Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method
Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method
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DOI：
10.1088/1361-6560/aa71f8
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发表时间：
2017-08-07
影响因子：
3.5
通讯作者：
Purdie, Thomas G.
中科院分区：
文献类型：
--
作者：
McIntosh, Chris;Welch, Mattea;Purdie, Thomas G.
最近在自动放射治疗计划方面的工作已经使用基于历史治疗计划的机器学习来直接从计划图像推断新患者的空间剂量分布。我们提出了一种基于图谱的概率方法，它使用一组自动选择的最相似的患者(图谱)来预测新患者的剂量。输出是空间剂量目标，它指定所需的剂量-体素，因此取代了指定和调整剂量-体积目标的需要。基于体素的剂量模拟优化然后将预测的剂量分布转换为完整的治疗计划，并使用折叠的锥形卷积剂量引擎进行剂量计算。在这项研究中，我们对接受调强放疗和VMAT治疗的右侧口咽头颈部患者的自动计划进行了研究。我们使用54名培训患者和12名独立测试患者的数据库，通过评估14个临床剂量评估标准，比较了我们的剂量预测管道的四个版本。我们的初步结果是有希望的，并表明自动化方法可以产生与临床相似的剂量分布。总体而言，与临床相比，自动化计划实现了目标覆盖评估标准的平均高0.6%的剂量，而评估的处于危险标准水平的器官的剂量平均低2.4%。根据构象数衡量，自动化计划和临床计划在高剂量符合性方面没有统计学意义上的差异。在测试的12名患者中，自动化计划实现了比临床更独特的9个标准，自动化计划在两个高风险目标覆盖标准的评估极限处获得了显着更高的剂量，在一个关键器官的最大剂量中获得了显着较低的剂量。这种新的剂量模拟剂量预测方法可以在12-13分钟内生成完整的治疗计划，而无需用户交互。这是一种很有前途的全自动治疗计划方法，可以很容易地应用于不同的治疗地点和方式。
Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired doseper-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.