Improved whole-brain spectroscopic MRI for radiation therapy planning

改进的全脑光谱 MRI 用于放射治疗计划

• 批准号: 10443355
• 负责人: Lee Cooper
• 金额: $ 66.12万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-05 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10443355
• 关键词: 3-Dimensional; Acceleration; Adoption; Adult; American College of Radiology; American College of Radiology Imaging Network; Area; Award; Back; Brain; Brain Neoplasms; Chemotherapy and/or radiation; Choline; Clinical; Clinical Treatment; Clinical Trials; Clinical Trials Cooperative Group; Collaborations; Computer software; Data; Data Display; Detection; Development; Diagnosis; Diagnostic; Disease Progression; Dose; Eastern Cooperative Oncology Group; Engineering; Environment; Excision; Funding; Genomics; Glioblastoma; Goals; Grant; Head; Histologic; Hour; Image; Imaging technology; Infiltration; Lead; Left; Life; Lipids; Magnetic Resonance Imaging; Maps; Measures; Metabolic; Metabolism; Methods; Morphologic artifacts; Motion; Multimodal Imaging; N-acetylaspartate; National Clinical Trials Network; Nature; Network-based; Newly Diagnosed; Online Systems; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Performance; Phase; Physicians; Pilot Projects; Predisposition; Primary Brain Neoplasms; Radiation Dose Unit; Radiation therapy; Radiology Specialty; Randomized; Reading; Recurrence; Reporting; Research; Resolution; Scanning; Sensitivity and Specificity; Site; Software Tools; Survival Analysis; System; Systems Integration; Techniques; Technology; Time; Tissues; Treatment outcome; Update; Vendor; Visualization; Work; base; brain tissue; clinical decision-making; clinical imaging; clinical implementation; cloud based; contrast enhanced; data analysis pipeline; data management; data visualization; follow-up; high risk; image reconstruction; imaging modality; imaging platform; improved; improved outcome; magnetic resonance spectroscopic imaging; metabolic imaging; multidimensional data; neoplastic cell; neovasculature; neural network; novel; predictive modeling; prototype; reconstruction; safety and feasibility; software development; software systems; spectroscopic data; spectroscopic imaging; standard of care; tool; treatment planning; treatment response; tumor; tumor progression; two-dimensional; user-friendly

Identifying the extent of brain tumor margins for radiation treatment planning remains a challenging task due to the infiltrative nature of these tumors and limitations in current standard imaging methods. Multiple studies including our own have demonstrated that an MR technique for detecting metabolites in tissue, MR spectroscopic imaging or spectroscopic MRI (sMRI), can detect areas of infiltrating tumor with a high degree of sensitivity and specificity, enabling better radiation treatment of areas that lead to early recurrence and extending life. sMRI enables the identification of tumor extent that is marked by increased Choline/N-Acetylaspartate ratios, including regions that are not detectable by diagnostic MRI and that are normally left untreated. By allowing these previously undetected regions to be treated, sMRI has the potential to improve the efficacy of radiation treatment and significantly delay recurrence. In our 3-site sMRI-guided radiation dose escalation pilot study which was completed in 2019, we were able to demonstrate feasibility and safety. Survival analysis of all 30 GBM patients shows a promising median overall survival (OS) of 23 months compared to 16 months OS for GBM patients receiving standard-of-care. Our trial has been approved as a National Clinical Trial Network (ECOG-ACRIN) trial (EAF211). This is a great opportunity to disseminate this technique with staff support from ACRIN and American College of Radiology (ACR). We will achieve the goal in the renewal funding period of our current project by leveraging diverse expertise at three research sites and collaboration with Siemens Healthineers to engineer and validate technological improvements needed to improve sMRI acquisition, analysis, and clinical integration. These improvements include: (1) updated rapid and motion-robust sMRI for improved image quality; (2) new accelerated data processing pipelines to return Cho/NAA ratio maps to PACS for clinically timely radiology reporting; (3) new processing, display, and analysis methods that will present metabolite maps in an efficient manner with a clinician-friendly interface that enables integration with radiation treatment planning software systems; and (4) development of new tools to predict the optimal baseline RT planning strategies using sMRI. The completion of this study will provide robust sMRI acquisition methods and software tools that are ready to be deployed in clinical use and which will help guide important treatment decisions.

确定脑肿瘤边缘的范围以制定放射治疗计划仍然是一项具有挑战性的任务