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: 9769672
• 负责人: Nola M. Hylton-Watson
• 金额: $ 62.73万
• 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/9769672
• 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; base; biomarker performance; chemotherapy; 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; 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.

总结/摘要 本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 建立在先前的NCI定量成像网络（QIN）U 01资助奖CA 151235的基础上， 评估乳腺癌对治疗的反应的成像”，并解决了改善 乳腺MRI的准确性、标准化和一致性，以进行治疗的定量评估 多个临床中心的反应。新的QIN项目将继续推进定量MRI 方法在I-SPY 2试验的背景下，一个适应性的II期试验的靶向药物乳腺癌。 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， 治疗修改在先前的工作中，我们开发并实施了图像质量控制和评估 美国放射学会使用的乳腺扩散加权MRI（DWI）流程 成像网络（ACRIN）试验6698，一项I-SPY 2测试DWI预测缓解的成像子研究。 初步结果显示，使用表观扩散系数（ADC）测量的可重复性极佳， 标准化的4 b值方案，ADC值随治疗的变化被认为是病理性的预测。 完全缓解（pCR）。在平行的努力中，我们与密歇根大学的QIN合作者合作， 工业合作伙伴开发梯度非线性校正和B 0不均匀性校正方法， ADC定量。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体模，用于临床试验中乳腺MRI的标准化。新U 01 该项目将在I-SPY 2中的多个供应商平台上评估这些方法，特别关注 最大化乳腺DCE-MRI和DWI的组合性能。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（多个 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 B 0不均匀性）。我们还将实施基于体模的质量保证流程， 所有站点的脉冲序列性能，目的是识别和纠正平台偏差和可变性 在ADC测量和建立数据验收的质量基准。具体目标2将侧重于 通过结合DWI与动态对比增强（DCE）的共配准来改善ADC定量 图像，以及自动分割技术来测量肿瘤ADC的异质性。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， 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中病理反应和存活的预测模型。