McIntosh Chris, Purdie Thomas G
Department of Medical Imaging & Physics, Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, ON, Canada. The Techna Institute, UHN, Toronto, ON, Canada.
Phys Med Biol. 2017 Jan 21;62(2):415-431. doi: 10.1088/1361-6560/62/2/415. Epub 2016 Dec 20.
Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to be used in plan optimization. In this work we outline a multi-patient atlas-based dose prediction approach that learns to predict the dose-per-voxel for a novel patient directly from the computed tomography planning scan without the requirement of specifying any objectives. Our method learns to automatically select the most effective atlases for a novel patient, and then map the dose from those atlases onto the novel patient. We extend our previous work to include a conditional random field for the optimization of a joint distribution prior that matches the complementary goals of an accurately spatially distributed dose distribution while still adhering to the desired dose volume histograms. The resulting distribution can then be used for inverse-planning with a new spatial dose objective, or to create typical dose volume objectives for the canonical optimization pipeline. We investigated six treatment sites (633 patients for training and 113 patients for testing) and evaluated the mean absolute difference in all DVHs for the clinical and predicted dose distribution. The results on average are favorable in comparison to our previous approach (1.91 versus 2.57). Comparing our method with and without atlas-selection further validates that atlas-selection improved dose prediction on average in whole breast (0.64 versus 1.59), prostate (2.13 versus 4.07), and rectum (1.46 versus 3.29) while it is less important in breast cavity (0.79 versus 0.92) and lung (1.33 versus 1.27) for which there is high conformity and minimal dose shaping. In CNS brain, atlas-selection has the potential to be impactful (3.65 versus 5.09), but selecting the ideal atlas is the most challenging.
使放射治疗计划流程自动化是一个技术难题。大多数自动化方法都集中在定制和推断用于计划优化的剂量体积目标上。在这项工作中，我们概述了一种基于多患者图谱的剂量预测方法，该方法能够直接从计算机断层扫描计划扫描中学习预测新患者的每体素剂量，而无需指定任何目标。我们的方法学会自动为新患者选择最有效的图谱，然后将这些图谱中的剂量映射到新患者身上。我们扩展了之前的工作，纳入了一个条件随机场，用于优化联合分布先验，以匹配准确空间分布剂量分布的互补目标，同时仍符合所需的剂量体积直方图。然后，所得分布可用于具有新空间剂量目标的逆向规划，或为规范优化管道创建典型的剂量体积目标。我们研究了六个治疗部位（633名患者用于训练，113名患者用于测试），并评估了临床剂量分布和预测剂量分布在所有剂量体积直方图中的平均绝对差异。与我们之前的方法相比，平均结果更有利（分别为1.91和2.57）。比较有无图谱选择的方法进一步验证了，图谱选择在全乳（分别为0.64和1.59）、前列腺（分别为2.13和4.07）和直肠（分别为1.46和3.29）中平均改善了剂量预测，而在乳腺腔（分别为0.79和0.92）和肺（分别为1.33和1.27）中，由于其高一致性和最小剂量塑形，图谱选择的重要性较低。在中枢神经系统脑肿瘤中，图谱选择有可能产生影响（分别为3.65和5.09），但选择理想的图谱是最具挑战性的。
