, and tumor cell density. 2. Perform uni- and multi-variate correlation analysis between blood flow, vessel perfusion, extravascular extracellular volume fraction, intracellular water lifetime, and cell density to provide a more complete understanding of the breast tumor environment. 3. Perform quantitative co-registration between in vivo MR images and ex vivo histological specimens to validate the MRI measures. The proposed research will combine several new imaging methods to obtain quantitative information on how breast tumors respond to treatment. We hypothesize that this will let us distinguish responders from non- responders early in the course of treatment so that treatments can be optimized on an individual basis. PUBLIC HEALTH RELEVANCE: The proposed research will combine specialized magnetic resonance imaging (MRI) methods to obtain quantitative information on how human breast tumors respond to treatment. Developing methods of tumor characterization that could be applied early in treatment to assess response would have profound impact on the management of many patients. We hypothesize that the combined analysis of contrast enhanced MRI and diffusion MRI data will provide predictive, non-invasive measurements of tumor response to treatment.