Quantitative (Perfusion and Diffusion) MRI Biomarkers to Measure Glioma Response

用于测量神经胶质瘤反应的定量（灌注和扩散）MRI 生物标志物

• 批准号: 10683139
• 负责人: KATHLEEN Marie SCHMAINDA
• 金额: $ 51.69万
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-28 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683139
• 关键词: Adoption; Affect; American College of Radiology Imaging Network; Biological Markers; Brain Neoplasms; Calibration; Clinical; Clinical Trials; Collaborations; Computer software; Data; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dose; Enhancing Lesion; Funding; Glioma; Goals; Image; Imaging Device; Imaging Techniques; Imaging technology; Individual; Industrialization; Lesion; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methods; Patients; Perfusion; Prediction of Response to Therapy; Predisposition; Radiation Therapy Oncology Group; Recommendation; Reporting; Research; Restriction Spectrum Imaging; Technology; Testing; Time; Translating; Tumor Burden; Update; Validation; anatomic imaging; bevacizumab; blood fractionation; cerebral blood volume; chemoradiation; clinical practice; deep learning; effective therapy; imaging modality; improved; magnetic resonance imaging biomarker; neuro-oncology; novel; outcome prediction; predicting response; programs; quantitative imaging; response; segmentation algorithm; standard of care; tool; treatment effect; treatment response; tumor; tumor progression; validation studies

Abstract The continuing goal of our research program is to optimize and disseminate effective imaging-based strategies to personalize brain tumor treatment. Current Response Assessment in NeuroOncology (RANO) criteria, which incorporate anatomic imaging only, are insufficient for distinguishing tumor from treatment effect (TE). Without definitive confirmation of tumor progression, no treatment changes are recommended for several months after standard therapies. Thus, patients are precluded from switching to potentially more effective therapies—a limitation that could be overcome with more reliable imaging techniques. To this end, during the previous funding cycle, we demonstrated the feasibility of several quantitative imaging (QI) tools to reliably distinguish tumor from treatment effect and predict treatment response. These QI tools include a machine-learning approach to calibrate T1w images enabling the creation of quantitative delta T1 (qDT1) maps. The qDT1 enable the detection of true contrast enhancing lesion volume (CELV). The qDT1 together with our proven dynamic susceptibility contrast (DSC) MRI methods, for determination of rCBV (relative cerebral blood volume), are used to generate a new biomarker, fractional tumor burden (FTB), to delineate the extent of tumor within CELV on a voxel-wise basis. These perfusion-based QI tools in combination with our diffusion MRI technology, which includes functional diffusion maps (FDMs) and more recently RSI (restriction spectrum imaging), provide a comprehensive assessment of brain tumor and its distinction from treatment effect. Now, in order to translate this technology for use in clinical trials and daily practice, some final updates and clinical validation studies are needed as proposed here. First, to ease adoption and testing in the clinical setting improvements are proposed for the individual QI technologies along with the development of a streamlined workflow (Aim 1). To improve the widespread adoption of DSC-MRI and FTB biomarker, studies will be performed to confirm that a single-dose DSC-MRI method can replace the standard double-dose method without affecting the accuracy of rCBV or the creation of FTB maps (Aim 1.1). Also, registration and segmentation algorithms will be updated to include deformable registration and recent advances in deep learning for longitudinal reporting of CELV, non-enhancing lesion volumes (NELV) and each of the QI metrics (Aim 1.2). Finally, a streamlined workflow that incorporates these improvements will be created (Aim 1.3). The Aim 2 studies will test the QI tools and workflow using clinical trial data (Aim 2.1-2.2) and daily clinical practice (Aim 2.3-2.4). Clinical validation of this new QI-RANO workflow, with evidence showing improved prediction in comparison to current measures, has the potential to cause a paradigm shift in how brain tumor burden is assessed.

抽象的 我们的研究计划的持续目标是优化和传播基于成像的有效 个性化脑肿瘤治疗的策略。神经学（RANO）的当前反应评估 仅包含解剖成像的标准不足以区分肿瘤和治疗效果 （TE）。没有明确确认肿瘤进展，建议几种治疗变化 标准疗法后的几个月。这是将患者从切换到潜在更有效的排除 疗法 - 可以通过更可靠的成像技术克服的限制。 为此，在上一个融资周期中，我们证明了几种定量成像的可行性 （QI）可靠地区分肿瘤和治疗效果并预测治疗反应的工具。这些Qi工具 包括一种校准T1W图像的机器学习方法，以创建定量Delta T1 （QDT1）地图。 QDT1可以检测真正的对比度增强病变体积（CELV）。 QDT1 以及我们经过验证的动态敏感性对比（DSC）MRI方法，用于确定RCBV（相对 脑血容量），用于生成新的生物标志物，分数肿瘤伯嫩（FTB），以描绘 CELV内肿瘤的程度以素为基础。这些基于灌注的Qi工具与我们的 扩散MRI技术，其中包括功能扩散图（FDM）和最近的RSI（限制 光谱成像），对脑肿瘤及其与治疗效果的区别进行全面评估。 现在，为了将这项技术转化为用于临床试验和日常练习，一些最终更新和 如这里提出的，需要临床验证研究。首先，在临床环境中简化采用和测试 提出了针对单个Qi技术的改进，并开发了流线型 工作流（目标1）。为了改善DSC-MRI和FTB生物标志物的宽度采用，研究将是 执行以确认单剂量DSC-MRI方法可以替换标准的双剂量方法 影响RCBV的准确性或创建FTB地图（AIM 1.1）。此外，注册和细分 算法将更新以包括可变形注册和深度学习的最新进展 CELV，非增强病变体积（NELV）和每个QI指标的纵向报告（AIM 1.2）。 最后，将创建一个简化的工作流程（AIM 1.3）。目标2研究 将使用临床试验数据（AIM 2.1-2.2）和每日临床实践（AIM 2.3-2.4）测试QI工具和工作流程。 对这种新的Qi-Rano工作流的临床验证，并有证据表明预测的改进 对于当前的措施，有可能导致评估脑肿瘤灼伤的范式转移。

项目成果

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies.

• DOI: 10.1117/1.jmi.5.1.011006
• 发表时间: 2018-01
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Malyarenko D;Fedorov A;Bell L;Prah M;Hectors S;Arlinghaus L;Muzi M;Solaiyappan M;Jacobs M;Fung M;Shukla-Dave A;McManus K;Boss M;Taouli B;Yankeelov TE;Quarles CC;Schmainda K;Chenevert TL;Newitt DC
• 通讯作者: Newitt DC

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network.

• DOI: 10.1117/1.jmi.5.1.011003
• 发表时间: 2018-01
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Newitt DC;Malyarenko D;Chenevert TL;Quarles CC;Bell L;Fedorov A;Fennessy F;Jacobs MA;Solaiyappan M;Hectors S;Taouli B;Muzi M;Kinahan PE;Schmainda KM;Prah MA;Taber EN;Kroenke C;Huang W;Arlinghaus LR;Yankeelov TE;Cao Y;Aryal M;Yen YF;Kalpathy-Cramer J;Shukla-Dave A;Fung M;Liang J;Boss M;Hylton N
• 通讯作者: Hylton N

Case report: Fractional brain tumor burden magnetic resonance mapping to assess response to pulsed low-dose-rate radiotherapy in newly-diagnosed glioblastoma.

• DOI: 10.3389/fonc.2022.1066191
• 发表时间: 2022
• 期刊: Frontiers in oncology
• 影响因子: 4.7

Spiral Perfusion Imaging With Consecutive Echoes (SPICE™) for the Simultaneous Mapping of DSC- and DCE-MRI Parameters in Brain Tumor Patients: Theory and Initial Feasibility.

• DOI: 10.18383/j.tom.2016.00217
• 发表时间: 2016-12
• 期刊: Tomography (Ann Arbor, Mich.)
• 作者: Paulson ES;Prah DE;Schmainda KM
• 通讯作者: Schmainda KM

Spatial discrimination of glioblastoma and treatment effect with histologically-validated perfusion and diffusion magnetic resonance imaging metrics.

• DOI: 10.1007/s11060-017-2617-3
• 发表时间: 2018-01
• 期刊: Journal of neuro-oncology
• 影响因子: 3.9
• 作者: Prah MA;Al-Gizawiy MM;Mueller WM;Cochran EJ;Hoffmann RG;Connelly JM;Schmainda KM
• 通讯作者: Schmainda KM