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，MR-LINAC的发展使高质量的在线自适应放射治疗成为临床治疗的一个重要方面。 考虑到日常解剖变化以保持治疗质量。MR-LINAC，组合 具有机载磁共振成像（MRI）的现代放射治疗直线加速器（LINAC）， 出色的软组织对比度，允许准确的器官和肿瘤分割，以精确捕获日常 每一位患者的解剖变化。结合先进的自适应治疗计划系统，MR-LINAC 是在线自适应放射治疗的理想平台，将把癌症放射治疗带到一个新的水平， 精确度和个性化。然而，这种新形式的放射治疗也带来了新的挑战， 传统质量保证无法令人满意地解决患者安全和计划质量检查问题 (QA)工具：1）患者躺在治疗床上等待治疗开始， 对团队施加压力，使其尽快完成工作流程，这可能会增加 因此，有效的质量保证程序就更加重要。2)每一个经过调整的计划都需要一个 新的质量保证程序，给本已时间极为紧张的程序增加了大量负担。QA过程 需要高效率。3)传统的质量保证程序相当复杂，涉及许多方面的投入， 利益相关者，因此是人力需求和容易出错。自动QA程序，要求 非常需要最少的人工干预和通信。4)除了检查质量外， 适应分割和治疗计划，这也是至关重要的QA程序，以确保其一致性， 医生在原始计划中的意图/偏好。5)能够预测计划的QA工具 在线自适应治疗需要在治疗之前的可输送性，而无需实际照射患者， 放疗该项目的总体目标是开发一个基于人工智能（AI）的QA系统 为了解决MR-LINAC在线自适应放射治疗的这些迫切的未满足的临床需求， 1）智能地评估适应的目标和危险器官分割的质量，以及 它们与原计划中的一致性; 2）智能地评估调整后的计划的质量及其 与原始计划的一致性; 3）使用基于AI的近实时有效执行第二次剂量检查 独立的剂量引擎;以及4）使用先验知识预测计划可交付性的基于测量的QA结果 知识和新的适应性规划信息。我们有两个具体目标：1）系统开发，包括 用于AI模型训练的数据采集，以及四个AI模型的开发;以及2）系统翻译和 在多个机构进行验证，包括开发迁移学习算法和自动化软件包， 模型调试;以及对开发的人工智能系统进行翻译、微调和评估。成功 该项目的实施将产生第一个智能、高效、可靠和独立的质量保证系统 促进释放MR-LINAC在线自适应放射治疗的全部潜力，以推进癌症治疗。