Deep learning-based multi-omics model to predict nasopharyngeal necrosis of re-irradiation for recurrent nasopharyngeal carcinoma.

Background and purpose: 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. Materials and methods: 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. Results: 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. Conclusion: 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.