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Intelligent treatment planning for cancer radiotherapy

癌症放疗智能治疗计划

基本信息

批准号：
10593946
负责人：
Xun Jia
金额：
$ 48.03万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10593946
关键词：
3-Dimensional Address Anatomy Area Artificial Intelligence Beds Cancer Patient Clinical Clinical Treatment Communication Complex Conceptions Consultations Curiosities Data Deterioration Dose Environment Failure Generations Head and Neck Cancer Healthcare Human Image Immunotherapy Individuality Intelligence Learning Medical Medical center Medicine Modality Nature Operative Surgical Procedures Organ Patient Care Patients Physicians Play Procedures Process Psychological reinforcement Radiation Radiation therapy Risk Role Site System Systems Development Techniques Technology Testing Time Treatment outcome Validation Vision Work arm cancer radiation therapy cancer therapy chemotherapy deep learning model deep reinforcement learning design experience head and neck cancer patient individual patient individualized medicine innovation negative affect optimal treatments population based preference satisfaction skills success supervised learning treatment planning treatment strategy tumor validation studies

项目摘要

PROJECT SUMMARY About 2/3 of cancer patients in US receive radiation therapy either alone or in conjunction with surgery, chemotherapy, immunotherapy, etc. Treatment planning, where an optimal treatment strategy is designed for each individual patient and executed for the whole treatment course, is analogous to the design of a blueprint for building construction. If a treatment plan is poorly designed, the desired treatment outcome cannot be achieved, no matter how well other components of radiation therapy are performed. In the current clinical workflow, a treatment planner works towards a good quality plan in a trial-and-error fashion. Many rounds of consultation between the planner and physician are needed to reach a plan of physician's satisfaction, because physician's preference for a particular patient can hardly be quantified and precisely conveyed to the planner. Consequently, planning time can be up to a week for complex cases and plan quality may be poor and can vary significantly due to varying levels of physician and planner's skills and physician-planner cooperation, etc., which substantially deteriorates treatment outcomes. For example, head and neck (H&N) cancer patients treated with suboptimal plans present 20% lower 2-year overall survival and 24% higher 2-year local-regional failure. Prolonged overall treatment process due to treatment planning reduces local-regional control rate by 12–14% per week. Furthermore, as patient's anatomy can rapidly change within the planning time, the optimally designed plan becomes inappropriate for the changed anatomy. Recently, artificial intelligence (AI) has made colossal advancements. We believe that AI technologies have a great potential to revolutionize treatment planning. Treatment planning consists of two major aspects: commonality and individuality. By exploiting the commonality through deep supervised learning, we can develop a treatment plan as good as those for previously treated similar patients. The individuality can be actualized by learning physician's special considerations for a particular patient using deep reinforcement learning. Our preliminary studies have demonstrated feasibility of these ideas. We hypothesize that an AI-based intelligent treatment planning system can consistently produce high-quality treatment plans with extremely high efficiency. This hypothesis will be tested using H&N cancer patients as a test bed via two aims. Aim 1, System development. Develop two deep-learning models to realize the proposed treatment planning workflow and incorporate them into a clinical environment. Aim 2, System validation. Acquire and analyze planning data before and after system implementation. The innovation of this project is the use and customization of the state-of-the-art AI techniques to solve a clinically important problem. These technologies would revolutionize treatment planning process, leading to the efficient generation of consistently high quality plans, irrespective of human skills, experiences, and communications, etc. Besides the significance demonstrated for the H&N cancer patients, the system can be easily extended to other tumor sites, yielding more substantial impacts.

项目摘要在美国，大约2/3的癌症患者单独接受放射治疗或与手术联合接受放射治疗，治疗计划，其中最佳治疗策略被设计用于每一个病人，并在整个治疗过程中执行，类似于一个蓝图的设计，用于建筑施工。如果治疗计划设计不当，就无法达到预期的治疗效果。无论放射治疗的其他组成部分执行得多么好。在目前的临床在工作流程中，治疗计划者以试错的方式朝着良好质量的计划工作。多轮需要计划者和医生之间的协商来达成医生满意的计划，因为医生对特定患者的偏好很难量化并精确地传达给医生，计划者因此，复杂情况下的计划时间可能长达一周，计划质量可能很差并且可以由于医生和计划者的技能以及医生-计划者的合作等等，这实质上恶化了治疗结果。例如，头部和颈部（H&N）采用次优方案治疗的癌症患者2年总生存率降低20%，2年总生存率提高24%。局部区域失败。由于治疗计划延长了整个治疗过程，控制率为每周12-14%。此外，由于患者的解剖结构可以在计划内快速变化，随着时间的推移，最佳设计的计划变得不适合改变的解剖结构。最近，人工人工智能（AI）已经取得了巨大的进步。我们认为，人工智能技术具有巨大的潜力，彻底改变治疗计划治疗计划包括两个主要方面：共性和个性。通过深度监督学习利用共性，我们可以制定治疗计划与以前治疗过的类似患者一样好。个性可以通过学习来实现医生对特定患者的特殊考虑使用深度强化学习。我们的初步研究已证明这些构想是可行的。我们假设基于人工智能的智能治疗计划系统可以以极高的效率持续地产生高质量的治疗计划。这该假设将通过两个目标以H&N癌症患者为测试床进行测试。目标1，系统开发。开发两个深度学习模型，以实现拟议的治疗计划工作流程并将其纳入进入临床环境。目标2，系统验证。获取和分析计划前后的数据系统实施。该项目的创新之处在于最先进的AI的使用和定制解决临床上重要问题的技术。这些技术将彻底改变治疗计划过程，导致有效地生成一致的高质量计划，无论人类技能如何，经验，和沟通，等等。除了对H&N癌症患者的意义，该系统可以很容易地扩展到其他肿瘤部位，产生更实质性的影响。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

基于深度学习的个性化3D剂量分布预测的可行性研究。

DOI：
10.1002/mp.15025
发表时间：
2021-08
期刊：
Medical physics
影响因子：
3.8
作者：
Ma J;Nguyen D;Bai T;Folkerts M;Jia X;Lu W;Zhou L;Jiang S
通讯作者：
Jiang S

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

DOI：
10.1088/1361-6560/ac09a2
发表时间：
2021-06-23
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
Shen C;Chen L;Jia X
通讯作者：
Jia X

Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation.

评估机器学习增强了精神主颈（HN）计划生成的智能优化引擎（IOE）性能。

DOI：
10.1002/acm2.13950
发表时间：
2023-07
期刊：
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS
影响因子：
2.1
作者：
Visak, Justin;Inam, Enobong;Meng, Boyu;Wang, Siqiu;Parsons, David;Nyugen, Dan;Zhang, Tingliang;Moon, Dominic;Avkshtol, Vladimir;Jiang, Steve;Sher, David;Lin, Mu-Han
通讯作者：
Lin, Mu-Han

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.