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：最终目标是在不伤害正常组织的情况下向癌症提供剂量 在放射治疗 (RT) 治疗中，特别是在头部和颈部，通过识别更有可能发生的肿瘤。 在治疗早期做出反应，以及耐药肿瘤的亚体积，可以每天更改 RT 计划，以 利用肿瘤的生物学改变，以同等概率减少副作用 功能成像技术已在临床系列中证明了其在早期诊断中的实用性。 头颈癌 (HNC) 放射治疗的反应者，以及识别放射抗性 这些功能成像技术可用于主动适应放射治疗。 在放射治疗过程中使用高频率。 我们的团队最近与我们的工业合作伙伴（医科达医疗系统）一起荣获 NIH R01 拨款（5R01DE028290-03，FREEDOMM-RT）用于开发硬件、软件、技术和 HNC 患者功能性图像引导放疗的质量保证基础设施。 除了其他 MRI 数据源之外，还来自该项目的解剖和功能成像数据 我们机构的数据为 MRI 引导的自适应 RT 形成了前所未有的新颖“大数据”语料库。 在本补充中，我们建议对这些数据进行选择性管理、注释和传播，以促进 社区驱动的人工智能 (AI) 模型构建工作，以便更轻松地转化 MR 引导的内容 RT 技术进入临床。 拟议的一年补充计划由数据管理和数据挑战执行工作组成。 具体来说，我们将在多个时间点策划高质量的解剖和功能 MRI 序列， 生成相应的感兴趣区域的剂量学、人口统计和临床数据； 这些基准数据集将被匿名化并传输给癌症中心。 供公众重复使用的成像档案，从而促进研究社区开发强大的以 RT 为中心的人工智能 此外，为了促进社区参与我们新颖的基准数据集，我们将启动一个项目。 通过这些挑战，我们将直接培育新颖的人工智能创新来解决。 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 此外，拟议的基准数据集将建立公共使用 RT 数据进行 AI 模型构建的生态系统。 为利用人工智能实现 HNC 患者个性化医疗的长期目标奠定基础 减少口腔牙齿后遗症，同时保持良好的治愈率，直接补充了该计划的目标 最后，该补充品将通过确定患者的特征来对患者产生积极影响。 恶性肿瘤，以改善人工智能技术的治疗干预和下游转化应用。