Alpuche Aviles Jorge Edmundo, Cordero Marcos Maria Isabel, Sasaki David, Sutherland Keith, Kane Bill, Kuusela Esa
CancerCare Manitoba, 675 McDermot Ave., Winnipeg, MB, R3E 0V9, Canada.
University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.
J Appl Clin Med Phys. 2018 May;19(3):215-226. doi: 10.1002/acm2.12322. Epub 2018 Apr 6.
Knowledge-based planning (KBP) can be used to estimate dose-volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not properly addressed. This work used RapidPlan to create models for the prostate and head and neck intended for large-scale distribution. Potential outlier plans were identified by means of regression analysis scatter plots, Cook's distance, coefficient of determination, and the chi-squared test. Outlier plans were identified as falling into three categories: geometric, dosimetric, and over-fitting outliers. The models were validated by comparing DVHs estimated by the model with those from a separate and independent set of clinical plans. The estimated DVHs were also used as optimization objectives during inverse planning. The analysis tools lead us to identify as many as 7 geometric, 8 dosimetric, and 20 over-fitting outliers in the raw models. Geometric and over-fitting outliers were removed while the dosimetric outliers were replaced after re-planning. Model validation was done by comparing the DVHs at 50%, 85%, and 99% of the maximum dose for each OAR (denoted as V50, V85, and V99) and agreed within -2% to 4% for the three metrics for the final prostate model. In terms of the head and neck model, the estimated DVHs agreed from -2.0% to 5.1% at V50, 0.1% to 7.1% at V85, and 0.1% to 7.6% at V99. The process used to create these models improved the accuracy for the pharyngeal constrictor DVH estimation where one plan was originally over-estimated by more than twice. In conclusion, our results demonstrate that KBP models should be carefully created since their accuracy could be negatively affected by outlier plans. Outlier plans can be addressed by removing them from the model and re-planning.
基于知识的计划（KBP）可用于使用模型估计危及器官（OAR）的剂量体积直方图（DVH）。然而，模型创建任务可能会导致准确性不同的估计；特别是当异常计划未得到妥善处理时。这项工作使用RapidPlan为前列腺和头颈部创建模型，以供大规模分发。通过回归分析散点图、库克距离、决定系数和卡方检验来识别潜在的异常计划。异常计划被分为三类：几何异常、剂量异常和过拟合异常。通过将模型估计的DVH与另一组独立的临床计划的DVH进行比较来验证模型。估计的DVH也被用作逆向计划期间的优化目标。分析工具使我们在原始模型中识别出多达7个几何异常、8个剂量异常和20个过拟合异常。去除几何异常和过拟合异常，同时在重新计划后替换剂量异常。通过比较每个OAR最大剂量的50%、85%和99%处的DVH（分别表示为V50、V85和V99）进行模型验证，最终前列腺模型的这三个指标在-2%至4%的范围内一致。对于头颈部模型，估计的DVH在V50处的一致性为-2.0%至5.1%，在V85处为0.1%至7.1%，在V99处为0.1%至7.6%。创建这些模型的过程提高了咽缩肌DVH估计的准确性，其中一个计划最初被高估了两倍多。总之，我们的结果表明，KBP模型应谨慎创建，因为其准确性可能会受到异常计划的负面影响。可以通过从模型中删除异常计划并重新计划来处理异常计划。
