Development of an autonomous treatment planning strategy for radiation therapy with effective use of population-based prior data
Development of an autonomous treatment planning strategy for radiation therapy with effective use of population-based prior data
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DOI：
10.1002/mp.12058
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发表时间：
2017-02-01
期刊：
影响因子：
3.8
通讯作者：
Xing, Lei
中科院分区：
文献类型：
--
作者：
Wang, Huan;Dong, Peng;Xing, Lei
目的：目前的治疗计划仍然是一个昂贵的和劳动密集型的程序，并需要多次试错调整系统参数，如加权因子和处方。本工作的目的是开发一个自主的治疗计划策略，有效地利用先验知识，并在一个临床现实的治疗计划平台，以促进放射治疗workflow.Method：我们的技术包括三个主要组成部分：（i）一个临床治疗计划系统（TPS），（ii）一个制定的决策功能构建使用一个组装的先验治疗计划，（iii）一个治疗计划系统（TPS）。（iii）计划评估器或决策函数以及独立于临床TPS的外循环优化，以评估TPS生成的计划并推动搜索优化决策函数的解决方案。应用Microsoft（MS）Visual Studio Coded UI将一些常见的计划器-TPS交互记录为用于查询和与TPS交互的子例程。这些子程序在外循环优化程序中被回调，以迭代地通过解空间导航计划选择过程。该方法的实用性证明，通过使用临床前列腺和头颈部cases.Results：一个自主的治疗计划技术，有效地利用了一个组装的治疗计划，开发自动机动的商业TPS平台上的临床治疗计划过程。该过程模仿人类计划者的决策过程，并自动提供临床上合理的治疗计划，从而减少/消除治疗计划的繁琐的手动试错。它被发现，前列腺和头颈部的治疗计划，使用该方法产生的比较有利，用于患者的实际treatment.Conclusions：临床逆向治疗规划过程可以有效地自动化与指导下组装的先前治疗计划。该方法有可能显着改善放射治疗工作流程。(C)2016年美国医学物理学家协会
Purpose: Current treatment planning remains a costly and labor intensive procedure and requires multiple trial-and-error adjustments of system parameters such as the weighting factors and prescriptions. The purpose of this work is to develop an autonomous treatment planning strategy with effective use of prior knowledge and in a clinically realistic treatment planning platform to facilitate radiation therapy workflow.Method: Our technique consists of three major components: (i) a clinical treatment planning system (TPS); (ii) a formulation of decision-function constructed using an assemble of prior treatment plans; (iii) a plan evaluator or decision-function and an outer-loop optimization independent of the clinical TPS to assess the TPS-generated plan and to drive the search toward a solution optimizing the decision-function. Microsoft (MS) Visual Studio Coded UI is applied to record some common planner-TPS interactions as subroutines for querying and interacting with the TPS. These subroutines are called back in the outer-loop optimization program to navigate the plan selection process through the solution space iteratively. The utility of the approach is demonstrated by using clinical prostate and head-and-neck cases.Results: An autonomous treatment planning technique with effective use of an assemble of prior treatment plans is developed to automatically maneuver the clinical treatment planning process in the platform of a commercial TPS. The process mimics the decision-making process of a human planner and provides a clinically sensible treatment plan automatically, thus reducing/eliminating the tedious manual trial-and-errors of treatment planning. It is found that the prostate and head-and-neck treatment plans generated using the approach compare favorably with that used for the patients' actual treatments.Conclusions: Clinical inverse treatment planning process can be automated effectively with the guidance of an assemble of prior treatment plans. The approach has the potential to significantly improve the radiation therapy workflow. (C) 2016 American Association of Physicists in Medicine