Real-time In Vivo MRI Biomarkers for Breast Cancer Pre-operative Treatment Trials

描述（由申请人提供）：该项目是UCSF和Sentinelle Medical， Inc.的研究人员之间的学术工业合作伙伴关系（AIP），旨在在现有的图像审查工作站上实现用于评估治疗反应的乳房MR图像分析系统。该项目的目标是通过使成像生物标志物作为乳腺MRI临床工作流程的一部分实时可用，更好地将成像生物标志物整合到临床试验中。加州大学旧金山分校的研究人员领导了一个大型的多中心项目，研究MRI和分子标记来评估乳腺癌对术前化疗的反应。这些努力有望导致后续的适应性设计试验，包括定量MRI测量来评估反应和修改治疗。Sentinelle Medical, Inc.生产一种成像和活检系统，具有先进的乳房磁共振成像和干预功能。该系统包括一个工作站和软件平台Aegis，用于图像检查和活检指导。Aegis是一个符合dicom标准的软件系统，可以与MRI扫描仪和PACS服务器进行通信。在这个AIP项目中，我们建议开发两个新的软件模块，它们将被添加到Aegis平台中，以支持实时图像分析和显示来自动态对比增强（DCE）和其他功能MRI数据的生物标志物数据。Specific Aim 1的目标是开发一个治疗反应模块（TRM），该模块将提供导入和分析图像的能力，并产生彩色显示和数字生物标志物信息。TRM的结果将在检查时立即可用，以指导活检靶向和临床评估治疗反应。Specific Aim 2的目标是开发一个生物标志物优化模块（BOM），设计成一个基于临床试验数据库的回顾性优化、测试和比较成像生物标志物的研究工具，并注释了相关的临床相关性和结果。BOM将允许通过调整生物标志物量化参数来优化生物标志物的性能，例如预测无病生存的能力。BOM还允许比较其他指标，例如，曲线下面积（AUC）和药代动力学参数ktrans，两者都用于量化对比摄取动力学。BOM将支持基于累积临床试验数据的成像指标的培训，并有望导致TRM中提供的参数分析的改进和改进。该AIP的目的是为参与临床试验的站点提供具有TRM和BOM功能的Aegis工作站。

DESCRIPTION (provided by applicant): This project is an academic industrial partnership (AIP) between researchers at UCSF and Sentinelle Medical, Inc., to implement a breast MR image analysis system for assessing response to treatment, on an existing image review workstation. The goal of this project is to better enable the integration of imaging biomarkers into clinical trials by making imaging biomarkers available in real-time as part of the clinical workflow for breast MRI. Investigators at UCSF lead a large multi-center effort investigating MRI and molecular markers for assessing breast cancer response to pre-operative chemotherapy. These efforts are expected to lead to subsequent trials with adaptive designs that incorporate quantitative MRI measurements to assess response and revise treatment. Sentinelle Medical, Inc., manufactures an imaging and biopsy system with advanced capabilities for breast MR imaging and intervention. The system includes a workstation and software platform, Aegis, for image review and biopsy guidance. Aegis is a DICOM-compliant software system that communicates with both the MRI scanner and PACS server. We propose in this AIP project to develop two new software modules that will be added to the Aegis platform to support real-time image analysis and display of biomarker data derived from dynamic contrast-enhanced (DCE) and other functional MRI data. The goal of Specific Aim 1 is to develop a therapeutic response module (TRM) that will provide capabilities for importing and analyzing images, and producing colorized displays and numeric biomarker information. Output from the TRM will be immediately available at the time of the exam to direct biopsy-targeting and for clinical assessment of treatment response. The goal of Specific Aim 2 is to develop a biomarker optimization module (BOM), designed to be a research tool for retrospective optimization, testing and comparison of imaging biomarkers based on clinical trial databases annotated with relevant clinical correlates and outcomes. The BOM will allow biomarker performance, for example, ability to predict disease-free survival, to be optimized by adjusting biomarker quantification parameters. The BOM will also allow alternative metrics to be compared, for example, area under the curve (AUC) and the pharmacokinetic parameter ktrans, both used to quantify contrast uptake kinetics. The BOM will support the training of imaging metrics based on accumulated clinical trials data and is expected to lead to refinements and improvements in the parametric analyses provided in the TRM. It is the intent of this AIP to provide Aegis workstations with TRM and BOM capabilities to the participating clinical trial sites. PUBLIC HEALTH RELEVANCE Non-invasive imaging methods have the potential to accelerate the evaluation of new cancer treatments. Molecular and functional imaging techniques can provide sensitive measures of treatment effects and can serve as in vivo biomarkers of response to treatment. The goal of this academic-industrial partnership is to advance breast MRI technology to allow imaging biomarkers to be measured in the context of clinical trials for real-time assessment of response and determination of treatment modifications. This project will also support the optimization and comparison of MRI-based imaging biomarkers based on clinical outcomes.