Abstract：
Radiation therapy has dramatically changed with the advent of computed tomography and intensity modulation. This added complexity to the workflow but allowed for more precise and reproducible treatment. As a result, these advances required the accurate delineation of many more volumes, raising questions about how to delineate them, in a uniform manner across centers. Then, as computing power improved, reverse planning became possible and three-dimensional dose distributions could be generated. Artificial intelligence offers the opportunity to make such workflow more efficient while increasing practice homogeneity. Many artificial intelligence-based tools are being implemented in routine practice to increase efficiency, reduce workload and improve homogeneity of treatments. Data retrieved from this workflow could be combined with clinical data and omic data to develop predictive tools to support clinical decision-making process. Such predictive tools are at the stage of proof-of-concept and need to be explainatory, prospectively validated, and based on large and multicenter cohorts. Nevertheless, they could bridge the gap to personalized radiation oncology, by personalizing oncologic strategies, dose prescriptions to tumor volumes and dose constraints to organs at risk.
摘要：
随着计算机断层扫描和强度调制的出现，放射治疗发生了巨大变化。这增加了工作流程的复杂性，但允许更精确和可重复的治疗。因此,这些进步需要准确描绘更多的卷，提出了如何描绘它们的问题，以统一的方式跨中心。然后，随着计算能力的提高，逆向规划成为可能，并且可以生成三维剂量分布。人工智能提供了使这种工作流程更高效的机会，同时增加了实践的同质性。许多基于人工智能的工具正在日常实践中实现，以提高效率，减少工作量，提高治疗的均匀性。从该工作流程中检索到的数据可以与临床数据和组学数据相结合，以开发预测工具来支持临床决策过程。这种预测工具正处于概念验证阶段，需要具有解释性，经过前瞻性验证，并基于大型和多中心队列。然而，他们可以弥合个性化放射肿瘤学的差距，通过个性化肿瘤策略，肿瘤体积的剂量处方和对危险器官的剂量限制。
