A feasibility study on deep learning-based individualized 3D dose distribution prediction.
A feasibility study on deep learning-based individualized 3D dose distribution prediction.
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基于深度学习的个性化3D剂量分布预测的可行性研究。
DOI：
10.1002/mp.15025
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发表时间：
2021-08
期刊：
影响因子：
3.8
通讯作者：
Jiang S
中科院分区：
文献类型：
--
作者：
Ma J;Nguyen D;Bai T;Folkerts M;Jia X;Lu W;Zhou L;Jiang S
放射治疗的治疗计划是一个反复试验的过程，通常很耗时。通过使用预训练的深度学习（DL）模型，可以预测与特定患者解剖结构相对应的近似最佳剂量分布。然而，剂量分布的优化往往不仅基于患者特定的解剖结构，而且还基于医生在计划靶体积（PTV）覆盖和保留危险器官（OAR）之间或不同OAR之间的首选权衡。因此，希望允许医生根据患者解剖结构对预测的剂量分布进行微调。在这项工作中，我们开发了一个DL模型来预测个体化的3D剂量分布，不仅使用患者的解剖结构，而且还使用所需的PTV/OAR权衡，如剂量体积直方图（DVH）所示，作为输入。在这项工作中，我们开发了一种改进的U-Net网络，通过使用患者PTV/OAR面罩和所需的DVH作为输入来预测3D剂量分布。医生根据最初预测的DVH对所需的DVH进行微调，首先将其投影到帕累托曲面上，然后转换为矢量，然后与PTV/OAR掩码编码的特征图相连接。用于训练的网络输出是Pareto最优DVH对应的剂量分布。训练/验证数据集包含77名前列腺癌患者，测试数据集包含20名患者。训练后的模型可以预测三维剂量分布，该分布近似于帕累托最优，同时DVH最接近输入所需的DVH。我们计算了预测剂量分布与最佳剂量分布之间的差值，其中最佳剂量分布的DVH最接近PTV和所有桨的期望剂量分布，作为定量评估。平均剂量的最大平均误差约为处方剂量的1.6%，最大剂量的最大平均误差约为处方剂量的1.8%。在这项可行性研究中，我们开发了一个3D U-Net模型，以患者的解剖结构和所需的DVH曲线作为输入，以预测个性化的3D剂量分布，该剂量分布近似于帕累托最优，同时DVH最接近所需的。预测的剂量分布可作为剂量测定师和医生快速制定临床可接受的治疗方案的参考。
Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient’s anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians’ preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient’s anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs. In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients. The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest average error in mean dose was about 1.6% of the prescription dose, and the largest average error in the maximum dose was about 1.8% of the prescription dose. In this feasibility study, we have developed a 3D U-Net model with the patient’s anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.
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