An artificial intelligence-driven distributed stereotactic radiosurgery strategy for multiple brain metastases management

人工智能驱动的分布式立体定向放射外科治疗多发性脑转移瘤策略

• 批准号: 10543133
• 负责人: Xuejun Gu
• 金额: $ 53.88万
• 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: 2019-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10543133
• 关键词: Address; Adverse effects; Adverse event; Affect; Aftercare; Artificial Intelligence; Brain; Brain Stem; Caring; Clinic; Clinical; Clinical Trials; Cranial Irradiation; Data; Dedications; Development; Distant; Dose; Ensure; Feedback; Foundations; Functional disorder; Goals; Infrastructure; Interdisciplinary Study; Knowledge; Learning; Life; Life Expectancy; Malignant Neoplasms; Maximum Tolerated Dose; Medical; Metastatic malignant neoplasm to brain; Morbidity - disease rate; Neoplasm Metastasis; Neurocognitive; Neurocognitive Deficit; Neurologic; Optic Nerve; Organ; Patients; Performance; Phase; Phase I/II Clinical Trial; Prediction of Response to Therapy; Procedures; Quality of life; Radiation; Radiation Oncologist; Radiation Therapy Oncology Group; Radiation Toxicity; Radiation necrosis; Radiation therapy; Radiosurgery; Relapse; Research; Retrospective Studies; Site; Solid; System; Technology; Therapeutic Intervention; Tissues; Translating; Treatment Protocols; Treatment outcome; United States; automated segmentation; brain size; clinical practice; design; effective therapy; falls; follow-up; imaging Segmentation; improved; innovation; irradiation; learning progression; nervous system disorder; novel; novel therapeutic intervention; outcome prediction; patient population; preservation; prospective; response; spatiotemporal; targeted treatment; technology development; tool; treatment planning; treatment strategy; tumor

PROJECT SUMMARY Brain metastases (BMs) are a life-threatening neurological disease, but current treatment regimens cannot manage multiple (>4) BMs (mBMs) without causing strong adverse effects. Stereotactic radiosurgery (SRS), utilizing potent dose to irradiate BMs and quick dose falloff to spare nearby tissues, has proven to be an effective treatment regimen for limited-number and small-size BMs. However, SRS could not avoid high toxic dose when BMs are multiple, clustered, or adjacent to critical organs. To safe and effectively treat mBMs with SRS requires addressing these urgent needs: 1) to identify the maximum tolerable SRS dose; 2) to study neurocognitive decline and design strategies to preserve patients’ post-treatment quality of life; and 3) to develop and implement high-quality streamlined mBMs SRS treatment and follow-up care. To address mBMs SRS management needs, we aim to develop and implement an artificial intelligence (AI)- driven treatment planning system (TPS) and conduct a therapeutic intervention clinical trial, both dedicated to improve mBMs SRS treatment quality and efficiency. The AI-driven TPS, namely AimBMs, will have three AI- based computational modules, including AI-Segtor for automatic segmentation, AI-Predictor for treatment outcome prediction and AI-Planner for spatiotemporal distributed SRS plan optimization. AimBMs is initially developed based on retrospective data and facilitate the mBMs distributed SRS prospective phase I/II clinical trials, while the clinical trial will provide critical clinical knowledge and evidence as feedback to improve AimBMs performance. The ultimate goal of the project is to translate the AimBMs to routine clinical practice to improve mBMs SRS treatment quality, patients’ post-treatment QoL, and clinical facility workflow. In response to PAR-18-560, we have formed a multidisciplinary collaboration between radiation oncologists and medical physicists to develop a novel AI-driven distributed SRS technology and conduct a cancer-targeted therapeutic intervention for managing mBMs. The project’s innovations include: 1) novel SRS treatment planning technological capability enabled by AI-based auto-segmentation, treatment outcome prediction, and spatiotemporal plan optimization; 2) novel AI learning capability to improve developed AI tools’ performance through the coherent clinical trial. The technology development will support the therapeutic intervention clinical trial, while the clinical trial is designated to improve the developed system performance. This seamlessly integrated development mode ensures the developed system is clinically practical. Upon completion, our newly developed AimBMs will lay a solid foundation for mBMs SRS management and benefit a wide population of patients with BMs. Moreover, the AI-based treatment planning and treatment delivery infrastructure built for mBMs SRS can be transferred to other tumor sites to generate an even broader clinical impact.

项目摘要 脑转移瘤（BM）是一种危及生命的神经系统疾病，但目前的治疗方案不能 管理多个（>4个）BM（mBM），而不会造成强烈的不良影响。立体定向放射外科（SRS）， 利用有效剂量照射BM和快速剂量下降以节省附近组织，已被证明是有效的 有限数量和小尺寸BM的治疗方案。但是，SRS不能避免高毒性剂量， BM为多个、成簇或邻近关键器官。为了安全有效地使用SRS治疗mBM，需要 解决这些迫切需要：1）确定最大耐受SRS剂量; 2）研究神经认知 下降和设计策略，以保持患者的治疗后生活质量;和3）制定和实施 高质量的简化mBM SRS治疗和后续护理。 为了满足mBM SRS管理需求，我们的目标是开发和实施人工智能（AI）- 驱动的治疗计划系统（TPS），并进行治疗干预临床试验，都致力于 提高mBM SRS治疗质量和效率。AI驱动的TPS，即AimBM，将有三个AI- 基于计算模块，包括用于自动分割的AI-Segtor，用于治疗的AI-Predictor 结果预测和用于时空分布式SRS计划优化的AI-Planner。AimBMs最初是 基于回顾性数据开发，并促进mBM分发SRS前瞻性I/II期临床 临床试验将提供关键的临床知识和证据作为反馈，以改善AimBM 性能该项目的最终目标是将AimBM转化为常规临床实践， mBM SRS治疗质量、患者治疗后QoL和临床机构工作流程。 针对PAR-18-560，我们在放射肿瘤学家和 医学物理学家开发一种新的AI驱动的分布式SRS技术，并进行针对癌症的 用于管理mBM的治疗干预。该项目的创新包括：1）新颖的SRS治疗计划 通过基于AI的自动分割、治疗结果预测和 时空规划优化; 2）新的AI学习能力，以提高开发的AI工具的性能 通过连贯的临床试验该技术的发展将支持临床治疗干预 试验，而临床试验被指定为提高开发的系统性能。这天衣无缝 集成开发模式保证了所开发系统的临床实用性。完成后，我们的新 开发的AimBM将为mBM SRS管理奠定坚实的基础， BM患者。此外，基于人工智能的治疗规划和治疗提供基础设施， mBM SRS可以转移到其他肿瘤部位，以产生更广泛的临床影响。