Administrative Supplement: Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac device

行政补充：使用新型 MR-Linac 设备开发功能性磁共振成像引导的适应性放射治疗头颈癌患者

• 批准号: 10593525
• 负责人: John Paul Christodouleas
• 金额: $ 32.27万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-13 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10593525
• 关键词: Administrative Supplement; Anatomy; Artificial Intelligence; Award; Benchmarking; Big Data; Biological; Cervical lymph node group; Clinic; Clinical; Clinical Data; Communities; Complement; Computer Assisted; Computer software; Data; Data Set; Data Sources; Dental; Deposition; Development; Devices; Disease Resistance; Dose; FAIR principles; Fostering; Foundations; Frequencies; Functional Imaging; Functional Magnetic Resonance Imaging; Generations; Goals; Grant; Head and Neck Cancer; Head and neck structure; Image; Imaging Techniques; Industrialization; Infrastructure; Institution; Intervention; Magnetic Resonance Imaging; Malignant Neoplasms; Medical; Medical Imaging; Modeling; Modernization; Normal tissue morphology; Outcome; Patients; Physicians; Primary Neoplasm; Probability; Process; Radiation; Radiation therapy; Research; Resistance; Series; Societies; Source; Structure; System; Technology; The Cancer Imaging Archive; Therapeutic Intervention; Tissues; Translating; United States National Institutes of Health; University of Texas M D Anderson Cancer Center; anatomic imaging; clinical decision support; clinically relevant; community engagement; data curation; data dissemination; data ecosystem; data management; data repository; data submission; head and neck cancer patient; image guided; image guided radiation therapy; improved; innovation; interest; lymph nodes; model building; model development; novel; parent grant; personalized medicine; predicting response; quality assurance; radioresistant; side effect; translational applications; tumor

PROJECT SUMMARY/ABSTRACT: Delivering dose to cancers while sparing normal tissue is the ultimate goal in radiotherapy (RT) treatment, especially in the head and neck. By identifying tumors which are more likely to respond early in treatment, as well as subvolumes of resistant tumor, RT plans could be changed each day to take advantage of biological alteration in the tumor, resulting in reduced side effects with equivalent probability of cure. Functional imaging techniques have demonstrated utility in clinical series in discriminating early responders to radiation therapy in head and neck cancer (HNC), as well as identifying radiation resistant disease post-therapy. These functional imaging techniques could be utilized to actively adapt radiation therapy with high frequency during the radiation treatment course. In tandem with our industrial partner (Elekta Medical Systems), our group has recently been awarded an NIH R01 grant (5R01DE028290-03, FREEDOMM-RT) to develop the hardware, software, technical, and quality assurance infrastructure for functional image-guided RT for HNC patients. The resulting high-frequency anatomical and functional imaging data derived from this project, in addition to additional MRI data sources from our institution forms a corpus of unprecedented novel “big data” for MRI-guided adaptive RT. Therefore, in this supplement, we propose the selective curation, annotation, and dissemination of these data to facilitate community-driven artificial intelligence (AI) model building efforts in order to more readily translate MR-guided RT technologies into the clinic. The proposed one-year supplement is composed of data curation and data challenge execution efforts. Specifically, we will curate high-quality anatomical and functional MRI sequences at multiple timepoints and generate corresponding segmentations regions of interest; dosimetric, demographic, and clinical data will be curated for each patient. These benchmark datasets will be anonymized and transmitted to The Cancer Imaging Archive for public re-use, thereby fostering the research community to develop robust RT-centric AI projects. Additionally, to facilitate community engagement with our novel benchmark datasets, we will initiate a series of AI “data challenges”. Through these challenges we will directly foster novel AI innovation to solve clinically relevant RT problems. Successful completion of this project will enable a modernized and integrated biomedical data ecosystem for public use of RT data for AI model building. Moreover, the proposed benchmark datasets will provide a foundation to achieve the long-term goal of personalized medicine for HNC patients using AI to reduce oro-dental sequelae while maintaining excellent cure rates, directly complementing the goals of the parent grant. Finally, this supplement will positively impact patients by enabling the characterization of malignancy for improved therapeutic intervention and downstream translational application of AI technologies.

项目摘要/摘要：在不影响正常组织的情况下，向肿瘤输送剂量是最终目标