Intelligent treatment planning for cancer radiotherapy

癌症放疗智能治疗计划

• 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
• 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

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.

项目总结 美国约三分之二的癌症患者单独或与手术联合接受放射治疗， 化疗、免疫治疗等。治疗计划，其中最优治疗策略是为 每一个患者的个体和整个治疗过程的执行，就好比设计了一张蓝图 用于建筑施工。如果治疗计划设计得很差，期望的治疗结果不可能是 无论放射治疗的其他部分做得多么好，都能实现。在当前的临床中 在工作流程中，治疗规划师以一种反复试验的方式努力制定一个高质量的计划。多轮的 需要规划者和医生之间的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，也很难准确地传达给 规划师。因此，对于复杂的案例，计划时间可能长达一周，并且计划质量可能很差 由于医生和规划师的技能和医生规划师的不同水平，可能会有很大差异 合作等，这大大恶化了治疗结果。例如，Head and Neck(H&N) 接受次优方案治疗的癌症患者两年总生存率降低20%，两年生存率提高24% 地方性的失败。由于治疗计划而延长了整个治疗过程，减少了局部-区域 控制率每周提高12-14%。此外，由于患者的解剖结构可能会在计划内迅速变化 随着时间的推移，优化设计的方案变得不适合改变的解剖结构。最近，人工的 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。治疗计划由两个主要方面组成：共同性和 个性。通过深度监督学习来挖掘共性，我们可以制定治疗计划 与之前治疗的类似患者一样好。个性可以通过学习来实现 使用深度强化学习，医生对特定患者的特殊考虑。我们的预赛 研究已经证明了这些想法的可行性。我们假设一种基于人工智能的智能治疗 计划系统能够以极高的效率始终如一地产生高质量的治疗计划。这 假设将通过两个目标以H&N癌症患者作为试验床进行验证。目标1，系统开发。 开发两个深度学习模型来实现拟议的治疗计划工作流并将其合并 进入临床环境。目标2，系统验证。获取并分析规划前后的数据 系统实施。该项目的创新之处在于对最先进的人工智能的使用和定制 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。 流程，从而高效地生成一贯的高质量计划，而不考虑人的技能， 体验和交流等。除了对H&N癌症患者展示的意义之外， 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。