Meyer Robert R, Zhang Hao H, Goadrich Laura, Nazareth Daryl P, Shi Leyuan, D'Souza Warren D
Computer Sciences Department, University of Wisconsin, Madison, WI, USA.
Int J Radiat Oncol Biol Phys. 2007 Jul 15;68(4):1178-89. doi: 10.1016/j.ijrobp.2007.02.051. Epub 2007 May 23.
To describe a multiplan intensity-modulated radiotherapy (IMRT) planning framework, and to describe a decision support system (DSS) for ranking multiple plans and modeling the planning surface.
One hundred twenty-five plans were generated sequentially for a head-and-neck case and a pelvic case by varying the dose-volume constraints on each of the organs at risk (OARs). A DSS was used to rank plans according to dose-volume histogram (DVH) values, as well as equivalent uniform dose (EUD) values. Two methods for ranking treatment plans were evaluated: composite criteria and pre-emptive selection. The planning surface determined by the results was modeled using quadratic functions.
The DSS provided an easy-to-use interface for the comparison of multiple plan features. Plan ranking resulted in the identification of one to three "optimal" plans. The planning surface models had good predictive capability with respect to both DVH values and EUD values and generally, errors of <6%. Models generated by minimizing the maximum relative error had significantly lower relative errors than models obtained by minimizing the sum of squared errors. Using the quadratic model, plan properties for one OAR were determined as a function of the other OAR constraint settings. The modeled plan surface can then be used to understand the interdependence of competing planning objectives.
The DSS can be used to aid the planner in the selection of the most desirable plan. The collection of quadratic models constructed from the plan data to predict DVH and EUD values generally showed excellent agreement with the actual plan values.
描述一种多平面调强放射治疗(IMRT)计划框架,并描述一种用于对多个计划进行排序和对计划表面进行建模的决策支持系统(DSS)。
通过改变每个危及器官(OAR)的剂量体积约束,依次为一例头颈病例和一例盆腔病例生成125个计划。使用DSS根据剂量体积直方图(DVH)值以及等效均匀剂量(EUD)值对计划进行排序。评估了两种对治疗计划进行排序的方法:综合标准和优先选择。使用二次函数对由结果确定的计划表面进行建模。
DSS提供了一个易于使用的界面,用于比较多个计划特征。计划排序导致识别出一到三个“最优”计划。计划表面模型对DVH值和EUD值都具有良好的预测能力,一般误差<6%。通过最小化最大相对误差生成的模型的相对误差明显低于通过最小化平方和误差获得的模型。使用二次模型,一个OAR的计划属性被确定为其他OAR约束设置的函数。然后,建模的计划表面可用于理解相互竞争的计划目标之间的相互依存关系。
DSS可用于帮助计划者选择最理想的计划。从计划数据构建的用于预测DVH和EUD值的二次模型集合通常与实际计划值显示出极好的一致性。