Hien Lam Thanh, Hieu Pham Trung, Toan Do Nang
Faculty of Information Technology, Lac Hong University, Huynh Van Nghe, Bien Hoa 76120, Vietnam.
Institute of Information Technology, Vietnam Academy of Science and Technology, Hoang Quoc Viet, Hanoi 10072, Vietnam.
Diagnostics (Basel). 2025 Jan 14;15(2):177. doi: 10.3390/diagnostics15020177.
: Cancer is a highly lethal disease with a significantly high mortality rate. One of the most commonly used methods for treatment is radiation therapy. However, cancer treatment using radiotherapy is a time-consuming process that requires significant manual work from planners and doctors. In radiation therapy treatment planning, determining the dose distribution for each of the regions of the patient's body is one of the most difficult and important tasks. Nowadays, artificial intelligence has shown promising results in improving the quality of disease treatment, particularly in cancer radiation therapy. : The main objective of this study is to build a high-performance deep learning model for predicting radiation therapy doses for cancer and to develop software to easily manipulate and use this model. : In this paper, we propose a custom 3D convolutional neural network model with a U-Net-based architecture to automatically predict radiation doses during cancer radiation therapy from CT images. To ensure that the predicted doses do not have negative values, which are not valid for radiation doses, a rectified linear unit (ReLU) function is applied to the output to convert negative values to zero. Additionally, a proposed loss function based on a dose-volume histogram is used to train the model, ensuring that the predicted dose concentrations are highly meaningful in terms of radiation therapy. The model is developed using the OpenKBP challenge dataset, which consists of 200, 100, and 40 head and neck cancer patients for training, testing, and validation, respectively. Before the training phase, preprocessing and augmentation techniques, such as standardization, translation, and flipping, are applied to the training set. During the training phase, a cosine annealing scheduler is applied to update the learning rate. : Our model achieved strong performance, with a good DVH score (1.444 Gy) on the test dataset, compared to previous studies and state-of-the-art models. In addition, we developed software to display the dose maps predicted by the proposed model for each 2D slice in order to facilitate usage and observation. These results may help doctors in treating cancer with radiation therapy in terms of both time and effectiveness.
癌症是一种致死率很高的疾病，死亡率显著偏高。最常用的治疗方法之一是放射治疗。然而，使用放射疗法进行癌症治疗是一个耗时的过程，需要规划师和医生进行大量的人工操作。在放射治疗计划中，确定患者身体各部位的剂量分布是最困难且重要的任务之一。如今，人工智能在改善疾病治疗质量方面已显示出令人鼓舞的成果，尤其是在癌症放射治疗中。
本研究的主要目标是构建一个高性能的深度学习模型，用于预测癌症放射治疗剂量，并开发软件以方便操作和使用该模型。
在本文中，我们提出了一种基于U-Net架构的定制3D卷积神经网络模型，用于从CT图像自动预测癌症放射治疗期间的放射剂量。为确保预测剂量不出现对放射剂量无效的负值，对输出应用修正线性单元（ReLU）函数将负值转换为零。此外，使用基于剂量体积直方图提出的损失函数来训练模型，确保预测的剂量浓度在放射治疗方面具有高度意义。该模型使用OpenKBP挑战数据集开发，该数据集分别由200、100和40例头颈部癌患者组成，用于训练、测试和验证。在训练阶段之前，对训练集应用预处理和增强技术，如标准化、平移和翻转。在训练阶段，应用余弦退火调度器来更新学习率。
与先前的研究和最先进的模型相比，我们的模型表现出色，在测试数据集上具有良好的剂量体积直方图（DVH）分数（1.444戈瑞）。此外，我们开发了软件来显示所提出模型为每个2D切片预测的剂量图，以便于使用和观察。这些结果在时间和有效性方面可能有助于医生进行癌症放射治疗。
