A Physiologic Adaptive Radiation Therapy Pipeline for Glioblastoma by Daily Multiparametric MRI and Machine Learning

通过日常多参数 MRI 和机器学习治疗胶质母细胞瘤的生理适应性放射治疗流程

• 批准号: 10446236
• 负责人: Eric Albert Mellon
• 金额: $ 60.49万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-03 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10446236
• 关键词: Address; Agreement; Anatomy; Biopsy; Brain; Brain Neoplasms; Cellularity; Clinical; Collaborations; Computer software; Data; Detection; Development; Devices; Diagnosis; Diagnostic; Diffusion; Disabled Persons; Dose; Excision; Future; Gadolinium; Generations; Glioblastoma; Goals; Growth; Hypoxia; Image; Imaging Techniques; Inflammation; Institutional Review Boards; Intervention; Kinetics; Lesion; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Manuals; Measurement; Metabolism; Mission; Modeling; Motion; National Cancer Institute; Outcome; Patient-Focused Outcomes; Patients; Perfusion; Periodicity; Phase; Physiological; Physiology; Pilot Projects; Preparation; Process; Prognosis; Protocols documentation; Radiation therapy; Research; Scanning; Spectrum Analysis; System; Techniques; Testing; Translating; Tumor Volume; Validation; Work; automated segmentation; base; chemotherapy; clinical translation; deep learning; design; high risk; image processing; improved; industry partner; innovation; member; neovascularization; neuroimaging; perfusion imaging; prospective; quantitative imaging; radiomics; response; temozolomide; treatment response; trend; tumor; tumor growth; validation studies

Project Summary/Abstract Glioblastoma is the most common cancer originating in the brain with ~12,000 new diagnoses per year in the U.S.A. and median survival about 18 months. A common dilemma for the patient and treatment team is that the clinical MRI one month after radiation therapy (RT) shows growth of unclear significance in up to 50% of patients. Patients with true progression (TP) of non-responding tumor continue to progress on serial MRIs and usually die within 9 months. TP is usually determined 6 or more months after completion of RT, often when it is too late to intervene. The mission of the National Cancer Institute is to improve survival of patients with cancer. Our goal, and an unmet need, is to identify glioblastoma patients with TP early during treatment and implement aggressive second-line therapy to improve survival. This proposal describes an innovative approach to identify early glioblastoma TP by improving neuroimaging and image processing on MRIdian, a new combination MRI and RT (MRI-RT) device from ViewRay, Inc where patients undergo MRI daily as part of their RT. Our preliminary data with MRIdian is the first to demonstrate daily glioblastoma growth on MRI in patients during RT. By developing physiologic MRI techniques on MRIdian (Aim 1), we seek to identify TP when there is growth during RT (Aim 2), and intensify RT to that TP (Aim 3/future). The most sensitive and specific commonly applied clinical MRI techniques for identifying TP after RT correlate with tumor physiology: diffusion (cellularity), perfusion (hypoxia), and spectroscopy (metabolism). These are collectively termed mpMRI. The hypothesis of aim 1 is that the academic-industrial partnership between Miami and ViewRay can develop mpMRI for daily measurements during RT on MRIdian. The hypothesis of aim 2 is that the images from daily mpMRI during MRI-RT in glioblastoma patients can be processed by machine learning and radiomics techniques to automatically detect glioblastoma growth and predict long-term outcome. Aim 3 then combines aims 1-2 to test a prospective workflow to intensify RT to TP when TP is first identified during RT based on mpMRI elucidated trends in tumor physiology, so called “physiologic adaptive RT” (PART). The PART workflow will be developed by Miami and Viewray and integrated into MRIdian. The advantage of using single platform MRIdian is that the PART workflow will be distributed by Viewray to the over 60 MRIdian centers. This easy clinical translation will permit us to proceed with multi-institutional trials of early RT dose escalation to improve survival of poorly responding glioblastomas.

项目总结/摘要 胶质母细胞瘤是最常见的起源于大脑的癌症，约有12，000例新诊断 在美国，平均每年死亡18个月。患者常见的困境 放射治疗（RT）后一个月的临床MRI显示， 在高达50%的患者中生长不明显。真实进展（TP）患者 无反应的肿瘤在连续MRI上继续发展，通常在9个月内死亡。TP 通常在完成RT后6个月或更长时间确定，通常为时已晚， 介入美国国家癌症研究所的使命是提高患有癌症的患者的生存率。 癌我们的目标和未满足的需求是在治疗早期识别出胶质母细胞瘤患者的TP。 治疗和实施积极的二线治疗，以提高生存率。 该提案描述了一种创新的方法来识别早期胶质母细胞瘤TP， MRIdian上的神经成像和图像处理，一种新的MRI和RT组合（MRI-RT） ViewRay，Inc的设备，患者每天接受MRI作为其RT的一部分。 MRIDian的数据首次证明了患者在MRI上的胶质母细胞瘤每日生长 通过在MRI上开发生理MRI技术（目的1），我们试图识别 当RT（目标2）期间有增长时，TP，并加强RT到TP（目标3/未来）。 最敏感和特异的临床常用MRI技术用于识别TP RT后与肿瘤生理学相关：扩散（细胞结构），灌注（缺氧）， 光谱学（代谢）。这些统称为mpMRI。目标1的假设是 迈阿密和ViewRay之间的学术-工业合作伙伴关系可以开发mpMRI， 在RT期间对MRI进行每日测量。目标2的假设是， 胶质母细胞瘤患者MRI-RT期间的每日mpMRI可以通过机器学习进行处理 和放射组学技术，以自动检测胶质母细胞瘤的生长， 结果。目标3然后结合目标1-2来测试一个预期的工作流程，以加强RT到TP 当基于mpMRI在RT期间首次识别TP时，阐明了肿瘤生理学的趋势， 称为“生理适应性RT”（PART）。 PART工作流程将由迈阿密和Viewray开发，并集成到MRIDian中。的 使用单一平台MRIDian的优点是，PART工作流将通过以下方式分发： Viewray到60多个MRIDian中心。这个简单的临床翻译将允许我们继续 早期RT剂量递增的多机构试验， 胶质母细胞瘤