Wang Mingqing, Pan Yuxi, Zhang Xile, Yang Ruijie
Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, China.
Front Oncol. 2024 Sep 16;14:1433225. doi: 10.3389/fonc.2024.1433225. eCollection 2024.
The 3D U-Net deep neural network structure is widely employed for dose prediction in radiotherapy. However, the attention to the network depth and its impact on the accuracy and robustness of dose prediction remains inadequate.
92 cervical cancer patients who underwent Volumetric Modulated Arc Therapy (VMAT) are geometrically augmented to investigate the effects of network depth on dose prediction by training and testing three different 3D U-Net structures with depths of 3, 4, and 5.
For planning target volume (PTV), the differences between predicted and true values of D, D, and Homogeneity were statistically 1.00 ± 0.23, 0.32 ± 0.72, and -0.02 ± 0.02 for the model with a depth of 5, respectively. Compared to the other two models, these parameters were also better. For most of the organs at risk, the mean and maximum differences between the predicted values and the true values for the model with a depth of 5 were better than for the other two models.
The results reveal that the network model with a depth of 5 exhibits superior performance, albeit at the expense of the longest training time and maximum computational memory in the three models. A small server with two NVIDIA GeForce RTX 3090 GPUs with 24 G of memory was employed for this training. For the 3D U-Net model with a depth of more than 5 cannot be supported due to insufficient training memory, the 3D U-Net neural network with a depth of 5 is the commonly used and optimal choice for small servers.
3D U-Net深度神经网络结构在放射治疗剂量预测中被广泛应用。然而，对网络深度及其对剂量预测准确性和稳健性的影响关注仍显不足。
对92例行容积调强弧形放疗（VMAT）的宫颈癌患者进行几何增强，通过训练和测试三种深度分别为3、4和5的不同3D U-Net结构，研究网络深度对剂量预测的影响。
对于计划靶区（PTV），深度为5的模型预测值与真实值之间D、D和均匀性的差异在统计学上分别为1.00±0.23、0.32±0.72和 -0.02±0.02。与其他两个模型相比，这些参数也更好。对于大多数危及器官，深度为5的模型预测值与真实值之间的平均和最大差异优于其他两个模型。
结果表明，深度为5的网络模型表现出卓越的性能，尽管在这三个模型中训练时间最长且计算内存最大。本次训练使用了一台配备两块24G内存的NVIDIA GeForce RTX 3090 GPU的小型服务器。由于训练内存不足，无法支持深度超过5的3D U-Net模型，深度为5的3D U-Net神经网络是小型服务器常用的最佳选择。
