Interactively exploring optimized treatment plans
Interactively exploring optimized treatment plans
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DOI：
10.1016/j.ijrobp.2004.09.022
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发表时间：
2005-02-01
影响因子：
7
通讯作者：
Liao, ZX
中科院分区：
文献类型：
--
作者：
Rosen, I;Liu, HH;Liao, ZX
目的：提出一种新的治疗规划范式，它体现了交互式探索优化方案空间的概念。在这种方法中，治疗计划忽略了个人计划的细节，而是向医生呈现明确临床目标的解决方案集的临床摘要，其中每个解决方案都已通过计算机算法预先优化。方法和材料：在交互计划之前，针对各种治疗方案和关键结构剂量-体积约束创建优化计划集。然后，将优化方案的剂量-体积参数拟合成线性函数。这些线性函数被用来实时显示当关键结构的剂量-体积直方图(DVH)交互改变时，目标剂量-体积直方图(DVH)的变化。使用优化方案空间的位图来约束可行解。医生选择给予计划目标体积(PTV)所需剂量的关键结构剂量-体积约束，然后使用这些约束来创建相应的优化计划。结果：使用原型软件、治疗计划资源管理器(TPex)和一个右肺肿瘤患者的临床实例对该方法进行了演示。在本示例中，交付选项包括4个开梁、12个开梁、4个楔形梁和12个楔形梁。针对肺部和食道的一系列关键结构剂量-体积约束，优化了射束方向和相对重量。脐带剂量限制在45Gy.使用交互界面，医生探索了随着关键结构剂量-体积约束的收紧或放松，肿瘤剂量如何变化，并为每种输送方案选择了最佳折衷方案。计算相应的治疗计划，并与在TPex中提交给医生的线性参数进行比较。最大PTV剂量的线性拟合最好，最小PTV剂量的线性拟合最差。根据拟合值与其相应数据值之间的均方根误差，线性拟合似乎是足够的，尽管高次多项式可能会给出更好的结果。拟合中的一些差异是由于模拟退火法优化算法的随机性造成的，该算法在重复相同的计算时不会重复完全相同的结果。使用定向搜索算法进行计划优化应能产生更好的参数拟合，从而更好地预测计划特征。结论：使用TPEX，医生可以很容易地为患者选择最优的计划，而不会强加“最佳”计划的任意定义。更重要的是，医生可以很容易地看到使用给定的分娩技术可以为患者实现什么。对于是否存在更好的计划，没有更多的不确定性。通过比较不同交付方案的“最佳”方案(例如，三维适形放射治疗与调强放射治疗)，医生可以评估技术复杂性更高的临床益处。然而，在TPEX过程可以用于临床之前，需要更快的计算机和/或算法，并且需要更多的研究来更好地对优化解的空间进行建模。(C)2005年爱思唯尔公司。
Purpose: A new paradigm for treatment planning is proposed that embodies the concept of interactively exploring the space of optimized plans. In this approach, treatment planning ignores the details of individual plans and instead presents the physician with clinical summaries of sets of solutions to well-defined clinical goals in which every solution has been optimized in advance by computer algorithms.Methods and Materials: Before interactive planning, sets of optimized plans are created for a variety of treatment delivery options and critical structure dose-volume constraints. Then, the dose-volume parameters of the optimized plans are fit to linear functions. These linear functions are used to show in real time how the target dose-volume histogram (DVH) changes as the DVHs of the critical structures are changed interactively. A bitmap of the space of optimized plans is used to restrict the feasible solutions. The physician selects the critical structure dose-volume constraints that give the desired dose to the planning target volume (PTV) and then those constraints are used to create the corresponding optimized plan.Results: The method is demonstrated using prototype software, Treatment Plan Explorer (TPEx), and a clinical example of a patient with a tumor in the right lung. For this example, the delivery options included 4 open beams, 12 open beams, 4 wedged beams, and 12 wedged beams. Beam directions and relative weights were optimized for a range of critical structure dose-volume constraints for the lungs and esophagus. Cord dose was restricted to 45 Gy. Using the interactive interface, the physician explored how the tumor dose changed as critical structure dose-volume constraints were tightened or relaxed and selected the best compromise for each delivery option. The corresponding treatment plans were calculated and compared with the linear parameterization presented to the physician in TPEx. The linear fits were best for the maximum PTV dose and worst for the minimum PTV dose. Based on the root-mean-square error between the fit values and their corresponding data values, the linear fit appears to be adequate, although higher order polynomials could give better results. Some of the variance in fit is due to the stochastic nature of the simulated annealing optimization algorithm, which does not reproduce the exact same results in repetitions of the same calculation. Using a directed search algorithm for plan optimization should produce better parameter fits and, therefore, better predictions of plan characteristics by TPEx.Conclusions: Using TPEx, the physician can easily select the optimum plan for a patient, with no imposed arbitrary definition of the "best" plan. More importantly, the physician can readily see what can be achieved for the patient with a given delivery technique. There is no more uncertainty about whether or not a better plan exists. By comparing the "best" plans for different delivery options (e.g., three-dimensional conformal radiotherapy versus intensity-modulated radiation therapy), the physician can gauge the clinical benefits of greater technical complexity. However, before the TPEx process can be clinical useful, faster computers and/or algorithms are needed and more studies are needed to better model the spaces of optimized solutions. (C) 2005 Elsevier Inc.