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: 10250327
• 负责人: KATHLEEN Marie SCHMAINDA
• 金额: $ 53.72万
• 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/10250327
• 关键词: Adoption; Affect; American College of Radiology Imaging Network; Biological Markers; Brain Neoplasms; 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; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methods; Patients; Perfusion; Prediction of Response to Therapy; Predisposition; Radiation Therapy Oncology Group; Reporting; Research; Restriction Spectrum Imaging; Technology; Testing; Time; Translating; Tumor Burden; Update; Validation; anatomic imaging; base; bevacizumab; 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.

摘要 我们的研究计划的持续目标是优化和传播有效的基于成像的 个性化脑肿瘤治疗的策略。当前神经肿瘤缓解评估（RANO） 仅结合解剖成像的标准不足以区分肿瘤和治疗效果 （TE）。在没有明确确认肿瘤进展的情况下，不建议对几种 几个月后，标准治疗。因此，患者无法转换到可能更有效的药物， 这一局限性可以通过更可靠的成像技术来克服。 为此，在上一个资助周期中，我们展示了几种定量成像的可行性 (QI)可靠区分肿瘤与治疗效果并预测治疗反应的工具。这些QI工具 包括机器学习方法来校准T1 w图像，从而能够创建定量Δ T1 （qDT 1）图谱。qDT 1能够检测真实造影增强病变体积（CELV）。QDT1 与我们已证实的动态磁化率对比（DSC）MRI方法一起，用于确定rCBV（相对 脑血容量），用于产生一种新的生物标志物，肿瘤负荷分数（FTB）， CELV内肿瘤的范围（基于体素）。这些基于灌注的QI工具与我们的 弥散MRI技术，包括功能性弥散图（FDM）和最近的RSI（限制 光谱成像），提供脑肿瘤综合评估及其与治疗效果的区别。 现在，为了将这项技术用于临床试验和日常实践，一些最终的更新和 如本文所述，需要进行临床验证研究。首先，为了便于在临床环境中采用和测试 沿着提出了对各个QI技术的改进， 工作流程（目标1）。为了提高DSC-MRI和FTB生物标志物的广泛采用，将进行研究 进行，以确认单剂量DSC-MRI方法可以取代标准的双剂量方法， 影响rCBV的准确性或FTB图的创建（目标1.1）。此外，配准和分割 算法将被更新，以包括可变形配准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（目标1.2）。 最后，将建立一个包含这些改进的简化工作流程（目标1.3）。Aim 2研究 将使用临床试验数据（目标2.1-2.2）和日常临床实践（目标2.3-2.4）测试QI工具和工作流程。 这种新QI-RANO工作流程的临床验证，有证据表明，相比之下， 目前的措施，有可能导致一个范式转变，在如何评估脑肿瘤负担。