Gianoli Chiara, De Bernardi Elisabetta, Parodi Katia
Department of Experimental Physics - Medical Physics, Faculty for Physics of the Ludwig-Maximilians-Universität München (LMU Munich), Geschwister-Scholl-Platz 1, München, 80539, Germany.
School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, Milano, 20126, Italy.
BJR Open. 2024 Jul 16;6(1):tzae017. doi: 10.1093/bjro/tzae017. eCollection 2024 Jan.
This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks involved in the radiotherapy workflow. The radiotherapy framework is presented on 2 different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy. The second level is referred to as biology-driven workflow, explored in the research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A 2-fold role for AI is defined according to these 2 different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers that were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or multiomics, when complemented by clinical and biological parameters (ie, biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI's growing role in personalized radiotherapy.
本综述介绍并讨论了人工智能（AI）工具当前进行干预或未来可能进行干预的方式，以增强放射治疗工作流程中涉及的各种任务。为了实现治疗的个性化，放射治疗框架从两个不同层面进行了介绍，在任务和方法上各有不同。第一个层面是临床上成熟的基于解剖结构的工作流程，即自适应放射治疗。第二个层面被称为生物学驱动的工作流程，在研究文献中有所探讨，最近也出现在一些个性化放射治疗的初步临床试验中。根据这两个不同层面，定义了人工智能的双重作用。在基于解剖结构的工作流程中，与传统方法相比，人工智能的作用是在时间和可变性降低方面简化和改进任务。相反，生物学驱动的工作流程完全依赖于人工智能，人工智能引入了决策工具，开辟了过去被认为难以探索的未知领域。这些方法被称为放射组学和剂量组学，用于处理成像和剂量信息，当辅以临床和生物学参数（即生物标志物）时则称为多组学。本综述明确强调了目前已纳入临床实践或仍在研究中的方法，旨在展示人工智能在个性化放射治疗中日益重要的作用。
