Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique
Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique
复制标题
基于深度学习技术预测三维剂量分布的自动治疗计划
DOI：
10.1002/mp.13271
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发表时间：
2019-01-01
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
目的开发一种用于外照射调强放射治疗（IMRT）的自动治疗计划策略，包括基于深度学习的三维（3D）剂量预测和基于剂量分布的计划生成算法。训练基于残差神经网络的深度学习模型以基于患者特定几何形状和处方剂量预测剂量分布。本研究共招募了270例头颈部癌症病例，包括训练数据集中的195例，验证数据集中的25例和测试数据集中的50例。所有患者均接受了各种不同处方模式的IMRT治疗。模型输入由CT图像和轮廓组成，描绘了风险器官（OAR）和计划靶体积（PTV）。训练算法输出以预测CT图像切片上的剂量分布。所获得的预测模型用于预测新患者的剂量分布。然后，基于这些预测的剂量分布创建优化目标函数以用于自动计划生成。结果我们的结果表明，深度学习方法可以预测临床可接受的剂量分布。除脑干、左右透镜外，所有临床相关剂量体积直方图（DVH）指标的预测值与真实的临床计划值之间均无统计学显著差异。然而，预测的计划在临床上仍然是可接受的。计划生成结果显示，除PTV70.4外，自动生成的计划与预测计划之间无统计学显著差异，但差异仅为0.5%，仍在临床上可接受。结论本研究开发了一种新的基于三维剂量预测和三维剂量分布优化的自动放射治疗计划系统。这是一个很有前途的方法，在未来实现自动治疗计划。
Purpose To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm. Methods and Materials A residual neural network-based deep learning model is trained to predict a dose distribution based on patient-specific geometry and prescription dose. A total of 270 head-and-neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation. Results Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose-volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV70.4, but the difference is only 0.5% which is still clinically acceptable. Conclusion This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution-based optimization. It is a promising approach for realizing automated treatment planning in the future.