Artificial Intelligence Driven Automatic Treatment Planning of Stereotactic Radiosurgery for the Management of Multiple Brain Metastases

人工智能驱动的立体定向放射外科治疗多发性脑转移瘤自动治疗计划

• 批准号: 10501864
• 负责人: Zhen Tian
• 金额: $ 36.59万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10501864
• 关键词: 3-Dimensional; Affect; Artificial Intelligence; Arts; Beds; Brain; Cancer Patient; Caring; Clinical; Clinical Research; Clinical Trials; Cognition; Communication; Computer Vision Systems; Consultations; Custom; Decision Making; Development; Devices; Disease; Dose; Effectiveness; Evaluation; Excision; Feedback; Foundations; Gamma Knife Radiosurgery; Guidelines; Hour; Human; Intelligence; Learning; Life; Location; Malignant Neoplasms; Manuals; Mathematics; Medical Device; Medicine; Metastatic malignant neoplasm to brain; Methods; Modernization; National Comprehensive Cancer Network; Neurocognition; Neurocognitive Deficit; Normal tissue morphology; Operative Surgical Procedures; Organ; Outcome; Pancreas; Patient Care; Patients; Physicians; Policies; Process; Prospective Studies; Prostate; Psychological reinforcement; Radiation therapy; Radiosurgery; Reporting; Resource-limited setting; Rewards; Robotics; Safety; System; Systems Integration; Technology; Testing; Time; Treatment outcome; Validation; Variant; base; cancer radiation therapy; deep learning; deep neural network; deep reinforcement learning; design; improved; individual patient; innovation; knowledge base; palliative; patient prognosis; preference; prospective; prototype; satisfaction; skills; standard of care; technology development; treatment planning; tumor

Project Summary/Abstract Brain metastases (BMs) are a life-threatening disease, occurring in up to 40% of cancer patients. About 40% of BM patients have multiple (≥4) BMs (mBMs). Whole brain radiotherapy (WBRT), which has long been the standard of care for mBMs patients, has shown pronounced impairment of neurocognitive functions. Stereotactic radiosurgery (SRS) has improved tumor control and reduced negative effect on cognition function, compared to WBRT. However, it has been historically reserved only for patients with <4 BMs. Recently, several clinical trials reported strong evidence to support SRS for mBMs patients. National Comprehensive Cancer Network guidelines hence no longer restrict the number of BMs for SRS. However, the larger BM number in mBMs patients substantially increases the complexity of treatment planning. Conventional manual forward planning to manually determine plan parameters becomes cumbersome and impractical for mBMs. Modern inverse planning methods can determine plan parameters by solving an optimization problem that is composed of multiple objectives designed for various clinical or practical considerations, while the priorities among these objectives affect the resulting plan quality. The physician’s preferences for a particular patient can hardly be quantified and precisely conveyed to the planner, especially for mBMs patients due to the varying number, size, and locations of BMs. Hence, the best physician-preferred plan is often achieved through extensive trial-and-error priority tuning and several rounds of interactions between the planner and physician. Consequently, planning time can take up to hours, and plan quality may be suboptimal and can vary significantly, due to the varying levels of physician and planner’s skills and physician-planner communication and cooperation, leading to deteriorated clinical outcome. Inspired by the recent colossal advancements of artificial intelligence (AI), particularly deep reinforcement learning (DRL) and deep inverse reinforcement learning (DIRL), on intelligent decision-making in computer visions and robotics, we propose to develop an artificial intelligence driven automatic SRS treatment planning system for effective management of mBMs (Aid-mBMs), learning a human-level intelligence on treatment planning from human experts. We envision the system to have two deep neural networks: DNN-R that acts as an AI-physician to predict the physician’s preferences for each individual patient, and DNN-P that acts as an AI-planner to tune the priorities to achieve a plan of physician’s satisfaction. We will pursue two specific aims. Aim 1. System prototype development: We will collect human expert planners’ priority-tuning actions and develop DNN-R and DNN-P via interleaved DIRL-based reward function learning and DRL-based policy learning. Aim 2. System improvement and end-to-end evaluation: We will perform a prospective study to improve our system based on human expert’s further fine-tuning actions on the generated AI plans, and then evaluate the feasibility, effectiveness, and efficiency of our system. Upon completion, Aid-mBMs will provide high-quality and efficient SRS treatment planning to benefit mBMs patients, especially those in resource-limited regions.

项目摘要/摘要 脑转移（BMS）是一种威胁生命的疾病，发生在多达40％的癌症患者中。约40％ BM患者有多个（≥4）BMS（MBM）。整个大脑放疗（WBRT），长期以来一直是 MBMS患者的护理标准已显示出明显的神经认知功能受损。立体定向 放射外科手术（SRS）已改善肿瘤控制，对认知功能的负面影响降低 wbrt。但是，历史上仅用于<4 BMS的患者。最近，一些临床试验 报告了有力的证据支持MBMS患者的SRS。国家综合癌症网络 因此，指南不再限制SRS的BMS数量。但是，MBMS中较大的BM数 患者大大提高了治疗计划的复杂性。传统的手动远期计划 对于MBM，手动确定计划参数变得繁琐且不切实际。现代反向计划 方法可以通过解决由多个组成的优化问题来确定计划参数 为各种临床或实际考虑的目标而设计的目标，而这些目标之间的优先事项 影响最终的计划质量。物理学对特定患者的偏好几乎无法量化，并且 精确地传达给计划者，特别是由于数量，大小和位置的不同而针对MBMS患者 BMS。因此，通常通过广泛的反复试验来实现最佳的物理优先计划 调整以及计划者与物理之间的几轮相互作用。因此，计划时间可以 最多需要数小时，计划质量可能是最佳的，并且由于不同的水平，可能会有很大的不同 物理和计划者的技能以及物理播放器的交流与合作，导致细节 临床结果。受人工智能（AI）最近巨大进步的启发，尤其是深度 强化学习（DRL）和深层增强学习（DIRL），智能决策 计算机视觉和机器人技术，我们建议开发人工智能驱动的自动SRS处理 有效管理MBM（AID-MBM）的计划系统，学习人类水平的智能 人类专家的治疗计划。我们设想该系统具有两个深神经网络：DNN-R 充当AI-Physician，以预测每个患者的医生偏好，而DNN-P的行为 作为AI-Planner，可以调整优先事项，以实现身体满意的计划。我们将追求两个特定的 目标。目标1。系统原型开发：我们将收集人类专家规划师的优先调整行动和 通过基于DIRL的奖励功能学习和基于DRL的政策学习来开发DNN-R和DNN-P。 目标2。系统改进和端到端评估：我们将进行一项前瞻性研究以改善我们的 基于人类专家对生成的AI计划的进一步微调行动的系统，然后评估 我们系统的可行性，有效性和效率。完成后，AID-MBM将提供高质量的 有效的SRS治疗计划使MBMS患者受益，尤其是在资源有限地区的患者。