Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
Front Med. 2020 Aug;14(4):431-449. doi: 10.1007/s11684-020-0761-1. Epub 2020 Jul 29.
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
放射治疗（RT）被广泛用于癌症治疗。在过去的 30 年中，RT 技术取得了进展。这些进展，如三维图像引导、强度调制和机器人技术，为下一个突破带来了挑战和机遇，人工智能（AI）可能在其中发挥重要作用。AI 将取代某些重复和劳动密集型任务，并提高其他任务的准确性和一致性，特别是那些由于技术进步而变得更加复杂的任务。提高效率和一致性对于管理不断增加的癌症患者负担至关重要。此外，人工智能还可以提供新的功能，促进满意的 RT。这些功能包括用于实时干预和自适应和个性化 RT 的优质图像。人工智能可以有效地合成和分析此类数据。这篇综述描述了 RT 工作流程，并确定了受益于 AI 的领域，包括成像、治疗计划、质量保证和结果预测。本综述主要侧重于深度学习技术，虽然也提到了传统的机器学习技术。
