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

接受标准治疗的成年高级别胶质瘤(GBM)患者的中位生存期为14个月。基底膜的一个特征是存在异常的微血管，血管源性水肿增加，正常脑组织高度浸润。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，可能代表肿瘤合并胶质细胞增生、水肿、坏死和炎症。核磁共振光谱成像(MRSI)是解决这种模糊性的一种很有前途的技术，它提供了关于组织的生物和代谢特性的信息。一般来说，肿瘤或多发性硬化症等脑部疾病会导致大脑中强烈的新陈代谢变化，这使得它们非常适合进行磁共振成像表征。然而，磁共振波谱成像在临床上仍然没有被广泛用作一种标准方式。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业知识。此外，缺乏一个标准的参考框架来定义不同空间位置的代谢信息的正常值，这使得评估容易由于观察者的主观成分而产生不确定性。本项目的目的是通过提供可靠和健壮的度量来描述病变和周围组织的时空变化，为自动分析来自GBM患者的脑MRSI数据提供参考框架。第一步将是从健康志愿者那里获得磁共振成像数据，以构建正常人脑的代谢图谱，经验证后将提供给研究界。此外，该图谱将被用作参考标准，以识别从加州大学旧金山分校400多名患者获得的磁共振成像数据中的异常模式，并将它们与图像引导组织样本的组织学特性联系起来。建立的关系将使分类算法得以开发，用于评估治疗反应和评估连续患者扫描中的肿瘤进展。这些方法将为预测结果和确定新疗法的特征提供客观标准。这些发现将改善诊断，帮助选择适当的治疗方法，以及加快发现和评估改进的治疗方法。

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.