Rabe Moritz, Kurz Christopher, Thummerer Adrian, Landry Guillaume
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
German Cancer Consortium (DKTK), partner site Munich, a partnership between the DKFZ and the LMU University Hospital Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
Strahlenther Onkol. 2025 Mar;201(3):283-297. doi: 10.1007/s00066-024-02277-9. Epub 2024 Aug 13.
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
放射治疗(RT)是一个高度数字化的领域,严重依赖计算方法,因此,它对现代人工智能(AI)所提供的自动化潜力具有很高的亲和力。这在涉及成像的情况下尤其相关,在图像引导放射治疗(IGRT)期间更是如此。随着磁共振(MR)直线加速器(linacs)和锥形束计算机断层扫描(CBCT)直线加速器的在线自适应放射治疗(ART)工作流程的出现,对自动化的需求进一步增加。因此,应用于现代IGRT的AI是RT的一个领域,我们预计在不久的将来会有重要的发展。在这篇综述文章中,在概述现代IGRT和在线ART工作流程之后,我们涵盖了AI在CBCT和MRI校正以进行剂量计算、IGRT成像上的自动分割、运动管理以及基于室内成像的反应评估中的作用。