Characterization and Quantification of Treatment Response for Patients with Glioblastoma multiforme by Referencing to a multi-scale Atlas of Anatomic and Metabolic Imaging Data

通过参考多尺度解剖和代谢成像数据图谱来表征和量化多形性胶质母细胞瘤患者的治疗反应

• 批准号: 318380466
• 负责人: Dr. Stojan Maleschlijski
• 金额: --
• 依托单位: Department of Radiology and Biomedical Imaging
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 无数据
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/318380466?language=en
• 关键词: Characterization; Quantification; Treatment; Response; Patients

The median survival for adult patients with high-grade glioma (GBM) following standard of care treatment is 14 months. A hallmark of GBM is the presence of abnormal microvasculature, increased vasogenic edema and a high degree of infiltration into normal brain. While anatomic images are widely used for diagnosis and clinical assessment of these features, the changes observed following treatment are non-specific and may represent a combination of tumor with gliosis, edema, necrosis and inflammation. A promising technology for resolving such ambiguities is magnetic resonance spectroscopic imaging (MRSI), which provides information about the biological and metabolic properties of the tissue. Generally, brain diseases like tumors or multiple sclerosis, cause strong metabolic changes in the brain, which makes them very suited for MRSI characterization. However, MR spectroscopic imaging is still not widely used as a standard modality in clinical context. One reason for this discrepancy is that the method is technically challenging, which requires additional training and expertise. In addition, the lack of a standard reference framework, which defines the normal values of metabolic information at different spatial locations, makes it prone to evaluation uncertainties due to the subjective component of the viewer.The objective of this project is to provide a reference framework for the automated analysis of brain MRSI data from patients with GBM by providing reliable and robust metrics that describe the spatial and temporal changes in the lesion and surrounding tissue. The first step will be to obtain MRSI data from healthy volunteers in order to construct a metabolic atlas of the normal human brain, which will be provided to the research community upon validation. Furthermore, the atlas will be used as a reference standard to identify abnormal patterns in MRSI data obtained from over 400 patients at UCSF and link them to histological properties from image-guided tissue samples. The relationships that are established will allow classification algorithms to be developed for assessing response to treatment and evaluating tumor progression in serial patient scans. The methods will provide objective criteria for predicting outcome and characterizing new therapies. The findings will improve diagnosis and assist in selecting appropriate treatments, as well as accelerating the discovery and evaluation of improved treatments.

高级别胶质瘤（GBM）成人患者接受标准治疗后的中位生存期为14个月。GBM的标志是存在异常微血管、增加的血管源性水肿和高度浸润到正常脑中。虽然解剖图像被广泛用于这些特征的诊断和临床评估，但治疗后观察到的变化是非特异性的，可能代表肿瘤与神经胶质增生、水肿、坏死和炎症的组合。解决此类模糊性的一项有前途的技术是磁共振光谱成像（MRSI），它提供有关组织生物学和代谢特性的信息。 一般来说，脑部疾病如肿瘤或多发性硬化症，会导致大脑中强烈的代谢变化，这使得它们非常适合MRSI表征。然而，MR光谱成像仍然没有被广泛用作临床背景中的标准模态。造成这种差异的一个原因是，这种方法在技术上具有挑战性，需要额外的培训和专门知识。此外，缺乏标准的参考框架，该框架定义了不同空间位置的代谢信息的正常值，该项目的目的是通过提供可靠和鲁棒的度量来描述GBM患者的空间和时间变化，从而为自动分析脑MRSI数据提供参考框架在病变和周围组织中。第一步将是从健康志愿者中获得MRSI数据，以构建正常人脑的代谢图谱，该图谱将在验证后提供给研究界。此外，该图谱将被用作参考标准，以识别从UCSF的400多名患者中获得的MRSI数据中的异常模式，并将其与图像引导组织样本的组织学特性联系起来。建立的关系将允许开发分类算法，用于评估对治疗的反应和评价连续患者扫描中的肿瘤进展。这些方法将为预测结果和表征新疗法提供客观标准。这些发现将改善诊断，帮助选择适当的治疗方法，并加速发现和评估改进的治疗方法。