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Machine learning applications in radiation oncology

机译：辐射肿瘤学中的机器学习应用

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Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
机译：机器学习技术对辐射肿瘤的影响越来越多，在研究和工业中增加了存在。多种数据的普遍性包括3D成像和3D辐射剂量递送给未来自动化和治疗患者治疗改进的潜力提出了潜力。利用这种潜力需要标准化工具和数据，并在专业领域之间的重点合作。辐射肿瘤学治疗技术的快速进步为机器学习集成与针对数据质量，数据提取，软件和临床专业知识的参与的投资提供了机会。在本次审查中，我们在审查将机器学习的进步进行辐射肿瘤学并将这些技术集成到辐射肿瘤学工作流程之前，我们提供了机器学习概念的概述。在应用机器学习的辐射肿瘤工作流程中概述了几个关键领域，并且在效率方面可能产生重大影响，治疗的一致性和整体治疗结果。这篇综述亮点由于许多任务的重复性，而且目前还具有人为审查的许多任务的重复性，因此突出了机器学习在辐射肿瘤学中具有关键应用。常规收集的成像和辐射剂量数据的标准化数据管理也被强调为在利用机器学习的研究中实现参与，并且该技术将这些技术与临床工作流程集成到临床工作流程中以使患者受益。物理学家需要成为促进这种技术整合的谈话的一部分。

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