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.

项目总结 近年来，MR-LINACs的发展使高质量的在线适应性放射治疗成为临床 现实中要考虑到日常的解剖变化，以保持治疗质量。MR-LINAC，组合 配备机载磁共振成像(MRI)的现代放射治疗直线加速器(LINAC)提供 卓越的软组织对比度，使精确的器官和肿瘤分割能够准确地捕捉日常 每例患者的解剖变化。再加上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 精准性和个性化。然而，这种新的放射治疗形式也带来了新的挑战 传统质量保证无法令人满意的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，有越来越多的人 团队面临尽快完成工作流程的压力，这可能会增加 犯错误，从而建立有效的质量保证程序就更重要了。2)每个修改后的计划都保证 新的QA流程，给本已极其紧张的流程增加了实质性的负担。质量保证流程 需要高效率的。3)传统的质量保证程序相当复杂，涉及许多人的意见 因此，对人力资源要求很高，而且容易出错。自动质量保证程序需要 最大限度地减少人为干预和交流是非常必要的。4)除了检查产品的质量外 经过调整的分割和治疗计划，对于质量保证程序确保它们与 医生在原始计划中的意图/偏好。5)能够预测计划的QA工具 在线适应需要在治疗前交付，而不需要对患者进行实际照射 放射治疗。该项目的总体目标是开发一个基于人工智能(AI)的问答系统 为了解决这些紧急的未得到满足的临床需求，MR-LINAC在线自适应放射治疗，主要有四个方面 用于：1)智能地评估适应的目标和危险器官分割的质量以及 它们与原始计划的一致性；2)智能地评估调整后的计划及其 与原始计划保持一致；3)通过基于AI的近实时高效执行二次剂量检查 独立剂量引擎；以及4)使用先验预测基于测量的计划交付能力的质量保证结果 知识和新改编的计划信息。我们有两个具体目标：1)系统开发，包括 用于人工智能模型培训的数据获取，以及开发四个人工智能模型；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、微调和评价。成功者 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 为了促进释放MR-LINAC在线自适应放射治疗的全部潜力，以推进癌症治疗。