Improved Response Assessment in Anti-Angiogenic Treatment of Glioblastoma

描述（由申请人提供）：在美国，原发性中枢神经系统肿瘤每年导致约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，其预后特别差，标准治疗后的中位生存期仅为9-15个月。除了患者生存之外，对治疗反应的评估通常依赖于x线片反应率，特别是单张脑MRI片上对比增强肿瘤区域的变化。这是有问题的，原因有几个。首先，每次访问时，受试者的头部在扫描仪中的位置不同，导致图像切片的角度和位置变化，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深刻地降低，而不是真正的抗肿瘤效果。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，在预测非增强性肿瘤成分的反应和量化变化方面表现出了希望。然而，神经肿瘤学反应评估（RANO）小组得出结论，这些技术仍然过于有限，无法得出非增强性疾病的客观测量，并且需要严格的验证才能将其纳入用于高级别胶质瘤临床试验的反应标准。该项目的核心目标是改进AAT的反应评估。我们将通过以下方法解决当前的局限性：(i)通过一种新的自动切片处方方法来减少采集的可变性，该方法确保获得与同一患者先前扫描相同的图像切片；（ii）通过对肿瘤周围组织的访问变形（质量效应）进行量化来识别假反应。（iii）通过结合多个序列/模式和访问信息的时间多参数模型，在体素水平上评估肿瘤成分的灌注及其与局部反应的相关性。模拟和回顾性临床数据（40例患者，10-30个时间点）将为计算工具和标记物的开发提供信息。随后，前瞻性数据（48例患者，每月扫描）的反应评估和结果预测将用于验证，以解决严格评估的需要。该项目将提供免费提供的先进工具。候选人带来了

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.