Improved Management of GBM by Integrating Imaging and Tissue Analysis

通过整合成像和组织分析改善 GBM 的管理

• 批准号: 9317436
• 负责人: SARAH J. NELSON
• 金额: $ 58.4万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-27 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9317436
• 关键词: Algorithms; Biologic Characteristic; Brain; Characteristics; Choline; Clinical; Creatine; Custom; Data; Data Quality; Evaluation; Excision; Functional Imaging; Genomics; Glioblastoma; Goals; Heterogeneity; Histologic; Image; Imaging Device; Imaging Techniques; Imaging technology; Individual; Inositol; Instruction; Lesion; Link; Malignant - descriptor; Malignant neoplasm of prostate; Measures; Methods; Molecular; Monitor; N-acetylaspartate; Newly Diagnosed; Noise; Normal tissue morphology; Operative Surgical Procedures; Patient Care; Patients; Phase; Physiologic pulse; Property; Pyruvate; RF coil; Recurrence; Recurrent tumor; Reproducibility; Research Methodology; Research Personnel; Resources; Safety; Sampling; Sensitivity and Specificity; Sequence Analysis; Signal Transduction; Techniques; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Therapeutic Effect; Time; Tissue Sample; Tissues; Translating; Work; anatomic imaging; base; cohort; data acquisition; design; experience; image guided; imaging agent; imaging approach; imaging biomarker; imaging modality; improved; in vivo; in vivo imaging; indexing; ineffective therapies; interest; magnetic resonance spectroscopic imaging; metabolic imaging; new technology; nonhuman primate; novel; novel therapeutics; preclinical study; response; spectroscopic imaging; tool; treatment effect; tumor

instrucfions): The goal of this project is to develop and test novel MR metabolic imaging approaches that will help to distinguish recurrent tumor from normal brain'and treatment effects in patients with glioblastoma (GBM). Characterizing temporal changes in tumor size and malignant potential is critically important for making fundamental decisions about patient care. The availability of imaging technologies that can provide more reliable metrics of tumor response to therapy and can identify progression at an early stage is critical for determining when to stop ineffective treatment and for selecting alternative strategies. We will integrate the multi-parametric imaging examination developed and validated in the current cycle of this P01 with novel H- 1 MR spectroscopic imaging techniques and new hyperpolarized C-13 metabolic imaging methods in order to develop improved mertics for evaluating the lesion. Linking in vivo imaging parameters with histological and genomic properties being studied in Project 2 and with information about therapeutic effects from Project 3 will provide important new strategies for selecting and evaluating novel therapeutics. In Aim 1 we will develop and test a novel 3D H-1 MRSI sequence that provides automated prescription and significantly improved brain coverage for short echo (TE=20-35ms) spectral arrays. This will facilitate the estimation of in vivo levels of myo-lnositol in the same acquisition as choline, creatine and N-acetylasptate. In Aim 2 we will utilize the unique experience and resources at UCSF to investigate the application of hyperpolarized C-13 MR metabolic imaging to patients with GBM. This exciting new technology has been shown in pre-clinical studies to differentiate tumor from normal tissue and provide a rapid assessment of response to therapy. In Aim 3 we will use the techniques developed in Aims 1 and 2 in conjunction with image guided tissue sampling in order to determine wheytherthe following metabolic imaging parameters can improve upon the characterization of recurrent tumor and treatment effect: (i) the choline to N-acetylaspartate index, (ii) the ratio of myo-lnositol/choline and (ill) the ratio of hyperpolarized C-13 lactate/pyruvate. RELEVANCE (See instructions): In this Project we will integrate novel metabolic and physiological imaging methods with tissue studies in order to determine which metabolic imaging parameters are the most effective in distinguishing recurrent tumor from treatment effects. This will have a major impact upon the clincial management of patients with GBM and upon the evaluation of novel therapeutics.

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