Gao Xingwang, Peng Yinglin, Lu Shanfu, An Yuhan, Chen Meining, Zhang Jun, Huang Runda, Wang Yiran, Qi Zhenyu, Lu Yao, Zhao Chong, Deng Xiaowu, Miao Jingjing, Liu Yimei
Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
Front Oncol. 2025 Jul 15;15:1607218. doi: 10.3389/fonc.2025.1607218. eCollection 2025.
Patients with recurrent nasopharyngeal carcinoma (rNPC) undergoing re-irradiation have a high risk of lethal nasopharyngeal necrosis (NN), which may lead to massive nasopharyngeal hemorrhage or death. Predicting NN is crucial to improve the prognosis of these patients. We aimed to utilize deep learning techniques in combination with multi-sequence magnetic resonance imaging (MRI) radiomics and dosiomics to predict the risk of nasopharyngeal necrosis in patients with recurrent nasopharyngeal carcinoma undergoing re-irradiation therapy.
117 patients with rNPC were included, comprising pre-treatment multi-sequence MR images (including T1, T1C, and T2 sequences) and a planned re-irradiation therapy dose distribution. A three-dimensional (3D) convolutional neural network (CNN) deep learning network model was utilized to integrate the selected MRI radiomics and dosiomics features. Eight prediction deep learning models were developed for training, 97 cases were used as the training set and 20 as the test set. The performance and prediction accuracy of each deep learning network model were then evaluated.
Thirty-two features correlated with necrosis of rNPC. The model based on multi-sequence MRI radiomics could better predict necrosis. The models combining radiomics and dosiomics features were more accurate for the prediction of NN, especially the model of multi-sequence MRI radiomics plus dosiomics, which showed the best performance in the test set, with an AUC, ACC, and F1-Score of 0.81, 0.75, and 0.74, respectively.
The deep learning model leveraging pre-treatment multi-sequence MRI radiomics and dosiomics of re-irradiation therapy can serve as a potential predictor of NN in patients with recurrent nasopharyngeal carcinoma, thereby improving clinical decision-making processes.
接受再程放疗的复发性鼻咽癌（rNPC）患者发生致命性鼻咽坏死（NN）的风险很高，这可能导致大量鼻咽出血或死亡。预测NN对于改善这些患者的预后至关重要。我们旨在利用深度学习技术结合多序列磁共振成像（MRI）影像组学和剂量学来预测接受再程放疗的复发性鼻咽癌患者发生鼻咽坏死的风险。
纳入117例rNPC患者，包括治疗前多序列MR图像（包括T1、T1C和T2序列）以及计划的再程放疗剂量分布。利用三维（3D）卷积神经网络（CNN）深度学习网络模型整合所选的MRI影像组学和剂量学特征。开发了8个预测深度学习模型用于训练，97例作为训练集，20例作为测试集。然后评估每个深度学习网络模型的性能和预测准确性。
32个特征与rNPC坏死相关。基于多序列MRI影像组学的模型能更好地预测坏死。结合影像组学和剂量学特征的模型对NN的预测更准确，尤其是多序列MRI影像组学加剂量学模型，在测试集中表现最佳，AUC、ACC和F1-Score分别为0.81、0.75和0.74。
利用再程放疗前多序列MRI影像组学和剂量学的深度学习模型可作为复发性鼻咽癌患者NN的潜在预测指标，从而改善临床决策过程。
