Artificial Intelligence-Based Quality Assurance for Online Adaptive Radiotherapy

基于人工智能的在线自适应放射治疗质量保证

• 批准号: 10589063
• 负责人: Steve Bin Jiang
• 金额: $ 53.91万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-09 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589063
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Anatomy; Artificial Intelligence; Body Weight decreased; Clinical; Clinical Research; Communication; Complex; Coupled; Development; Dose; Ensure; Evaluation; Expert Systems; Goals; Human; Institution; Intelligence; Intervention; Knowledge; Linear Accelerator Radiotherapy Systems; Logistics; Magnetic Resonance Imaging; Measurement; Modeling; Modernization; Normal tissue morphology; Organ; Patient imaging; Patients; Physicians; Procedures; Process; Radiation Dose Unit; Radiation therapy; Risk; Safety; Schedule; System; Systems Development; Systems Integration; Testing; Therapeutic; Time; Toxic effect; Training; Translations; Validation; Variant; Vendor; cancer care; cancer radiation therapy; data acquisition; imaging capabilities; learning algorithm; patient safety; patient variability; preference; preservation; pressure; quality assurance; radiation response; sample fixation; soft tissue; tool; transfer learning; treatment planning; tumor

PROJECT SUMMARY Recently, the development of MR-LINACs has made high-quality online adaptative radiotherapy a clinical reality to account for the daily anatomical variations to preserve the treatment quality. MR-LINACs, combining modern radiotherapy linear accelerators (LINACs) with on-board magnetic resonance imaging (MRI), offer excellent soft-tissue contrast to allow accurate organ and tumor segmentation to precisely capture the daily anatomical changes of each patient. Coupled with advanced adaptive treatment planning systems, MR-LINAC is the ideal platform for online adaptive radiotherapy and will bring cancer radiotherapy to a new level of precision and personalization. However, this new format of radiotherapy also comes with new challenges for patient safety and plan quality checks that cannot be satisfactorily addressed with traditional quality assurance (QA) tools: 1) With the patient lying on the treatment couch waiting for the treatment to start, there is mounting pressure on the team to move through the workflow as fast as possible, which may increase the likelihood of making mistakes and thus an effective QA procedure is even more important. 2) Each adapted plan warrants a new QA process, adding substantial burdens to an already extremely time-constrained process. A QA process with high efficiency is needed. 3) Conventional QA procedures are quite complex, involving inputs from many stakeholders, and thus are human-power demanding and error-prone. An automatic QA procedure requiring minimal human interventions and communications is highly desired. 4) In addition to checking the quality of the adapted segmentation and treatment plan, it is also crucial for a QA procedure to ensure their consistency with the physician’s intentions/preferences in the original plan. 5) A QA tool that is able to predict the plan deliverability prior to treatments, without actually irradiating the patients, is needed for online adaptive radiotherapy. The overarching goal of this project is to develop an Artificial Intelligence (AI)-based QA system to address these urgent unmet clinical needs for MR-LINAC online adaptive radiotherapy, with four main components to: 1) intelligently assess the quality of the adapted target and organ-at-risk segmentations and their consistency with those in the original plan; 2) intelligently assess the quality of the adapted plan and its consistency with the original plan; 3) efficiently perform 2nd dose check with an AI-based near real-time independent dose engine; and 4) predict the measurement-based QA results of plan deliverability using prior knowledge and new adapted plan information. We have two Specific Aims: 1) System development, including data acquisition for AI model training, and development of four AI models; and 2) System translation and validation at multiple institutions, including developing transfer learning algorithm and package for automated model commissioning; and translation, fine-tuning and evaluation of the developed AI systems. The successful conduct of the proposed project will result in the first intelligent, efficient, reliable, and independent QA system to facilitate unleashing the full potential of MR-LINAC online adaptive radiotherapy to advance cancer care.

项目摘要 最近，Linacs MR的发展使高质量的在线自适应放射疗法成为临床 考虑每日解剖变异以保持治疗质量的现实。 Mr-Linacs，合并 带有车载磁共振成像（MRI）的现代放射疗法线性加速器（LINACS），提供 出色的软组织对比，使精确的器官和肿瘤分割精确捕获每日 每个患者的解剖变化。加上高级自适应治疗计划系统，MR-LINAC 是在线自适应放疗的理想平台，将使癌症放射疗法达到新水平 精度和个性化。但是，这种放射疗法的新格式也面临着新的挑战 传统质量保证无法令人满意地解决的患者安全和计划质量检查 （QA）工具：1）患者躺在治疗沙发上等待治疗开始时，有安装 团队的压力尽可能快地通过工作流程，这可能会增加 犯错并因此进行有效的质量检查程序更为重要。 2）每个改编计划保证 新的质量检查过程，为已经非常耗时的过程增加了大量的伯伦斯。质量检查过程 需要高效率。 3）传统的质量检查程序非常复杂，涉及许多的投入 利益相关者，因此是人力的要求和错误。自动质量检查程序需要 高度期望人类的干预和交流。 4）除了检查质量 改编的细分和治疗计划，对于质量保证程序也至关重要 原始计划中的身体意图/偏好。 5）能够预测计划的质量检查工具 在线自适应需要治疗前的可递送性，而无需实际照射患者 放疗。该项目的总体目标是开发基于人工智能（AI）的质量检查系统 解决这些紧急未满足的未满足的临床需求，对林纳克在线自适应放射疗法，有四个主要 组件：1）智能评估适应性的目标和风险分段的质量以及 他们与原始计划中的计划的一致性； 2）智能评估改编计划及其质量 与原始计划的一致性； 3）通过基于AI的近实时进行有效进行第二剂检查 独立剂量引擎； 4）使用先验预测基于测量的计划可交付性结果 知识和新的改编计划信息。我们有两个具体的目标：1）系统开发，包括 用于AI模型培训的数据获取以及四个AI模型的开发； 2）系统翻译和 在多个机构进行验证，包括开发自动化的转移学习算法和软件包 模型调试；以及开发的AI系统的翻译，微调和评估。成功 拟议项目的进行将导致第一个智能，高效，可靠和独立的QA系统 为了促进MR-LINAC在线自适应放射疗法的全部潜力，以改善癌症护理。