Zhang Huai-Wen, Wang You-Hua, Hu Bo, Pang Hao-Wen
Department of Radiotherapy, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi Province, China.
Department of Oncology, Gulin People's Hospital, Luzhou 646500, Sichuan Province, China.
World J Gastrointest Oncol. 2024 Oct 15;16(10):4146-4156. doi: 10.4251/wjgo.v16.i10.4146.
The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.
To predict the uninvolved liver dose in stereotactic body radiotherapy (SBRT) for liver cancer using a neural network-based method.
A total of 114 SBRT plans for liver cancer were used to test the neural network method. Sub-organs of the uninvolved liver were automatically generated. Correlations between the volume of each sub-organ, uninvolved liver dose, and neural network prediction model were established using MATLAB. Of the cases, 70% were selected as the training set, 15% as the validation set, and 15% as the test set. The regression -value and mean square error (MSE) were used to evaluate the model.
The volume of the uninvolved liver was related to the volume of the corresponding sub-organs. For all sets of -values of the prediction model, except for D which was 0.7513, all -values of D-D and D were > 0.8. The MSE of the prediction model was also low.
We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer. It is simple and easy to use and warrants further promotion and application.
放射治疗计划的质量通常取决于计划设计者的知识和专业技能。
使用基于神经网络的方法预测肝癌立体定向体部放射治疗（SBRT）中未受照射肝脏的剂量。
共使用114个肝癌SBRT计划来测试神经网络方法。自动生成未受照射肝脏的亚器官。使用MATLAB建立每个亚器官体积、未受照射肝脏剂量与神经网络预测模型之间的相关性。在这些病例中，70%被选为训练集，15%为验证集，15%为测试集。使用回归值和均方误差（MSE）评估模型。
未受照射肝脏的体积与相应亚器官的体积相关。对于预测模型的所有 - 值集，除D为0.7513外，D - D和D的所有 - 值均>0.8。预测模型的MSE也较低。
我们开发了一种基于神经网络的方法来预测肝癌SBRT中未受照射肝脏的剂量。该方法简单易用，值得进一步推广应用。
