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：在放映正常组织的同时向癌症输送剂量是最终目标 在放疗（RT）治疗中，尤其是在头部和颈部。通过识别更有可能的肿瘤 在治疗的早期以及耐药性肿瘤的亚参数中，RT计划每天都可以将其更改为 利用肿瘤的生物学改变，导致副作用降低，同等概率 治愈。功能成像技术已在临床系列中表现出效用 响应者对头颈癌（HNC）中的放射治疗，并鉴定抗辐射 治疗后疾病。这些功能成像技术可用于主动调整辐射疗法 在辐射治疗过程中频率高。 与我们的工业合作伙伴（Elekta Medical Systems）一致，我们的小组最近被授予 NIH R01赠款（5R01DE028290-03，Freedomm-RT）开发硬件，软件，技术和 HNC患者功能图像引导的RT的质量保证基础设施。由此产生的高频 除了其他MRI数据源外，还从该项目得出的解剖学和功能成像数据 从我们的机构中​​，MRI引导的自适应RT构成了前所未有的新型“大数据”语料库。所以， 在此补充中，我们提出了这些数据的选择性策划，注释和传播，以促进 社区驱动的人工智能（AI）建立模型的努力，以便更容易翻译MR引导 RT技术进入诊所。 拟议的一年补品包括数据策展和数据挑战执行工作。 具体而言，我们将在多个时间点和 生成相应的利益细分区域；剂量学，人口统计和临床数据将是 为每个患者策划。这些基准数据集将被匿名化并传输到癌症 成像档案以进行公共重复使用，从而培养研究社区以开发以RT为中心的AI 项目。此外，为了促进社区参与我们的新型基准数据集，我们将启动一个 一系列AI“数据挑战”。通过这些挑战，我们将直接促进小说AI创新来解决 临床相关的RT问题。 该项目的成功完成将实现现代化和集成的生物医学数据 用于公共用于AI模型构建的RT数据的生态系统。此外，拟议的基准数据集将 为使用AI的HNC患者实现个性化医学的长期目标提供基础 在保持出色的治愈率的同时，减少了牙科的后遗症，直接完成了目标的目标 父母赠款。最后，这种补充剂将通过表征的表征对患者产生积极影响 改善AI技术的治疗干预和下游翻译应用的恶性肿瘤。