Correction of Diffusion Gradient Bias in Quantitative Diffusivity Metrics for MultiPlatform Clinical Oncology Trials

多平台临床肿瘤学试验定量扩散率指标中扩散梯度偏差的校正

• 批准号: 10455475
• 负责人: THOMAS L CHENEVERT
• 金额: $ 63.54万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-10 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10455475
• 关键词: Address; Adoption; Agreement; American College of Radiology Imaging Network; Benchmarking; Biological; Bone Marrow; Breast; California; Cancer Detection; Cancer Patient; Clinical; Clinical Management; Clinical Trials; Clinical Trials Cooperative Group; Collaborations; Complex; Data; Detection; Development; Diagnosis; Diagnostic; Diffuse; Diffusion; Early treatment; Engineering; Ensure; Generations; Goals; Head and neck structure; Image; Imaging technology; Industrialization; Institution; Left; Lesion; Magnetic Resonance Imaging; Malignant Neoplasms; Maps; Memorial Sloan-Kettering Cancer Center; Michigan; Modeling; Monitor; Multi-Institutional Clinical Trial; Organ; Output; Parents; Pathology; Patients; Performance; Procedures; Protocols documentation; Radiation; Radiology Specialty; Reproducibility; Research; Research Personnel; Route; San Francisco; Scanning; Scientist; Site; Source; Standardization; System; Systematic Bias; Technology; Testing; Therapeutic; Time; Tissue Model; Tissues; Translations; Universities; Variant; Vendor; Washington; Weight; base; cancer biomarkers; cancer clinical trial; cancer imaging; clinical application; clinical center; clinical decision-making; clinical imaging; cohort; cost; design; flexibility; image registration; imaging capabilities; imaging facilities; imaging platform; improved; industry partner; malignant breast neoplasm; non-Gaussian model; oncology trial; parent project; predicting response; prognostic; prospective; prototype; public health relevance; quantitative imaging; success; tool; translational cancer research; translational oncology; treatment response; tumor

Abstract Multi-center clinical trials increasingly utilize quantitative diffusion imaging (DWI) to aid in patient management and treatment response assessment for translational oncology applications. A major source of systematic bias in diffusion was discovered originating from platform-dependent gradient hardware. Left uncorrected, these biases confound quantitative diffusion metrics used for characterization of tissue pathology and treatment response leading to inconclusive findings, and increasing the requisite subject numbers and trial cost. Technical remedy was defined by University of Michigan investigators and motivated our parent AIP involving the three dominant MRI manufactures. The current AIP merged expertise among commercial scientists/engineers and academic researchers, and resulted in successful design and development of prototype correction tools to eliminate systematic diffusion weighting bias in quantitative DWI applications across diverse clinical MRI platforms. As a result, two vendors have implemented prototype tools on their respective scanner platforms for correction of mean tissue diffusivity metric widely used in oncology trials. Furthermore, feasibility of retrospective correction across all three vendor platforms was demonstrated for the repeatability cohort of ACRIN 6698 breast cancer imaging trial. Our AIP participation with clinical trial cooperative groups and quantitative imaging consortia revealed that the most efficient route for adoption of developed technology on clinical platforms is by vendor implementation. Additional need was noted for flexible integration with advanced acquisition protocols and analyses using multiple b-values to accurately quantify complex metrics beyond mean diffusivity. To address these needs, the renewal AIP will extend our collaborations to include two additional academic cancer imaging centers to integrate and validate developed practical correction tools in five active clinical oncology trials, as well as enable application to advanced tissue diffusivity models. These tools will eliminate systematic cross-platform, cross-exam variability to facilitate longitudinal and multi-institutional translational cancer research that utilize quantitative diffusivity metrics. Success of this project will further enhance accuracy and precision of cancer detection and monitoring. These goals will be achieved through Aim1: deployment of DWI bias correction tools for application in multiple cancer imaging trials, and through Aim2: correction integration with advanced DWI protocols and tissue models. Academic team of the proposed partnership consists of recognized experts in quantitative diffusion imaging standardization and translation to clinical oncology trials. The PI institution has active research agreements with three dominant clinical MRI manufactures with prior record of successful implementations for the developed technologies. Accomplishment of the project goals will eliminate significant instrumental bias that confounds current multi-center/multi-platform clinical trials that employ quantitative DWI.

抽象的 多中心临床试验越来越多地利用定量扩散成像 (DWI) 来帮助患者管理 以及转化肿瘤学应用的治疗反应评估。系统偏差的主要来源 扩散被发现源自依赖于平台的梯度硬件。如果不加以纠正，这些 偏差混淆了用于表征组织病理学和治疗的定量扩散指标 反应导致不确定的结果，并增加必要的受试者数量和试验成本。技术的 补救措施是由密歇根大学研究人员定义的，并促使我们的家长 AIP 涉及这三个方面 占主导地位的 MRI 制造商。当前的 AIP 融合了商业科学家/工程师和 学术研究人员，并成功设计和开发了原型校正工具 消除不同临床 MRI 定量 DWI 应用中的系统扩散加权偏差 平台。因此，两家供应商在各自的扫描仪平台上实现了原型工具 校正肿瘤学试验中广泛使用的平均组织扩散率指标。此外，追溯的可行性 ACRIN 6698 乳房的可重复性队列证明了所有三个供应商平台的校正 癌症成像试验。我们的AIP参与临床试验合作组和定量成像 联盟透露，在临床平台上采用已开发技术的最有效途径是 供应商实施。还注意到与先进采集协议的灵活集成的额外需求 并使用多个 b 值进行分析，以准确量化平均扩散率之外的复杂指标。致地址 为了满足这些需求，更新的 AIP 将扩大我们的合作范围，包括另外两项学术癌症成像 中心还在五项活跃的临床肿瘤学试验中整合和验证开发的实用校正工具 能够应用于先进的组织扩散模型。这些工具将消除系统性的跨平台、 交叉检查变异性，以促进纵向和多机构转化癌症研究，利用 定量扩散指标。该项目的成功将进一步提高癌症诊断的准确性和精确度 检测和监测。这些目标将通过目标 1 来实现：部署 DWI 偏差校正工具 用于多种癌症成像试验，并通过 Aim2：与先进 DWI 的校正集成 协议和组织模型。 拟议合作伙伴关系的学术团队由定量扩散成像领域的知名专家组成 标准化并转化为临床肿瘤学试验。 PI 机构与以下机构签订了积极的研究协议： 三个主要的临床 MRI 制造商之前都有成功实施开发的记录 技术。项目目标的实现将消除令人困惑的重大工具偏见 目前采用定量 DWI 的多中心/多平台临床试验。