Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy
Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy
复制标题
自动规划中的迭代数据集优化：乳腺癌和直肠癌放射治疗的实施
DOI：
10.1002/mp.12232
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发表时间：
2017-06-01
期刊：
影响因子：
3.8
通讯作者：
Hu, Weigang
中科院分区：
文献类型：
--
作者：
Fan, Jiawei;Wang, Jiazhou;Hu, Weigang
目的：目的：研究乳腺癌和直肠癌放射治疗计划的自动化方法，包括选择迭代优化的训练数据集、预测危及器官的剂量体积直方图（dose volume histogram，DVH）和自动生成临床可接受的治疗计划。迭代优化的训练数据集是通过迭代优化从接受放射治疗的左乳腺癌和直肠癌患者的40个治疗计划中选择的。一个二维核密度估计算法（记为两个参数KDE），其中包括两个预测功能的实现，以产生预测的DVH。最后，使用Pinnacle 3自动计划（AP）模块（版本9.10，Philips Medical Systems）重新计划10个额外的新左乳治疗计划，目标函数来自预测的DVH曲线。结果：通过结合迭代优化训练数据集方法和双参数KDE预测算法，我们提出的自动规划策略提高了DVH预测的准确性。自动生成的治疗计划，使用来自预测的DVH的剂量可以实现更好的剂量节省一些OARs，而不影响其他指标的计划quality.Conclusions：建议的新的自动化治疗计划解决方案可用于有效地评估和提高强度调制乳腺癌和直肠癌放射治疗的治疗计划的质量和一致性。(C)2017年美国医学物理学家协会
Purpose: To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy.Methods: The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for leftbreast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans.Results: By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality.Conclusions: The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. (C) 2017 American Association of Physicists in Medicine