Intelligent treatment planning for cancer radiotherapy

癌症放疗智能治疗计划

基本信息

批准号：
10190850
负责人：
Xun Jia
金额：
$ 49.01万
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 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10190850
关键词：
3-Dimensional Address Anatomy Area Artificial Intelligence Arts Beds Cancer Patient Clinical Clinical Treatment Communication Complex Conceptions Consultations Curiosities Custom Data Dose Due Process Environment Failure Generations Head and Neck Cancer Healthcare Human Image Immunotherapy Individuality Intelligence Learning Medical Medical center Medicine Modality Modeling 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 Work arm base cancer radiation therapy cancer therapy chemotherapy deep learning 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.

项目概要在美国，大约 2/3 的癌症患者接受单独的放射治疗或联合手术治疗，化疗、免疫疗法等。治疗计划，其中设计最佳治疗策略每个患者并在整个治疗过程中执行，类似于蓝图的设计如果处理计划设计不当，就无法达到预期的处理结果。无论放射治疗的其他部分在当前的临床中执行得如何，都可以实现。在工作流程中，治疗计划者通过多轮试错的方式制定出高质量的计划。需要计划者和医生之间协商才能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准确传达给医生经过检查，对于复杂的情况，计划时间可能长达一周，而且计划质量可能很差。由于医生和计划者的技能水平以及医生-计划者的水平不同，可能会有很大差异合作等，这会严重恶化治疗效果，例如头颈 (H&N)。采用次优计划治疗的癌症患者的 2 年总生存率降低 20%，2 年总生存率提高 24% 由于治疗计划而延长的整体治疗过程减少了局部区域的失败。每周控制率提高 12–14% 此外，由于患者的身体结构可能会在计划内迅速发生变化。随着时间的推移，优化设计的计划变得不适合最近改变的解剖结构。人工智能（AI）已经取得了巨大的进步，我们相信人工智能技术具有巨大的潜力。彻底改变治疗计划。治疗包括两个主要方面的计划：共性和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，可以通过学习来实现个性。医生使用深度强化学习对特定患者的特殊考虑。研究证明了这些想法的可行性，我们致力于基于人工智能的智能治疗。规划系统能够以极高的效率持续制定高质量的治疗计划。将通过两个目标，使用 H&N 癌症患者作为试验台来测试假设。开发两个深度学习模型以实现所提出的治疗计划工作流程并将其合并目标 2，获取并分析之前和之后的计划数据。该项目的创新之处在于最先进的人工智能的使用和定制。解决临床重要问题的技术这些技术将彻底改变治疗计划。流程，从而有效生成始终如一的高质量计划，无论人员技能如何，除了对H&N癌症患者所展示的意义外，该系统可以轻松扩展到其他肿瘤部位，产生更实质性的影响。