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

基本信息

  • 批准号:
    508465-2017
  • 负责人:
  • 金额:
    $ 7.31万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Collaborative Health Research Projects
  • 财政年份:
    2017
  • 资助国家:
    加拿大
  • 起止时间:
    2017-01-01 至 2018-12-31
  • 项目状态:
    已结题

项目摘要

Radiation therapy (RT) is one of the main methods of treating cancer. The delivery of RT is acomplicated process that requires both clinical and technical expertise. The process ofgenerating an RT treatment plan, which specifies how the radiation dose for treatment is to bedelivered to the patient, relies on complex technology and considerable manual effort oftenrequiring hours to days of dedicated time to plan each patient. We are proposing a method toautomatically generate an RT plan that can readily be integrated into the existing clinical RTprocess.The goal of the research is to develop and make available to other institutions an automatedRT treatment planning method that will i) rapidly generate RT plans in minutes, expediting theRT process, ii) allow patients greater access to RT based on the expertise of establishedcancer institutions in Canada, and iii) produce high quality RT plans that are tailored to eachspecific 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 RTplan data are best for deciding where dose should be placed and how dose should bedelivered in an RT plan. The method automatically learns based on RT plan data frompreviously treated patients and generates a personalized RT plan without requiring anymanual intervention.We are engaging multiple cancer institutions in Canada to evaluate and contribute to thedevelopment of the automated planning method in order to ensure the research has wideclinical applicability and has consensus expert review.The proposed research is applicable to all patients receiving RT as part of their cancermanagement. The research will transform the existing RT process to make better use oflimited resources, while still ensuring high quality, personalized, and cost-effective healthcareis 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。放射治疗计划的制定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,该计划规定了如何将治疗剂量传递给患者。我们提出了一种自动生成放射治疗计划的方法,该方法可以很容易地集成到现有的临床放射治疗过程中。研究的目标是开发一种自动化的放射治疗计划方法,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放射治疗计划,加快放射治疗过程;ii)基于加拿大现有癌症机构的专业知识,允许患者更多地获得放射治疗;以及iii)产生针对每个特定患者的高质量的放射治疗计划。自动计划方法应用最先进的机器学习、图像配准、以及优化算法,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如何输送剂量。该方法根据先前治疗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评估并促进自动化计划方法的开发,以确保研究具有广泛的临床适用性并获得一致的专家评审。建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、个性化和成本效益高的医疗保健。

项目成果

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Purdie, Thomas其他文献

Lung sparing and dose escalation in a robust-inspired IMRT planning method for lung radiotherapy that accounts for intrafraction motion
  • DOI:
    10.1118/1.4805101
  • 发表时间:
    2013-06-01
  • 期刊:
  • 影响因子:
    3.8
  • 作者:
    McCann, Claire;Purdie, Thomas;Bissonnette, Jean-Pierre
  • 通讯作者:
    Bissonnette, Jean-Pierre

Purdie, Thomas的其他文献

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{{ truncateString('Purdie, Thomas', 18)}}的其他基金

Machine Learning Assisted Decision Support Platform for Radiation Treatment Assessment
用于放射治疗评估的机器学习辅助决策支持平台
  • 批准号:
    RGPIN-2022-04163
  • 财政年份:
    2022
  • 资助金额:
    $ 7.31万
  • 项目类别:
    Discovery Grants Program - Individual
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
  • 财政年份:
    2018
  • 资助金额:
    $ 7.31万
  • 项目类别:
    Collaborative Health Research Projects
Improving quality and patient safety in radiation therapy by integrating multi-disciplinary criteria into an artificial intelligence system
通过将多学科标准整合到人工智能系统中,提高放射治疗的质量和患者安全
  • 批准号:
    446596-2013
  • 财政年份:
    2014
  • 资助金额:
    $ 7.31万
  • 项目类别:
    Collaborative Health Research Projects
Improving quality and patient safety in radiation therapy by integrating multi-disciplinary criteria into an artificial intelligence system
通过将多学科标准整合到人工智能系统中,提高放射治疗的质量和患者安全
  • 批准号:
    446596-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 7.31万
  • 项目类别:
    Collaborative Health Research Projects

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