Quantitative Imaging for Assessing Breast Cancer Response to Treatment

摘要/摘要 该项目的目标是实施有效的、基于成像的策略，将DCE-MRI和DWI相结合，以 评估接受术前(新辅助)化疗的乳腺癌患者的反应。这个项目 建立在之前的NCI定量成像网络(QIN)U01奖CA151235的基础上 用于评估乳腺癌治疗反应的成像“，并解决了改进的需求 乳腺MRI定量评估治疗的准确性、标准化和一致性 跨多个临床中心的响应。新的QIN项目将继续推进定量MRI 方法在I-SPY 2试验的背景下，这是一项针对乳腺癌靶向药物的适应性II期试验。 我们将使用应用于不断扩大的i-spy 2队列的诊断模型来最大化生物标记。 成像测量的性能，并构建决策工具以支持Rational策略 治疗改进。在以前的工作中，我们开发并实现了图像质量控制和评估 美国放射学会使用的乳腺弥散加权磁共振成像(DWI)方法 成像网络(ACRIN)试验6698，I-SPY 2测试DWI用于预测反应的成像子研究。 初步结果表明，表观扩散系数(ADC)测量的良好重复性使用 标准化的4b值方案，治疗过程中ADC的变化被发现是病理的预测 完全应答(PCR)。在并行的努力中，我们与密歇根大学的秦合作者和 工业合作伙伴开发梯度非线性校正和B0不均匀性校正方法 ADC量化。我们还与美国国家标准与技术研究所(NIST)合作 研制一种通用的乳腺MRI体模，用于临床试验中乳腺MRI的标准化。新款U01 Project将在i-spy 2中的多个供应商平台上评估这些方法，重点是 最大限度地发挥乳腺DCE-MRI和DWI的综合性能。在具体目标1下，我们建议 通过实施更高级的DWI脉冲序列技术(多 B值DWI和高空间分辨率DWI)和校正已知系统误差(梯度非线性 和B0不均质性)。我们还将实施基于幻影的质量保证流程，以评估 所有地点的脉冲序列性能，目标是识别和纠正平台偏差和可变性 在ADC测量方面，并为数据接受建立质量基准。《特定目标2》将专注于 结合动态对比度增强(DCE)的DWI联合配准改善ADC定量 图像，以及自动分割技术，以衡量肿瘤ADC的异质性。我们期待着 在图像采集、标准化、质量基准和像素使用方面的集体改进- 基于指标的指标将导致ADC测量的整体改进。改进后的指标将在 I-spy 2中病理反应和存活率的预测模型。

Summary/Abstract The goal of this project is to implement effective, imaging-based strategies combining DCE-MRI and DWI to assess response for breast cancer patients receiving pre-operative (neoadjuvant) chemotherapy. This project builds on the prior NCI Quantitative Imaging Network (QIN) U01 grant award CA151235 entitled “Quantitative Imaging for Assessing Breast Cancer Response to Treatment” and addresses the needs for improved accuracy, standardization and consistency of breast MRI to perform quantitative assessment of treatment response across multiple clinical centers. The new QIN project will continue to advance quantitative MRI methods in the context of the I-SPY 2 TRIAL, an adaptive Phase II trial of targeted agents for breast cancer. We will use diagnostic models applied to the expanding I-SPY 2 cohorts to maximize the biomarker performance of imaging measurements and to construct decision tools to enable rational strategies for treatment modification. In prior work we developed and implemented image quality control and assessment processes for breast diffusion-weighted MRI (DWI) that were utilized in the American College of Radiology Imaging Network (ACRIN) trial 6698, an imaging sub-study of I-SPY 2 testing DWI for prediction of response. Initial results showed excellent repeatability of apparent diffusion coefficient (ADC) measurements using a standardized 4 b-value protocol, and change in ADC with treatment was found to be predictive of pathologic complete response (pCR). In parallel efforts, we worked with QIN collaborators at University of Michigan and industrial partners to develop gradient non-linearity correction and B0 inhomogeneity correction methods for ADC quantification. We also collaborated with the National Institute of Standards and Technology (NIST) to develop a universal breast MRI phantom for standardization of breast MRI in clinical trials. The new U01 project will evaluate these methods on the multiple vendor platforms in I-SPY 2 with particular focus on maximizing the combined performance of breast DCE-MRI and DWI. Under Specific Aim 1, we propose to gain performance improvements by implementing more advanced DWI pulse sequence techniques (multi b-value DWI and high spatial resolution DWI) and correcting known systematic errors (gradient non-linearity and B0 inhomogeneity). We will additionally implement a phantom-based quality assurance process to evaluate pulse sequence performance at all sites, with the goal of identifying and correcting platform bias and variability in ADC measurement and establishing quality benchmarks for data acceptance. Specific Aim 2 will focus on improving ADC quantitation by incorporating co-registration of DWI to dynamic contrast-enhanced (DCE) images, as well as automated segmentation techniques to measure heterogeneity in tumor ADC. We anticipate that these collective improvements in image acquisition, standardization, use of quality benchmarks and pixel- based metrics will lead to overall improvements in ADC measurement. The improved metrics will be tested in predictive models for pathologic response and survival in I-SPY 2.