Stieler Florian, Yan Hui, Lohr Frank, Wenz Frederik, Yin Fang-Fang
Department of Radiation Oncology, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany.
Radiat Oncol. 2009 Sep 25;4:39. doi: 10.1186/1748-717X-4-39.
Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined.
The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules.
Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 +/- 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%.
The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting.
在调强放射治疗（IMRT）逆向治疗计划过程中，参数优化主要由人工计划者进行，以创建具有所需剂量分布的计划。为了使这个繁琐的过程自动化，开发并检验了一种人工智能（AI）引导系统。
该AI系统可以基于由几个模糊推理系统（FIS）操作的先验知识自动完成优化过程。先验知识是在人工计划者进行逆向计划的常规试错过程中收集的，首先必须“转换”为一组“如果-那么”规则来驱动FIS。为了最小化在这个知识获取过程中可能代价高昂的主观误差，有必要找到一种定量方法来自动完成这项任务。本研究引入了一种基于自适应神经模糊推理系统（ANFIS）的成熟机器学习技术。基于这种方法，可以从观测数据（临床使用的约束条件）中快速收集模糊推理系统的先验知识。通过从具有已知设置和规则的AI系统收集的数据生成多个FIS，分析了该系统的学习能力和准确性。
多次分析表明，根据规则（基于FIS训练数据的ANFIS输出值误差为7.77±0.02%）和隶属函数（3.9%），FIS和ANFIS具有良好的一致性，因此表明基于此过程，一个FIS的“行为”可以传播到另一个FIS。在一个临床病例上的初步实验结果表明，ANFIS是从实际数据构建FIS的有效方法，并且对ANFIS和FIS与临床病例的分析表明ANFIS提供了良好的计划结果。等剂量特征百分比所包含的危及器官体积平均减少了0%至28%。
该研究证明了一种可行的方法，即在无需人工干预的情况下，在先验知识的指导下自动进行逆向治疗计划的参数优化，只需提供一组在给定设置中已证明临床有用的约束条件。
