Department of Medical Physics, Al-Neelain University, Khartoum, Sudan.
Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
J Appl Clin Med Phys. 2023 Sep;24(9):e14015. doi: 10.1002/acm2.14015. Epub 2023 May 3.
In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics.
A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices.
The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for the and indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net.
All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.
本文使用深度学习比较了四种新的基于知识的规划（KBP）算法，以使用相同患者数据集和定量评估指标来预测头颈部计划的三维（3D）剂量分布。
本研究使用了 340 名接受调强放射治疗的口咽癌患者的数据集，该数据集代表 AAPM OpenKBP-2020 挑战赛数据集。构建了四个 3D 卷积神经网络架构。该模型在数据集的 64%上进行训练，并在 16%上进行验证，以进行体素剂量预测：U-Net、注意力 U-Net、残差 U-Net（Res U-Net）和注意力 Res U-Net。然后，通过将预测的剂量分布与ground-truth 进行比较，使用剂量统计量和剂量体积指数评估在测试数据集（数据集的 20%）上的模型性能。
在测试集中，68 个计划中，四个 KBP 剂量预测模型的表现均很有前景，体内轮廓的平均绝对剂量误差均<3Gy。预测所有靶区 D 指数的平均差异分别为注意力 Res U-Net 为 0.92Gy（p=0.51），Res U-Net 为 0.94Gy（p=0.40），注意力 U-Net 为 2.94Gy（p=0.09），U-Net 为 3.51Gy（p=0.08）。对于 OARs， 指数和 指数的值分别为注意力 Res U-Net 为 2.72Gy（p<0.01），Res U-Net 为 2.94Gy（p<0.01），注意力 U-Net 为 1.10Gy（p<0.01），U-Net 为 0.84Gy（p<0.29）。
所有模型在体素剂量预测方面表现几乎相当。基于 3D U-Net 架构的 KBP 模型可以投入临床使用，通过创建具有一致质量的计划和提高放射治疗工作流程的效率来改善癌症患者的治疗。
