Improving Image-Guided Radiation Therapy of Gliomas with High-Resolution MR Spectroscopic Imaging

利用高分辨率磁共振波谱成像改善神经胶质瘤的图像引导放射治疗

• 批准号: 10501516
• 负责人: Chao Ma
• 金额: $ 51.42万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10501516
• 关键词: Address; Adjuvant Chemotherapy; Adjuvant Radiotherapy; Adoption; Area; Biological; Biological Markers; Biopsy; Blood - brain barrier anatomy; Brain; Clinical; Data; Development; Dose; Evaluation; Excision; Extravasation; Glioblastoma; Glioma; Goals; Guidelines; Head; Heterogeneity; Histology; Hypoxia; Image; Image Enhancement; Imaging technology; Infiltration; Inflammation; Investigation; Knowledge; Label; Location; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Malignant - descriptor; Malignant Glioma; Malignant neoplasm of brain; Maps; Measurement; Metabolic; Methods; Microscopic; Motion; Necrosis; Newly Diagnosed; Noise; Operative Surgical Procedures; Outcome; Patients; Performance; Phase I Clinical Trials; Phase III Clinical Trials; Positron-Emission Tomography; Postoperative Period; Predisposition; Protons; Radiation therapy; Recurrence; Reproducibility; Research; Resolution; Sampling; Scanning; Sensitivity and Specificity; Signal Transduction; Specificity; Specimen; Speed; Structure; Techniques; Time; Tissues; Tumor Volume; Uncertainty; United States; Validation; Water; base; brain tissue; brain tumor imaging; chemotherapy; clinical application; computerized data processing; contrast enhanced; data acquisition; density; experimental study; follow-up; image guided radiation therapy; imaging modality; improved; in vivo; innovation; ischemic injury; metabolic imaging; molecular imaging; neoplastic; neoplastic cell; novel; quantum; research clinical testing; simulation; spectroscopic imaging; standard care; tool; treatment planning; tumor

ABSTRACT Glioma make up 80% of all primary malignant brain tumors. The current standard treatment for newly diagnosed gliomas includes maximal surgical resection, radiation therapy (RT), and chemotherapy. A key technical challenge in RT treatment planning is accurate target volume delineation of gliomas. The current clinical guidelines for target volume delineation rely primarily on structural Magnetic Resonance Imaging (MRI) images. Gross Tumor Volume (GTV) is defined based on contrast-enhanced T1-weighted MRI and T2-weighted MRI. However, structural MRI alone lacks specificity for delineation of true tumor boundaries. Accordingly, Clinical Target Volume (CTV) is often defined as the GTV plus a large margin (e.g., 20-25 mm) to account for possible microscopic infiltration. The lack of specificity of structural MRI is a critical factor limiting the investigation and clinical application of new RT techniques for better clinical outcome. MR spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for label-free molecular imaging of brain tumor. In a recent Phase I clinical trial, MRSI is used to guide dose escalation in RT for Glioblastoma multiforme patients, showing very promising preliminary results. Although general clinical applications of MRSI have been impeded by its limited spatial resolution and long scan time, significant progresses have been made in addressing these technical challenges over the past decade using advanced data acquisition and processing methods. Our group have successfully developed a powerful MRSI technology, known as SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). SPICE effectively integrates rapid scanning, sparse sampling, quantum simulation of molecule resonance structures, and machine learning to enable rapid high-resolution MRSI. Preliminary results by our and other groups have shown an exciting potential of SPICE to achieve an unprecedented combination of resolution, speed, and SNR for metabolic imaging. We have also demonstrated, for the first time, the feasibility of mapping T1, T2 and proton-density parameters of brain tissues using the unsuppressed water signals from the SPICE scans. The primary goal of this project is to leverage this significant advance in MRSI technology and investigate the use of high-resolution metabolic and structural information to achieve more accurate target volume delineation for RT treatment planning of gliomas. We will: 1) further develop and optimize SPICE for MRI/MRSI-guided RT of gliomas in clinical settings, 2) perform systematic performance evaluation of the proposed method on phantoms, healthy subjects, and glioma patients, and 3) investigate the use of metabolic and structural biomarkers for delineation of biological target volume to improve image-guided RT of gliomas. The proposed research is innovative in developing a novel molecular imaging technology and a timely effort on improving RT treatment planning of gliomas with quantitative metabolic and structural biomarkers. Successful completion of the project will have a significant impact on image-guided RT for gliomas, opening up new opportunities for better control of recurrence in glioma patients using dose escalated RT.

摘要 胶质瘤占所有原发性恶性脑肿瘤的80%。目前的标准治疗新诊断的 神经胶质瘤的治疗包括最大手术切除、放射治疗（RT）和化学治疗。一个关键的技术 RT治疗计划中的挑战是准确描绘胶质瘤的靶体积。当前临床 目标体积描绘的指导原则主要依赖于结构磁共振成像（MRI）图像。 根据对比增强T1加权MRI和T2加权MRI定义大体肿瘤体积（GTV）。 然而，单独的结构MRI缺乏描绘真实肿瘤边界的特异性。因此，临床 目标体积（CTV）通常被定义为GTV加上大余量（例如，20-25 mm），以考虑可能的 微观渗透结构MRI缺乏特异性是限制研究的关键因素， 新的RT技术的临床应用，以获得更好的临床结果。MR光谱成像（MRSI）长期以来一直是 被认为是一个潜在的强大的工具，脑肿瘤的无标记分子成像。在最近的一个阶段 在一项临床试验中，MRSI用于指导多形性胶质母细胞瘤患者的RT剂量递增， 有希望的初步结果。尽管MRSI的一般临床应用受到其有限的限制， 空间分辨率和长扫描时间，在解决这些技术方面已经取得了重大进展 在过去十年中，使用先进的数据采集和处理方法，我们组有 成功开发了强大的MRSI技术，称为SPICE（Spectroscopic Imaging by Exploiting） 空间谱相关）。SPICE有效地集成了快速扫描、稀疏采样、量子模拟 分子共振结构和机器学习，以实现快速高分辨率MRSI。初步 我们和其他小组的研究结果表明，SPICE具有令人兴奋的潜力，可以实现前所未有的 代谢成像的分辨率、速度和SNR的组合。我们还首次证明， 利用非抑制水映射脑组织T1、T2和质子密度参数的可行性 SPICE扫描的信号该项目的主要目标是利用MRSI的这一重大进展 技术，并研究使用高分辨率的代谢和结构信息，以实现更多 准确的靶体积勾画，用于胶质瘤的RT治疗计划。我们将：1）进一步发展和优化 SPICE在临床环境中用于MRI/MRI引导的胶质瘤RT，2）进行系统性能评价， 所提出的方法对幻影，健康受试者，和神经胶质瘤患者，和3）调查使用代谢 以及用于描绘生物靶体积以改善胶质瘤的图像引导RT的结构生物标志物。的 拟议的研究在开发新的分子成像技术方面具有创新性， 用定量代谢和结构生物标志物改善胶质瘤的RT治疗计划。成功 该项目的完成将对胶质瘤的图像引导RT产生重大影响，开辟新的 使用剂量递增RT更好地控制胶质瘤患者复发的机会。