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: 10583517
• 负责人: Eric Albert Mellon
• 金额: $ 54.61万
• 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/10583517
• 关键词: Address; Agreement; Anatomy; Biopsy; Brain; Brain Neoplasms; Cellularity; Classification; Clinical; Collaborations; Computer software; Data; Detection; Development; Devices; Diagnostic; Diffusion; Disabled Persons; Dose; Early identification; Excision; Future; Gadolinium; Generations; Glioblastoma; Goals; Growth; Hypoxia; Image; Imaging Techniques; Inflammation; Institution; Institutional Review Boards; Intervention; Kinetics; Lesion; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Manuals; Maps; Measurement; Metabolism; Mission; Modeling; Motion; National Cancer Institute; Newly Diagnosed; Outcome; Patient-Focused Outcomes; Patients; Perfusion; Periodicals; 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; 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.

项目摘要/摘要 胶质母细胞瘤是最常见的起源于大脑的癌症，新诊断约12,000例 每年在美国，中位生存期约为18个月。患者面临的一个常见困境 而治疗小组则是放射治疗(RT)后一个月的临床MRI显示 高达50%的患者出现意义不明的生长。真正进展(TP)的患者 无反应的肿瘤继续在系列磁共振成像上进展，通常在9个月内死亡。总磷 通常在RT完成后6个月或更长时间确定，通常在为时已晚时 进行干预。国家癌症研究所的使命是提高患有癌症的患者的存活率 癌症。我们的目标，也是一个未得到满足的需求，是在早期发现患有TP的胶质母细胞瘤患者。 治疗和实施积极的二线治疗以提高存活率。 这项建议描述了一种创新的方法来识别早期胶质母细胞瘤TP通过改进 一种新的MRI与RT(MRI-RT)结合的MRIdian的神经成像和图像处理 来自ViewRay，Inc.的设备，患者每天在那里接受核磁共振检查，作为其RT的一部分。我们的预赛 MRIdian的数据第一次在患者的MRI上显示了胶质母细胞瘤的日常生长 在RT期间。通过在MRIdian(目标1)上开发生理学MRI技术，我们试图识别 在RT过程中有生长时进行TP(目标2)，并加强对TP的RT(目标3/将来)。 临床上最敏感、最特异的诊断TP的MRI技术 放疗后与肿瘤生理相关：扩散(细胞性)、灌注性(缺氧)和 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-产业合作伙伴关系可以为 在MRIdian上RT期间的每日测量。目标2的假设是来自 胶质母细胞瘤患者在MRI-RT期间的日常mpMRI可以通过机器学习进行处理 和放射组学技术，自动检测胶质母细胞瘤的生长并预测长期 结果。然后，目标3结合目标1-2来测试预期的工作流，以将RT强化到TP 当基于mpMRI的放射治疗中首次发现TP时，阐明了肿瘤生理学的趋势，因此 称为“生理适应性RT”(部分)。 零件工作流程将由迈阿密和ViewRay开发，并集成到MRIdian中。这个 使用单一平台MRIdian的优势在于，部件工作流将通过 向60多个核磁共振成像中心发送ViewRay。这一简单的临床翻译将允许我们继续进行 早期RT剂量递增的多机构试验以改善不良患者的存活率 有反应性的胶质母细胞瘤。