Kalet I J, Paluszynski W
Radiation Oncology Department, University of Washington, Seattle.
Am J Clin Oncol. 1990 Aug;13(4):344-51. doi: 10.1097/00000421-199008000-00015.
Radiation therapy is one of the first areas of clinical medicine to utilize computers in support of routine clinical decision making. The role of the computer has evolved from simple dose calculations to elaborate interactive graphic three-dimensional simulations. These simulations can combine external irradiation from megavoltage photons, electrons, and particle beams with interstitial and intracavitary sources. With the flexibility and power of modern radiotherapy equipment and the ability of computer programs that simulate anything the machinery can do, we now face a challenge to utilize this capability to design more effective radiation treatments. How can we manage the increased complexity of sophisticated treatment planning? A promising approach will be to use artificial intelligence techniques to systematize our present knowledge about design of treatment plans, and to provide a framework for developing new treatment strategies. Far from replacing the physician, physicist, or dosimetrist, artificial intelligence-based software tools can assist the treatment planning team in producing more powerful and effective treatment plans. Research in progress using knowledge-based (AI) programming in treatment planning already has indicated the usefulness of such concepts as rule-based reasoning, hierarchical organization of knowledge, and reasoning from prototypes. Problems to be solved include how to handle continuously varying parameters and how to evaluate plans in order to direct improvements.
放射治疗是临床医学中最早利用计算机支持常规临床决策的领域之一。计算机的作用已从简单的剂量计算发展到复杂的交互式图形三维模拟。这些模拟可以将兆伏光子、电子和粒子束的外部照射与间质和腔内源结合起来。凭借现代放疗设备的灵活性和强大功能以及能够模拟机器所能做的任何事情的计算机程序的能力,我们现在面临着利用这种能力设计更有效的放射治疗的挑战。我们如何应对复杂治疗计划日益增加的复杂性?一种有前途的方法是使用人工智能技术来系统化我们目前关于治疗计划设计的知识,并为开发新的治疗策略提供一个框架。基于人工智能的软件工具远非取代医生、物理学家或剂量师,而是可以协助治疗计划团队制定更强大、更有效的治疗计划。目前在治疗计划中使用基于知识(人工智能)编程的研究已经表明了基于规则的推理、知识的层次组织和从原型进行推理等概念的有用性。有待解决的问题包括如何处理不断变化的参数以及如何评估计划以指导改进。