Decision Support System for Stereotactic Radiosurgery

立体定向放射外科决策支持系统

• 批准号: RGPIN-2019-04715
• 负责人: Ruschin, Mark
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714550
• 关键词: Decision; Support; System; Stereotactic; Radiosurgery

The Gamma Knife (GK) system uses 192 small radiation beams that converge to deliver a high radiation dose within a brain tumour. This technique, called “stereotactic radiosurgery” (SRS) is an effective way of treating brain cancer. The point at which the beams intersect is called an “isocentre” and the treatment involves multiple isocentres to ensure the entire tumour receives a curative dose. Determination of the best isocentre positions and corresponding dwell times is a complex task, since there is a direct conflict between maximizing tumour dose and minimizing dose in surrounding healthy brain. Furthermore, as the number of patients requiring treatment is rapidly increasing due to an aging population and improved drug therapies, we need efficient computerized methods for automating this process. The present research program involves developing models of automation in order to obtain a deeper understanding of the complex interplay between the various objectives. The present program involves two main themes of research as follows. In the first theme, we will explore novel “inverse” automation models. In inverse methods we define the desired output and the computer infers the input. In SRS the desired output is a high radiation dose to the tumour and low dose to normal tissue and the input is the radiation dwell time at each isocentre. The mathematical formulation involves minimizing the numerical difference between the ideal and model-predicted radiation dose. Since the goals conflict (high tumour dose vs. low normal brain dose) and computers do not know what tradeoffs are acceptable, the user sets priorities in the algorithm via weighting factors. However since the relationship of such weight parameters to the solution is not known in advance, one needs to repeat the computation with different weight factors, and by doing so we generate a reasonable model of the problem. The mathematical models will ultimately feed back to end users to decide how weighting factors should be allocated, which will lead to completely automated planning in the future. In the second theme, our group will explore novel artificial intelligence (AI) based automation models. In AI, thousands of cases are used to define features such as tumour volume, and tumour histology. Novel mathematical approaches are then used to establish linkages between all of the features and the outcome, which can be a treatment plan quality metric. The result is a model that we can use to better understand the complex interplay between all of the features and outcome. It is possible to validate a given model by testing its predictive power for an unknown case, not included in the modeling itself. However the emphasis of the current program is in the engineering of the models to understand the problem. The successful completion of this program will lead to future clinically-oriented studies that can use such prediction models for informing plan quality outcome.

伽玛刀（GK）系统使用192个小辐射束汇聚，在脑肿瘤内传递高剂量的辐射。这种技术被称为“立体定向放射外科”（SRS），是治疗脑癌的有效方法。光束相交的点被称为“等中心点”，治疗包括多个等中心点，以确保整个肿瘤接受治疗剂量。确定最佳等中心位置和相应的停留时间是一项复杂的任务，因为在周围健康大脑中存在最大肿瘤剂量和最小剂量之间的直接冲突。此外，由于人口老龄化和药物治疗的改进，需要治疗的患者数量正在迅速增加，我们需要有效的计算机化方法来自动化这一过程。目前的研究计划包括开发自动化模型，以便更深入地了解各种目标之间复杂的相互作用。本项目涉及以下两个主要研究主题。