Novel optimization framework for real-time automated radiation therapy

实时自动放射治疗的新颖优化框架

• 批准号: ST/S002197/1
• 负责人: Suzanne Sheehy
• 金额: $ 7.54万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FS002197%2F1
• 关键词: Novel; optimization; framework; real; time

The World Health Organization estimates that over 8 million people die of cancer every year, around 70% of them in low and middle income countries. Radiation therapy (RT), a process whereby x-ray or particle beams are used to kill specific cells in cancer patients, is one of the most commonly used and cost effective ways to help treat cancer patients. It is estimated that over 50% of all cancer patients may benefit from receiving RT during the course of their treatment, either on its own or in combination with surgery, chemotherapy, hormonal therapy, or immunotherapy. However, the delivery of radiation therapy treatment plans is time consuming, involves cumbersome treatment planning systems (TPS), is expensive both in terms of personnel and infrastructure, and can be hampered by inaccurate computational models. This makes the delivery of high-quality and affordable treatment a challenging task globally, but also one which disproportionally affects low and middle income countries. Further, while the availability of RT centres in North America, Europe, Japan and Australia is generally adequate to cover current needs, similar coverage remains poor in Africa (34% of estimated need covered) and in the wider Asia-Pacific region (61%). As a consequence, the majority of the global population does not have sufficient access to appropriate cancer treatment. Unless addressed, this situation is expected to worsen further given that cancer incidence rates are projected to grow significantly in low and middle income countries over the next decade. As such, increasing the availability of high-quality cancer treatment, and of RT in particular, is recognized as a key global and societal challenge. Within this context, making RT more widely available, increasingly accurate, faster, and more cost effective, will play an important part in addressing this challenge.The delivery of high-quality radiation therapy relies on accurate treatment planning systems to create appropriate radiation treatment plans (TP) across a spectrum of different cancer types. Optimizing these TP can require considerable computational resources and is often personnel-intensive. In this project we will develop a fully-automated treatment planning system prototype based on advanced optimization techniques and remote supercomputing, thus addressing an important difficulty in deploying RT systems in challenging and remote environments. The system will make use of a range of cutting-edge computational and machine learning techniques to optimize the efficiency and robustness of treatment planning systems, with the view to reduce infrastructure and personnel costs in radiation therapy centres, and to provide more flexibility for use where expertise and large-scale computational resources may not be readily available.

世界卫生组织估计，每年有超过 800 万人死于癌症，其中约 70% 生活在低收入和中等收入国家。放射治疗 (RT) 是一种使用 X 射线或粒子束杀死癌症患者体内特定细胞的过程，是帮助治疗癌症患者的最常用且最具成本效益的方法之一。据估计，超过 50% 的癌症患者可能会在治疗过程中受益于接受放疗，无论是单独放疗还是与手术、化疗、激素疗法或免疫疗法相结合。然而，放射治疗计划的实施非常耗时，涉及繁琐的治疗计划系统（TPS），在人员和基础设施方面都很昂贵，并且可能受到不准确的计算模型的阻碍。这使得提供高质量且负担得起的治疗成为全球一项艰巨的任务，而且对低收入和中等收入国家的影响也尤为严重。此外，虽然北美、欧洲、日本和澳大利亚的 RT 中心总体上足以满足当前需求，但非洲（覆盖估计需求的 34%）和更广泛的亚太地区（61%）的类似覆盖率仍然很低。因此，全球大多数人口无法充分获得适当的癌症治疗。如果不加以解决，鉴于未来十年低收入和中等收入国家的癌症发病率预计将大幅上升，这种情况预计将进一步恶化。因此，提高高质量癌症治疗，特别是放疗的可用性，被认为是全球和社会的一项关键挑战。在此背景下，使放疗变得更广泛、更准确、更快速且更具成本效益，将在应对这一挑战中发挥重要作用。高质量放射治疗的实施依赖于准确的治疗计划系统，以针对各种不同的癌症类型制定适当的放射治疗计划（TP）。优化这些 TP 可能需要大量的计算资源，并且通常需要大量人员。在这个项目中，我们将开发一个基于先进优化技术和远程超级计算的全自动治疗计划系统原型，从而解决在具有挑战性的远程环境中部署RT系统的一个重要困难。该系统将利用一系列尖端的计算和机器学习技术来优化治疗计划系统的效率和稳健性，以减少放射治疗中心的基础设施和人员成本，并在可能无法轻易获得专业知识和大规模计算资源的情况下提供更大的使用灵活性。