Improved Response Assessment in Anti-Angiogenic Treatment of Glioblastoma

改进胶质母细胞瘤抗血管生成治疗的疗效评估

• 批准号: 9111876
• 负责人: Martin Reuter
• 金额: $ 18.99万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2016-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9111876
• 关键词: Accounting; Address; Algorithms; Area; Award; Blood - brain barrier anatomy; Blood Vessels; Brain; Central Nervous System Neoplasms; Cessation of life; Clinical; Clinical Data; Clinical Trials; Computing Methodologies; Data; Development; Diffusion; Disease; Edema; Engineering; Ensure; Evaluation; Functional Imaging; Glioblastoma; Glioma; Head; Health; Human; Image; Investigational Therapies; Lead; Location; Magnetic Resonance Imaging; Malignant neoplasm of brain; Maps; Measurement; Measures; Mentors; Methods; Modality; Modeling; Modification; Mutation; Oncology Group; Patients; Perfusion; Permeability; Pharmaceutical Preparations; Physiologic Neovascularization; Physiological; Positioning Attribute; Positron-Emission Tomography; Procedures; Recurrence; Regimen; Research; Resistance; Scanning; Slice; Subject Headings; Techniques; Time; Tissues; Training; United States; Validation; Vascular Permeabilities; Visit; angiogenesis; antitumor effect; cancer imaging; chemotherapy; clinical practice; computer science; computerized tools; contrast enhanced; follow-up; image registration; imaging biomarker; imaging modality; improved; individualized medicine; mathematical sciences; neuro-oncology; novel; outcome forecast; outcome prediction; predicting response; programs; prospective; response; skills; tool; treatment response; tumor; tumor microenvironment

DESCRIPTION (provided by applicant): Primary central nervous system tumors cause an estimated 13,000 deaths per year in the United States. Glioblastoma (GBM), the most aggressive malignant brain tumor in humans, has an especially poor prognosis with a median survival time of only 9-15 months following standard therapies. In addition to patient survival, evaluation of treatment response commonly relies on radiographic response rates, specifically changes in the area of contrast enhanced tumor as seen on a single brain MRI slice. This is problematic for several reasons. First, subjects' heads are placed differently in the scanner at each visit, inducing variability in the angle and position of the image slices, leading to an apparent change in tumor area unrelated to treatment. Second, agents used in anti-angiogenic therapies (AAT) cause a pseudoresponse, i.e. a rapid and profound decrease in contrast enhancement due to reduced vascular permeability rather than a true anti-tumor effect. Advanced MRI techniques such as perfusion, permeability, and diffusion imaging, as well as positron emission tomography show promise in predicting response and quantifying changes in non-enhancing tumor components. However, the Response Assessment in Neuro-Oncology (RANO) Group concluded that these techniques are still too limited to derive an objective measure of non-enhancing disease and require rigorous validation before they can be incorporated into response criteria used in clinical trials in high-grade gliomas. This project's core objective is to improve response assessment in AAT. We will address current limitations by (i) reducing acquisition variability via a novel automatic slice prescription method that ensures acquisition of identical image slices as in previous scans of the same patient, (ii) identifying pseudoresponse via quantification of deformations across visits (mass effects) in peritumoral tissue, and (iii) assessing perfusion and correlation with local response in tumor components at the voxel level through a temporal multi-parametric model combining several sequences/modalities and information across visits. Both simulated and retrospective clinical data (40 patients, 10-30 time points) will inform the development of the computational tools and markers. Subsequently, response assessment and outcome prediction in prospective data (48 patients, monthly scans) will be used for validation to address the need for rigorous evaluation. This project will deliver advanced tools that will be made freely available. The candidate brings a strong background in math and computer science and a unique skill set related to the development of computational tools, e.g. image registration. A strong team of mentors and collaborators will support the candidate's training in neuro-oncology, angiogenesis, and physiological and functional imaging modalities such as PET and MR. This award will enable him to focus his future research primarily on questions of health and disease, specifically, to establish an interdisciplinary tumor imaging research program, and to successfully transfer computational methods and imaging biomarkers into clinical practice.

描述（由申请人提供）：在美国，原发性中枢神经系统肿瘤估计每年导致 13,000 人死亡。胶质母细胞瘤 (GBM) 是人类最具侵袭性的恶性脑肿瘤，预后特别差，标准治疗后的中位生存时间仅为 9-15 个月。除了患者生存率之外，治疗反应的评估通常还依赖于放射照相反应率，特别是在单个脑 MRI 切片上看到的对比增强肿瘤区域的变化。由于多种原因，这是有问题的。首先，每次就诊时，受试者的头部在扫描仪中的放置位置不同，导致图像切片的角度和位置发生变化，导致与治疗无关的肿瘤区域发生明显变化。其次，抗血管生成疗法（AAT）中使用的药物会引起假反应，即由于血管通透性降低而导致对比度增强迅速而显着下降，而不是真正的抗肿瘤作用。先进的 MRI 技术，如灌注、渗透性和扩散成像，以及正电子发射断层扫描，在预测反应和量化非增强肿瘤成分的变化方面显示出前景。然而，神经肿瘤学反应评估（RANO）小组得出的结论是，这些技术仍然太有限，无法得出非增强疾病的客观衡量标准，并且需要经过严格验证才能纳入高级别神经胶质瘤临床试验中使用的反应标准。该项目的核心目标是改进 AAT 中的响应评估。我们将通过以下方式解决当前的局限性：（i）通过一种新颖的自动切片处方方法减少采集变异性，该方法确保采集与同一患者先前扫描相同的图像切片，（ii）通过量化肿瘤周围组织中每次访问（质量效应）的变形来识别伪响应，以及（iii）通过时间序列评估体素水平上肿瘤成分的灌注和与局部响应的相关性。 多参数模型结合了多个序列/模式和访问信息。模拟和回顾性临床数据（40 名患者，10-30 个时间点）将为计算工具和标记的开发提供信息。随后，前瞻性数据（48 名患者，每月扫描）中的反应评估和结果预测将用于验证，以满足严格评估的需要。该项目将提供免费提供的高级工具。候选人带来了 强大的数学和计算机科学背景以及与计算工具开发相关的独特技能，例如图像注册。强大的导师和合作者团队将支持候选人在神经肿瘤学、血管生成以及 PET 和 MR 等生理和功能成像模式方面的培训。该奖项将使他能够将未来的研究主要集中在健康和疾病问题上，特别是建立一个跨学科的肿瘤成像研究项目，并成功地将计算方法和成像生物标志物转移到临床实践中。