Wells D M, Niederer J
Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
Int J Radiat Oncol Biol Phys. 1998 Apr 1;41(1):173-82. doi: 10.1016/s0360-3016(98)00035-2.
Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency.
The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach.
For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists.
An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely integrated with a dosimetric based treatment planning system.
许多放射治疗计划都涉及某种程度的标准化（例如，在射束弹道、准直器设置和楔形角方面），这主要由肿瘤部位和分期决定。如果能充分采样给定治疗部位相关解剖结构在患者之间的大小和形状差异，那么似乎有可能开发出一种通用方法，将个体患者的解剖结构自动映射到相应的一组治疗变量。开发了一种用于标准化治疗计划的医学专家系统方法，有望提高计划效率和一致性。
专家系统旨在根据一组模板（先前患者的治疗计划数据库）和一种相似性度量来为新患者指定治疗变量，该相似性度量用于确定新患者的相关解剖结构与数据库中患者的匹配程度。使用一组人工神经网络将治疗变量针对个体患者进行优化。以一个简化示例，即基于单个外部轮廓的前列腺治疗四野盒式技术，来测试该方法的可行性。
对于一组新的前列腺癌患者，将专家系统指定的治疗变量与剂量师选择的治疗变量进行比较。性能标准包括靶区内的剂量均匀性以及对周围关键器官的剂量。对于这种标准化的前列腺技术，专家系统要达到剂量师的性能水平，大约需要一个包含75条患者记录的数据库。
标准化治疗计划的专家系统方法有潜力通过减少生成优化剂量分布所需的迭代次数来提高计划过程的整体效率，并且要最有效地发挥作用，应与基于剂量测定的治疗计划系统紧密集成。
