A novel and individualized robust optimization method using normalized dose interval volume constraints (NDIVC) for intensity-modulated proton radiotherapy.
A novel and individualized robust optimization method using normalized dose interval volume constraints (NDIVC) for intensity-modulated proton radiotherapy.
复制标题
一种新颖的个性化鲁棒优化方法，使用归一化剂量间隔体积约束（NDIVC）进行调强质子放射治疗。
DOI：
10.1002/mp.13276
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发表时间：
2019
期刊：
影响因子：
3.8
通讯作者：
Liu,Wei
中科院分区：
文献类型：
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作者：
Shan,Jie;Sio,TerenceT;Liu,Chenbin;Schild,StevenE;Bues,Martin;Liu,Wei
目的：已知强度调制质子治疗（IMPT）对患者设置和范围不确定性问题敏感。为了减轻这些不确定性的影响，已经开发了多种鲁棒优化方法。在这里，我们提出了一种新的鲁棒优化方法，它为IMPT提供了一种鲁棒优化的替代方法，并且具有临床实用性，它将使用户能够以用户定义的方式控制标称计划质量和计划鲁棒性之间的平衡。方法计算了由患者设置和质子束距离不确定性引起的9种典型剂量分布和8种极端剂量分布。对于每个体素，定义归一化剂量间隔（NDI）为全剂量范围变化除以所有不确定情景下的最大剂量（NDI = [max - min剂量]/max剂量），然后计算归一化剂量间隔体积直方图（NDIVH）曲线。NDIVH曲线下的面积用来量化计划的稳健性。应用于靶标的归一化剂量间隔体积约束（NDIVC）来指定所需的稳健性，这是用户自定义的。然后，用户可以通过自由调整ndivc在NDIVH曲线上的位置来探索标称计划质量和计划鲁棒性之间的权衡。我们使用一个肺、五个头颈部（H&N）和三个前列腺病例对我们的方法进行基准测试，并将我们的结果与使用基于体素的最坏情况鲁棒优化得出的结果进行比较。结果使用基准案例，我们的新方法在名义计划质量和一般计划鲁棒性方面获得了与基于体素的最坏情况鲁棒优化所获得的质量相当的IMPT计划；在某些情况下，如果采用适当的NDIVCs，靶剂量分布甚至会更加适形和均匀。NDIVH下的AUC作为平面鲁棒性的精确定量指标，与DVH带宽一致。此外，我们证明了调整ndivc在NDIVH曲线中的位置的可行性，这允许用户探索标称计划质量和计划鲁棒性之间的权衡。结论基于NDIVH的鲁棒优化方法为IMPT提供了一种新颖的个性化鲁棒优化方法，使用户能够以用户自定义的方式调整标称计划质量和计划鲁棒性之间的平衡。该方法适用于未来不断改进和开发下一代IMPT规划算法。
PurposeIntensity‐modulated proton therapy (IMPT) is known to be sensitive to patient setup and range uncertainty issues. Multiple robust optimization methods have been developed to mitigate the impact of these uncertainties. Here, we propose a new robust optimization method, which provides an alternative way of robust optimization in IMPT, and is clinically practical, which will enable users to control the balance between nominal plan quality and plan robustness in a user‐defined fashion.MethodWe calculated nine individual dose distributions which corresponded to one nominal and eight extreme scenarios caused by patient setup and proton beam's range uncertainties. For each voxel, the normalized dose interval (NDI) is defined as the full dose range variation divided by the maximum dose in all uncertainty scenarios (NDI = [max – min dose]/max dose), which was then used to calculate the normalized dose interval volume histogram (NDIVH) curves. The areas under the NDIVH curves were used to quantify plan robustness. A normalized dose interval volume constraint (NDIVC) applied to the target was incorporated to specify the desired robustness which was user‐defined. Users could then explore the trade‐off between nominal plan quality and plan robustness by adjusting the position of the NDIVCs on the NDIVH curves freely. We benchmarked our method using one lung, five head and neck (H&N), and three prostate cases by comparing our results to those derived using the voxel‐wise worst‐case robust optimization.ResultsUsing the benchmark cases, our new method achieved quality IMPT plans comparable to those derived from the voxel‐wise worst‐case robust optimization for both nominal plan quality and plan robustness in general; even more conformal and more homogeneous target dose distributions in some cases, if proper NDIVCs were applied. The AUC under NDIVH, as a precise quantitative index of plan robustness, was consistent with DVH bandwidths. Additionally, we demonstrated the feasibility of adjusting the position of NDIVCs in the NDIVH curves which allowed users to explore the trade‐off between nominal plan quality and plan robustness.ConclusionsThe NDIVH‐based robust optimization method provided a novel and individualized way of robust optimization in IMPT, and enables users to adjust the balance between nominal plan quality and plan robustness in a user‐defined fashion. This method is applicable for continued improvement and developing the next generation of IMPT planning algorithms in the future.