Advancing Personalized Cancer Care with an Automated Radiomics-Based Radiation Therapy Method

利用基于放射组学的自动化放射治疗方法推进个性化癌症护理

• 批准号: 508465-2017
• 负责人: Purdie, Thomas
• 金额: $ 12.81万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662715
• 关键词: Advancing; Personalized; Cancer; Care; Automated

Radiation therapy (RT) is one of the main methods of treating cancer. The delivery of RT is a**complicated process that requires both clinical and technical expertise. The process of**generating an RT treatment plan, which specifies how the radiation dose for treatment is to be**delivered to the patient, relies on complex technology and considerable manual effort often**requiring hours to days of dedicated time to plan each patient. We are proposing a method to**automatically generate an RT plan that can readily be integrated into the existing clinical RT**process.**The goal of the research is to develop and make available to other institutions an automated**RT treatment planning method that will i) rapidly generate RT plans in minutes, expediting the**RT process, ii) allow patients greater access to RT based on the expertise of established**cancer institutions in Canada, and iii) produce high quality RT plans that are tailored to each**specific patient.**The automated planning method applies state-of-the-art machine learning, image registration,**and optimization algorithms to learn which relationships and patterns in RT image and RT**plan data are best for deciding where dose should be placed and how dose should be**delivered in an RT plan. The method automatically learns based on RT plan data from**previously treated patients and generates a personalized RT plan without requiring any**manual intervention.**We are engaging multiple cancer institutions in Canada to evaluate and contribute to the**development of the automated planning method in order to ensure the research has wide**clinical applicability and has consensus expert review.**The proposed research is applicable to all patients receiving RT as part of their cancer**management. The research will transform the existing RT process to make better use of**limited resources, while still ensuring high quality, personalized, and cost-effective healthcare**is accessible for all RT patients.

放射治疗(RT)是治疗癌症的主要方法之一。RT的实施是一个**复杂的过程，需要临床和技术专业知识。制定放射治疗计划的过程需要复杂的技术和大量的人工工作，通常需要数小时到数天的专门时间来计划每个患者。我们提出了一种方法，可以**自动生成可轻松集成到现有临床RT**过程中的放疗计划。**研究的目标是开发一种自动化的**RT治疗计划方法，并使其可供其他机构使用，该方法将i)在几分钟内快速生成RT计划，加快**RT过程，ii)允许患者基于加拿大现有**癌症机构的专业知识更好地获得RT，以及iii)产生为每个**特定患者量身定做的高质量RT计划。**自动化计划方法应用最先进的机器学习、图像配准、**和优化算法，以了解RT图像和RT**计划数据中的哪些关系和模式最适合于决定在RT计划中应该在哪里放置剂量以及应该如何**提供剂量。该方法根据**以前治疗过的患者的放射治疗计划数据自动学习，并生成个性化的放射治疗计划，而不需要**任何**人工干预。**我们正在聘请加拿大的多家癌症机构来评估和促进**自动化计划方法的**开发，以确保该研究具有广泛的**临床适用性并得到专家一致的审查。**拟议的研究适用于所有接受放射治疗的患者，作为其癌症管理的一部分**。这项研究将改变现有的RT流程，以更好地利用**有限的资源，同时仍确保所有RT患者都能获得高质量、个性化和经济高效的医疗保健**。