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.

项目摘要美国大约有2/3的癌症患者单独或与手术结合进行放射治疗，化学疗法，免疫疗法等。治疗计划，在此设计最佳治疗策略每个患者并在整个治疗过程中执行，类似于蓝图的设计用于建筑建设。如果设计计划的设计较差，那么所需的治疗结果就不可能是无论进行放射疗法的其他组成部分如何，都可以实现。在当前的临床中 Workflow是一名治疗策划者，以试验方式进行高质量的计划。许多回合需要在计划者和实物之间进行咨询，以达到物理学的满意计划，因为物理学对特定患者的偏爱很难被量化并精确地传达给计划者。因此，对于复杂案例而言，计划时间最多可能是一周，计划质量可能很差并且由于身体和计划者的技能和物理分支机的水平变化而有很大差异合作等，基本决定了治疗结果。例如，头部和颈部（H＆N）接受次优计划治疗的癌症患者的2年总生存率降低了20％，而2年的癌症患者则为24％地方区域失败。由于治疗计划的延长总体治疗过程可减少局部区域控制率每周12–14％。此外，由于患者的解剖学可以在计划中迅速改变时间，最佳设计的计划对于改变的解剖结构是不合适的。最近，人造情报（AI）取得了巨大的进步。我们认为AI技术具有很大的潜力革新治疗计划。治疗计划包括两个主要方面：共同性和个性。通过深入监督学习利用共同点，我们可以制定治疗计划与先前治疗的类似患者一样好。个性可以通过学习实现物理学对特定患者的特殊考虑，并使用深度加强学习。我们的初步研究证明了这些思想的可行性。我们假设基于AI的智能治疗计划系统可以始终如一地制定高质量的治疗计划。这假设将通过H＆N癌症患者通过两个目标进行测试床检验。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结合起来进入临床环境。 AIM 2，系统验证。在之前和之后获取和分析计划数据系统实现。该项目的创新是最先进的AI的使用和定制解决临床重要问题的技术。这些技术将彻底改变治疗计划过程，导致有效地产生一贯的高质量计划，无论人类技能如何经验和通讯等。除了对H＆N癌症患者表现出的重要性外，该系统可以很容易地扩展到其他肿瘤部位，从而产生更大的影响。

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

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

DOI：
10.1002/mp.13953
发表时间：
2020-02
期刊：
Medical physics
影响因子：
3.8
作者：
Xing Y;Nguyen D;Lu W;Yang M;Jiang S
通讯作者：
Jiang S

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

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

Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy.