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

基本信息

批准号：
10610971
负责人：
Xun Jia
金额：
$ 60.6万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
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/10610971
关键词：
Anatomy Area Artificial Intelligence Attention Back Beds Behavior Cancer Patient Caring Clinical Clinical Research Communities Complex Complication Data Decision Making Deterioration Development Dose Ensure Environment Evaluation Evolution Failure 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 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 new technology optimal treatments population based product development prototype response routine practice success treatment planning treatment strategy tumor validation studies

项目摘要

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.

项目概要放射治疗是癌症治疗的主要方法之一。治疗计划是癌症治疗的过程。为每位患者设计最佳的治疗方案是最关键的步骤之一。无论其他治疗步骤的质量如何，都无法获得令人满意的结果。现代放射治疗中的治疗计划被表述为由以下定义的数学优化问题：虽然存在一些可量化的指标来量化计划质量并指导计划过程中，这些都是简化的表述，无法完全描述医生的意图。这些指标仅从基于人群的角度衡量计划质量，不能指导治疗因此，最佳的解决方案通常是医生首选的。位于灰色区域，只能通过广泛的试错超参数调整过程才能实现计划者和医生之间的互动经过检查，复杂的计划时间可能需要长达一周的时间。如果规划者缺乏经验和/或时间紧迫，情况和计划质量可能会很差。后果明显更糟的治疗结果，这一点已在临床中得到明确证明最近，人工智能（AI）的进步，特别是模仿学习，使得人类能够就像通过观察人类专家的行为并在内部构建自己的决策来做出决策一样为了响应 PAR-18-530，该项目的目标是开发和翻译一个模仿的 AI 规划器。人工智能规划者不会取代人类专家的行为，而是会产生高质量的规划。 AI计划将作为当前规划流程的起点，以提高计划质量和规划人类规划者对进一步计划改进的行动可以通过反馈给人工智能规划者。我们将利用前列腺癌作为试验台，不断学习以实现其不断发展。通过学术与工业合作伙伴关系，结合德州大学西南医学中心强大的研究和临床专业知识瓦里安医疗系统公司医疗中心拥有丰富的商业产品开发经验。定义了以下具体目标：目标 1：我们将收集专家的行为。目标 2：系统验证和翻译。将 AI 规划器集成到 Varian Eclipse 治疗规划系统中，并在临床中验证该系统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问题。临床上重要的问题、所开发的系统所带来的新颖的技术能力，以及为最终用户提供新功能的连贯翻译活动通过广泛的内容来确保交付能力。初步研究以及整合互补专业知识和资源的合作伙伴关系。 AI规划师的技术将通过提供优质高效的治疗给放疗带来实质性影响计划造福患者，特别是资源有限地区的患者。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Advances in Automated Treatment Planning.

DOI：
10.1016/j.semradonc.2022.06.004
发表时间：
2022-10
期刊：
Seminars in radiation oncology
影响因子：
3.5
作者：
D. Nguyen;Mu-Han Lin;D. Sher;Wei Lu;X. Jia;Steve B Jiang
通讯作者：
D. Nguyen;Mu-Han Lin;D. Sher;Wei Lu;X. Jia;Steve B Jiang

Site-agnostic 3D dose distribution prediction with deep learning neural networks.

DOI：
10.1002/mp.15461
发表时间：
2022-03
期刊：
Medical physics
影响因子：
3.8
作者：
通讯作者：

Single patient learning for adaptive radiotherapy dose prediction.

单个患者学习自适应放疗剂量预测。

DOI：
10.1002/mp.16799
发表时间：
2023
期刊：
Medical physics
影响因子：
3.8
作者：
Maniscalco,Austen;Liang,Xiao;Lin,Mu-Han;Jiang,Steve;Nguyen,Dan
通讯作者：
Nguyen,Dan

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.

DOI：
10.1088/1361-6560/ac678a
发表时间：
2022-05-27
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
Barragán-Montero A;Bibal A;Dastarac MH;Draguet C;Valdés G;Nguyen D;Willems S;Vandewinckele L;Holmström M;Löfman F;Souris K;Sterpin E;Lee JA
通讯作者：
Lee JA

Modeling physician's preference in treatment plan approval of stereotactic body radiation therapy of prostate cancer.