Department of Radiotherapy, Jiang-xi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Nanchang, China; Department of Oncology, The Third People's Hospital of Jingdezhen, The third people's hospital of Jingdezhen affiliated to Nanchang Medical College, Jingdezhen, China.
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Sichuan, China.
Clin Oncol (R Coll Radiol). 2024 Jan;36(1):46-55. doi: 10.1016/j.clon.2023.11.031. Epub 2023 Nov 17.
A neural network method was used to establish a dose prediction model for organs at risk (OARs) during intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC).
In total, 103 patients with NPC were randomly selected for IMRT. Suborgans were automatically generated for OARs using ring structures based on distance to the target using a MATLAB program and the corresponding volume of each suborgan was determined. The correlation between the volume of each suborgan and the dose to each OAR was analysed and neural network prediction models of the OAR dose were established using the MATLAB Neural Net Fitting application. The R-value and mean square error in the regression analysis were used to evaluate the prediction model.
The OAR dose was related to the volume of the corresponding sub-OAR. The average R-values for the normalised mean dose (Dnmean) to parallel organs and serial organs and the normalised maximum dose (Dn0) to serial organs in the training set were 0.880, 0.927 and 0.905, respectively. The mean square error for each OAR in the prediction model was low (ranging from 1.72 × 10 to 7.06 × 10).
The neural network-based model for predicting OAR dose during IMRT for NPC is simple, reliable and worth further investigation and application.
利用神经网络方法建立鼻咽癌调强放疗中危及器官（OARs）的剂量预测模型。
共随机选择 103 例鼻咽癌患者进行调强放疗。使用基于距离靶区的环形结构，利用 MATLAB 程序自动生成 OAR 的子器官，并确定每个子器官的相应体积。分析每个子器官体积与每个 OAR 剂量之间的相关性，并使用 MATLAB 神经网络拟合应用程序建立 OAR 剂量的神经网络预测模型。回归分析中使用 R 值和均方误差评估预测模型。
OAR 剂量与相应的子 OAR 体积有关。在训练集中，平行器官和串行器官的归一化平均剂量（Dnmean）和串行器官的归一化最大剂量（Dn0）的平均 R 值分别为 0.880、0.927 和 0.905。预测模型中每个 OAR 的均方误差较低（范围为 1.72×10 至 7.06×10）。
基于神经网络的预测鼻咽癌调强放疗中 OAR 剂量的模型简单、可靠，值得进一步研究和应用。
