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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 14 - 2024 |
doi: 10.3389/fonc.2024.1433225
Exploring the Impact of Network Depth on 3D U-Net-Based Dose Prediction for Cervical Cancer Radiotherapy
Provisionally accepted- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
Purpose: 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.Results: For planning target volume (PTV), the differences between predicted and true values of D98, D99, 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.
Keywords: 3D U-Net, dose prediction, Radiotherapy, Network depth, Cervical cancer
Received: 15 May 2024; Accepted: 26 Aug 2024.
Copyright: © 2024 Wang, Pan, Zhang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuxi Pan, Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
Ruijie Yang, Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
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