School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Med Phys. 2021 Aug;48(8):4438-4447. doi: 10.1002/mp.15025. Epub 2021 Jul 11.
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 absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% 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.
放射治疗计划是一个反复试验、耗时的过程。通过使用预先训练的深度学习（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，剂量分布的优化不仅基于患者特定的解剖结构，还基于医生在计划靶区（PTV）覆盖和危及器官（OAR）保护之间的首选权衡，或者在不同的 OAR 之间。因此，理想情况下，允许医生根据患者的解剖结构对基于解剖结构预测的剂量分布进行微调。在这项工作中，我们开发了一种 DL 模型，通过使用患者的解剖结构以及医生从最初预测的剂量体积直方图（DVH）中调整的 PTV/OAR 权衡作为输入，来预测个体化的 3D 剂量分布。
在这项工作中，我们开发了一种修改后的 U-Net 网络，通过使用患者的 PTV/OAR 掩模和期望的 DVH 作为输入来预测 3D 剂量分布。医生从最初预测的 DVH 中调整后的期望 DVH 首先被投影到帕累托面上，然后转换为向量，然后与从 PTV/OAR 掩模编码的特征图拼接。训练/验证数据集包含 77 例前列腺癌患者，测试数据集包含 20 例患者。
训练后的模型可以预测出接近帕累托最优的 3D 剂量分布，同时使 DVH 与输入的期望 DVH 最接近。我们计算了预测剂量分布与优化剂量分布之间的差异，优化剂量分布的 DVH 最接近期望的 PTV 和所有 OAR 的剂量分布。PTV 处平均剂量的最大绝对误差约为处方剂量的 3.6%，最大剂量的最大绝对误差约为处方剂量的 2.0%。
在这项可行性研究中，我们开发了一种 3D U-Net 模型，将患者的解剖结构和期望的 DVH 曲线作为输入，以预测接近帕累托最优的个体化 3D 剂量分布，同时使 DVH 最接近期望的剂量分布。预测的剂量分布可以作为剂量师和医生快速制定临床可接受的治疗计划的参考。
