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

基本信息

批准号：
10445135
负责人：
Steve Bin Jiang
金额：
$ 63.2万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
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/10445135
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 base 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

项目摘要

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.

项目概要近年来，MR-LINAC的发展使高质量的在线自适应放疗成为临床结合现实情况来考虑日常解剖变化，以保持 MR-LINAC 的治疗质量。带有机载磁共振成像 (MRI) 的现代放射治疗直线加速器 (LINAC)，提供出色的软组织对比度，可实现准确的器官和肿瘤分割，从而精确捕捉日常情况结合先进的自适应治疗计划系统，MR-LINAC。是在线自适应放疗的理想平台，将把癌症放疗提升到一个新的水平然而，这种新的放射治疗方式也带来了新的挑战。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 (QA) 工具： 1) 当患者躺在治疗床上等待治疗开始时，安装团队面临尽快完成工作流程的压力，这可能会增加出现问题的可能性犯错误，因此有效的质量保证程序更加重要 2) 每个改编计划都需要一个。新的 QA 流程，给本已极其有限的时间的 QA 流程增加了巨大的负担。 3）传统的质量保证程序相当复杂，涉及许多人的输入。利益相关者，因此对人力要求高且容易出错。极需要最少的人为干预和通信 4) 除了检查质量之外。适应分割和治疗计划，对于 QA 程序来说，确保其与医生在原始计划中的意图/偏好 5) 能够预测计划的 QA 工具。在线自适应需要在治疗前进行递送，而无需实际照射患者该项目的总体目标是开发基于人工智能 (AI) 的 QA 系统。为了解决 MR-LINAC 在线自适应放疗的迫切未满足的临床需求，有四个主要组件：1）智能评估适应目标和风险器官分割的质量，与原始计划的一致性；2）智能评估调整后的计划及其质量与原始计划的一致性；3）通过基于人工智能的近实时有效地进行第二次剂量检查独立剂量引擎；4) 使用先验预测基于测量的计划交付能力的 QA 结果我们有两个具体目标：1）系统开发，包括。人工智能模型训练的数据采集，以及四个人工智能模型的开发；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包模型调试；以及已开发的人工智能系统的翻译、微调和评估。拟议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充分发挥 MR-LINAC 在线自适应放射治疗的潜力，以推进癌症治疗。