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.

项目总结/摘要：向癌症输送剂量，同时保留正常组织是最终目标 放疗（RT）治疗，尤其是在头部和颈部。通过识别更有可能 在治疗的早期反应，以及耐药肿瘤的亚体积，RT计划可以每天改变， 利用肿瘤中的生物学改变，以同等概率降低副作用 治愈功能性成像技术已被证明在临床系列中区分早期 头颈癌（HNC）放射治疗的反应者，以及识别放射抵抗 疾病治疗后。这些功能成像技术可用于主动适应放射治疗 在放射治疗过程中出现高频率。 与我们的工业合作伙伴（Elekta Medical Systems）一起，我们的集团最近被授予 NIH R 01资助（5 R 01 DE 028290 -03，FREEDOMM-RT），用于开发硬件、软件、技术和 HNC患者功能性影像引导RT的质量保证基础设施。由此产生的高频 除了额外的MRI数据源外，还从该项目中获得了解剖学和功能成像数据 从我们的机构形成了一个语料库的前所未有的新颖的“大数据”的MRI引导自适应RT。因此， 在这份补编中，我们建议对这些数据进行选择性的整理、注释和传播，以促进 社区驱动的人工智能（AI）模型构建工作，以便更容易地将MR引导的 RT技术进入临床。 拟议的一年期补充包括数据管理和数据质询执行工作。 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， 生成相应的分割感兴趣区域;剂量测定、人口统计学和临床数据将 为每一位患者提供服务。这些基准数据集将被匿名化并传输给The Cancer 图像存档供公众重复使用，从而促进研究社区开发强大的以RT为中心的AI 项目此外，为了促进社区参与我们的新基准数据集，我们将发起一个 一系列AI“数据挑战”。通过这些挑战，我们将直接促进新的人工智能创新，以解决 临床相关的RT问题。 该项目的成功完成将使现代化和综合生物医学数据 为公众使用RT数据构建AI模型提供生态系统。此外，拟议的基准数据集将 为HNC患者使用AI实现个性化医疗的长期目标提供基础， 减少口腔牙齿后遗症，同时保持良好的治愈率，直接补充了目标， 家长补助最后，该补充剂将通过使患者能够表征 用于改善治疗干预和AI技术的下游转化应用。