Human-like automated radiotherapy treatment planning via imitation learning

通过模仿学习制定类似人类的自动放射治疗计划

• 批准号: 10406863
• 负责人: Xun Jia
• 金额: $ 59.28万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-18 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406863
• 关键词: Anatomy; Area; Artificial Intelligence; Attention; Back; Beds; Behavior; Cancer Patient; Caring; Clinical; Clinical Research; Communities; Complex; Complication; Data; Decision Making; Development; Dose; Due Process; Ensure; Environment; Evaluation; Evolution; Feedback; Goals; Growth; Head and Neck Cancer; Human; Intention; Joints; Learning; Malignant neoplasm of prostate; Manuals; Mathematics; Measures; Medical; Medical center; Mind; Modality; Modeling; Modernization; Normal tissue morphology; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Performance; Physicians; Play; Probability; Process; Radiation therapy; Research; Resource-limited setting; Resources; Role; Scheme; Site; Structure; System; Testing; Time; Training; Translating; Translations; Treatment Step; Treatment outcome; Validation; algorithm development; base; cancer therapy; chemotherapy; clinical translation; design; experience; head and neck cancer patient; improved; individual patient; industry partner; innovation; learning algorithm; learning progression; model development; negative affect; novel; optimal treatments; population based; product development; prototype; response; routine practice; success; treatment planning; treatment strategy; tumor; validation studies

PROJECT SUMMARY Radiation therapy is one of the major approaches for cancer treatment. Treatment planning, the process of designing the optimal treatment plan for each patient, is one of the most critical steps. If a treatment is poorly designed, a satisfactory outcome cannot be achieved, regardless of the quality of other treatment steps. Treatment planning in modern radiotherapy is formulated as a mathematical optimization problem defined by a set of hyperparameters. While there exists several quantifiable metrics to quantify plan quality and guide the planning process, these are simplified representations that cannot fully describe the physician’s intent. In addition, these metrics only measure plan quality from a population-based perspective, and cannot guide treatment planning to achieve the patient-specific best treatment plans. Hence, the best physician-preferred solution often sits in a gray area, only achievable by an extensive trial-and-error hyperparameter tuning process and interactions between the planner and physician. Consequently, planning time can take up to a week for complex cases and plan quality may be poor, if the planner is inexperienced and/or under heavy time constraints. These consequences substantially deteriorate treatment outcomes, as having been clearly demonstrated in clinical studies. Recently, the advancement in artificial intelligence (AI), particularly in imitation learning allows human- like decision making by observing a human expert’s actions and internally building its own decision-making system. In response to PAR-18-530, the goal of this project is to develop and translate an AI planner that mimics human experts’ behavior to generate a high quality plan. The AI planner will not replace human planners. Instead, the AI plan will be used as a starting point in the current planning process to improve plan quality and planning efficiency. The human planner’s actions on further plan improvement can feed back to the AI planner through continuous learning for its continuous evolution. We will pursue this goal using prostate cancer as the test bed through an academic-industrial partnership, jointing strong research and clinical expertise at UT Southwestern Medical Center with extensive commercial product development experience at Varian Medical Systems Inc. The following specific aims are defined. Aim 1: Model and algorithm development. We will collect experts’ behavior data in routine treatment planning and train the AI planner. Aim 2: System validation and translation. We will integrate the AI planner into Varian Eclipse treatment planning system and validate the system in a clinically realistic setting. The innovations include the use of a state-of-the-art AI imitation learning algorithm to solve a clinically important problem, the novel technological capabilities enabled by the developed system, as well as coherent translation activities to deliver new capabilities to end users. Deliverability is ensured by extensive preliminary studies and the partnership integrating complementary expertise and resources. Clinical translation of the AI planner will bring substantial impacts to radiotherapy by providing high-quality and efficient treatment planning to benefit patients, especially those in resource-limited regions.

项目摘要 放射治疗是癌症治疗的主要方法之一。治疗计划， 为每位病人设计最佳的治疗方案是最关键的步骤之一。如果治疗效果不佳 设计，不能达到令人满意的结果，无论其他治疗步骤的质量。 现代放射治疗中的治疗计划被公式化为由以下定义的数学优化问题： 一组超参数虽然存在几个可量化的指标来量化计划质量并指导 计划过程中，这些都是简化的表示，不能完全描述医生的意图。此外，本发明还提供了一种方法， 这些指标只能从基于人群的角度衡量计划质量，不能指导治疗 制定针对患者的最佳治疗计划。因此，最好的医生首选的解决方案往往 处于灰色区域，只能通过广泛的试错超参数调整过程来实现， 规划师和医生之间的互动。因此，规划时间可能需要长达一周的复杂 如果计划者缺乏经验和/或时间限制很大，则案例和计划质量可能很差。这些 结果大大恶化了治疗结果，正如在临床中已经清楚地证明的那样， 问题研究近年来，人工智能（AI）的发展，特别是模仿学习的发展，使人类能够 比如通过观察人类专家的行为并在内部建立自己的决策来做出决策 系统作为对PAR-18-530的回应，该项目的目标是开发和翻译一个模仿 人类专家的行为来生成高质量的计划。人工智能规划师不会取代人类规划师。相反地， 人工智能计划将被用作当前规划过程的起点，以提高规划质量和规划能力 效率人类计划者对进一步计划改进的行动可以通过以下方式反馈给人工智能计划者： 不断学习，不断进化。我们将以前列腺癌为试验平台来实现这一目标 通过学术-工业合作伙伴关系，在UT西南部加入强大的研究和临床专业知识 在瓦里安医疗系统公司拥有丰富的商业产品开发经验的医疗中心。的 确定了以下具体目标。目标1：模型和算法开发。我们将收集专家的行为 常规治疗计划中的数据并训练AI计划者。目标2：系统验证和翻译。我们将 将AI计划器集成到Varian Eclipse治疗计划系统中，并在临床上验证该系统 现实的设定。这些创新包括使用最先进的人工智能模仿学习算法来解决一个 临床上重要的问题，开发的系统所实现的新技术能力，以及 连贯的翻译活动，为最终用户提供新的功能。可扩展性由广泛的 初步研究和伙伴关系整合互补的专门知识和资源。临床转化 人工智能计划器的应用将通过提供高质量和有效的治疗， 计划使患者受益，特别是资源有限地区的患者。