Quantitative Imaging for Assessing Breast Cancer Response to Treatment

用于评估乳腺癌治疗反应的定量成像

• 批准号: 10478050
• 负责人: Nola M. Hylton-Watson
• 金额: $ 62.6万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478050
• 关键词: Address; Adoption; Adverse effects; American College of Radiology Imaging Network; Award; Benchmarking; Benign; Breast; Breast Cancer Patient; Breast Cancer Treatment; Breast Magnetic Resonance Imaging; Cellularity; Clinical; Clinical Trials; Collaborations; Data; Diagnosis; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Effectiveness; Error Sources; Evaluation; Funding; Goals; Grant; Image; Image Enhancement; Imaging Phantoms; In complete remission; Industrialization; Institutes; Lesion; Magnetic Resonance Imaging; Malignant - descriptor; Measurement; Measures; Methods; Michigan; Modeling; Modification; Neoadjuvant Therapy; Neurologic; Operative Surgical Procedures; Outcome; Pathologic; Patients; Performance; Perfusion; Physiologic pulse; Precision therapeutics; Process; Process Assessment; Protocols documentation; Quality Control; Recurrence; Reproducibility; Research; Resolution; Risk; Scanning; Site; Standardization; System; Techniques; Technology; Testing; Time; Universities; Vendor; Washington; Work; automated segmentation; base; biomarker performance; chemotherapy; clinical center; clinical implementation; clinical practice; cohort; contrast enhanced; data acquisition; image registration; imaging biomarker; imaging modality; improved; individualized medicine; industry partner; malignant breast neoplasm; personalized strategies; phase II trial; pilot test; predicting response; predictive modeling; prevent; programs; quality assurance; quantitative imaging; response; survival prediction; targeted agent; tool; treatment response; treatment strategy; tumor; tumor heterogeneity

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.

摘要/摘要 该项目的目标是实施有效的、基于成像的策略，结合 DCE-MRI 和 DWI 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 资助奖 CA151235 的基础上，题为“定量 用于评估乳腺癌治疗反应的成像”并满足改进的需求 乳房 MRI 的准确性、标准化和一致性，用于对治疗进行定量评估 多个临床中心的反应。新的QIN项目将继续推进定量MRI I-SPY 2 TRIAL 是一项针对乳腺癌靶向药物的适应性 II 期试验。 我们将使用应用于扩展 I-SPY 2 队列的诊断模型，以最大限度地提高生物标志物 成像测量的性能并构建决策工具以实现合理的策略 治疗修改。在之前的工作中，我们开发并实施了图像质量控制和评估 美国放射学院使用的乳腺弥散加权 MRI (DWI) 流程 成像网络 (ACRIN) 试验 6698，I-SPY 2 的成像子研究，测试 DWI 以预测反应。 初步结果表明，表观扩散系数 (ADC) 测量具有出色的重复性 标准化 4 b 值方案，并且发现治疗后 ADC 的变化可预测病理变化 complete response (pCR).与此同时，我们与密歇根大学的 QIN 合作者进行了合作， 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院 (NIST) 合作 开发通用乳腺 MRI 模体，以实现临床试验中乳腺 MRI 的标准化。新款U01 项目将在 I-SPY 2 的多个供应商平台上评估这些方法，特别关注 最大限度地提高乳腺 DCE-MRI 和 DWI 的综合性能。在具体目标 1 下，我们建议 通过实施更先进的 DWI 脉冲序列技术（多 b值DWI和高空间分辨率DWI）并校正已知的系统误差（梯度非线性 和 B0 不均匀性）。我们还将实施基于模型的质量保证流程来评估 所有站点的脉冲序列性能，目的是识别和纠正平台偏差和变异性 ADC 测量和建立数据接受的质量基准。具体目标 2 将重点关注 通过将 DWI 联合配准与动态对比增强 (DCE) 相结合来改进 ADC 定量 图像，以及测量肿瘤 ADC 异质性的自动分割技术。我们预计 这些在图像采集、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 中病理反应和生存的预测模型。