Predicting dose-volume histograms for organs-at-risk in IMRT planning
Predicting dose-volume histograms for organs-at-risk in IMRT planning
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DOI：
10.1118/1.4761864
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发表时间：
2012-12-01
期刊：
影响因子：
3.8
通讯作者：
Moore, Kevin L.
中科院分区：
文献类型：
--
作者：
Appenzoller, Lindsey M.;Michalski, Jeff M.;Moore, Kevin L.
目的：这项工作的目的是开发一种质量控制（QC）工具，以减少调强放射治疗（IMRT）计划的可变性，并使用数学模型来提高治疗计划的质量，该模型可以根据个体患者的解剖结构预测可实现的危及器官（OAR）剂量体积直方图（DVH）。一个数学框架来预测可实现的OAR DVH的相关性的基础上，预期剂量的最小距离从体素到PTV表面。共享一系列最小距离的OAR体素被计算为子体积。一个三参数，偏态概率分布被用来拟合子体积剂量分布，和DVH预测模型通过拟合的偏态参数的演变与多项式的距离的函数。具有相同临床目标的20个前列腺和24个头颈部IMRT计划的队列用于训练直肠、膀胱和腮腺的器官特异性平均模型。定量临床批准的DVH和预测的DVH之间的综合差异的残差总和分析评价了DVH之间的相似性。在20个前列腺计划的独立验证队列中评价了平均模型前瞻性预测DVH的能力。训练和验证队列之间残差总和的统计比较量化了平均模型的准确性。使用限制残差和（RSR）识别潜在离群值，其中RSR的大值表明临床DVH超过预测DVH相当大的量。通过从训练队列中排除具有大RSR值的离群值，获得每个器官的精细模型。将改进的模型应用于原始训练队列，并利用有限的残差和来估计潜在的DVH改善。所有病例均由批准原始计划的医生重新计划和评价。使用原始和重新计划的DVH之间的残差和来量化在重新计划下实现的剂量学增益，从而评估改进模型正确识别离群值的能力。直肠（（SR）相对于条形图（直肠）= 0.003 +/-0.037）、膀胱（（SR）相对于条形图（膀胱）=-0.008 +/-0.037）、和腮腺（（SR）对条（腮腺）=-0.003 +/-0.060）训练组群产生接近零的平均值，并且相对于标准偏差较小，表明平均模型捕获训练组群的基本行为。平均直肠和膀胱模型的预测能力在训练集和验证集之间在统计学上无法区分，对于验证集，（SR）相对于条（直肠）= 0.002 +/-0.044，（SR）相对于条（膀胱）=-0.018 +/-0.058。改进模型检测离群值和预测可实现OAR DVH的能力通过预测增益（RSR）和重新规划后实现增益之间的强相关性得到证明，直肠样本相关系数r = 0.92，膀胱样本相关系数r = 0.88，腮腺样本相关系数r = 0.84。结果表明，我们的数学框架和适度的训练队列成功地预测了可实现的OAR DVH的基础上，个别患者的解剖结构。该模型正确地识别了次优计划，证明了重新规划后进一步节省OAR。这种建模技术不需要人工干预，除了适当选择具有相同评估标准的训练集。临床实施正在进行中，以评价对实时IMRT QC的影响。(C)2012年美国医学物理学家协会。[http：//dx.doi.org/10.1118/1.4761864]
Purpose: The objective of this work was to develop a quality control (QC) tool to reduce intensity modulated radiotherapy (IMRT) planning variability and improve treatment plan quality using mathematical models that predict achievable organ-at-risk (OAR) dose-volume histograms (DVHs) based on individual patient anatomy.Methods: A mathematical framework to predict achievable OAR DVHs was derived based on the correlation of expected dose to the minimum distance from a voxel to the PTV surface. OAR voxels sharing a range of minimum distances were computed as subvolumes. A three-parameter, skew-normal probability distribution was used to fit subvolume dose distributions, and DVH prediction models were developed by fitting the evolution of the skew-normal parameters as a function of distance with polynomials. Cohorts of 20 prostate and 24 head-and-neck IMRT plans with identical clinical objectives were used to train organ-specific average models for rectum, bladder, and parotids. A sum of residuals analysis quantifying the integrated difference between the clinically approved DVH and predicted DVH evaluated similarity between DVHs. The ability of the average models to prospectively predict DVHs was evaluated on an independent validation cohort of 20 prostate plans. Statistical comparison of the sums of residuals between training and validation cohorts quantified the accuracy of the average model. Restricted sums of residuals (RSR) were used to identify potential outliers, where large values of RSR indicate a clinical DVH that exceeds the predicted DVH by a considerable amount. A refined model was obtained for each organ by excluding outliers with large RSR values from the training cohort. The refined model was applied to the original training cohort and restricted sums of residuals were utilized to estimate potential DVH improvements. All cases were replanned and evaluated by the physician that approved the original plan. The ability of the refined models to correctly identify outliers was assessed using the residual sum between the original and replanned DVHs to quantify dosimetric gains realized under replanning.Results: Statistical analysis of average sum of residuals for rectum ((SR) over bar (rectum)=0.003 +/- 0.037), bladder ((SR) over bar (bladder)=-0.008 +/- 0.037), and parotid ((SR) over bar (parotid)=-0.003 +/- 0.060) training cohorts yielded mean values near zero and small with respect to the standard deviations, indicating that the average models are capturing the essential behavior of the training cohorts. The predictive abilities of the average rectum and bladder models were statistically indistinguishable between the training and validation sets, with (SR) over bar (rectum)=0.002 +/- 0.044 and (SR) over bar (bladder)=-0.018 +/- 0.058 for the validation set. The refined models' ability to detect outliers and predict achievable OAR DVHs was demonstrated by a strong correlation between predicted gains (RSR) and realized gains after replanning with sample correlation coefficients of r = 0.92 for the rectum, r = 0.88 for the bladder, and r = 0.84 for the parotid glands.Conclusions: The results demonstrate that our mathematical framework and modest training cohorts successfully predict achievable OAR DVHs based on individual patient anatomy. The models correctly identified suboptimal plans that demonstrated further OAR sparing after replanning. This modeling technique requires no manual intervention except for appropriate selection of a training set with identical evaluation criteria. Clinical implementation is in progress to evaluate impact on real-time IMRT QC. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4761864]