A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally- advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 LA-NPC patients (779 male, 260 female, mean age 44 [standard deviation: 11]) diagnosed between April 2009 and December 2015. A radiomics- clinical prognostic model (Model RC) was developed using pre-and post-IC MRI and other clinical factors using graph convolutional neural networks (GCN). The concordance index (C-index) was used to evaluate model performance in predicting disease-free survival (DFS). The survival benefits of concurrent chemoradiation therapy (CCRT) were analyzed in model-defined risk groups. Results The C-indexes of Model RC for predicting DFS were significantly higher than those of TNM staging in the internal (0.79 versus 0.53) and external (0.79 versus 0.62, both P < .001) testing cohorts. The 5-year DFS for the Model RC-defined low-risk group was significantly better than that of the high-risk group (90.6% versus 58.9%, P < .001). In high-risk patients, those who received CCRT had a higher 5-year DFS rate than those who did not (58.7% versus 28.6%, P = .03). There was no evidence of a difference in 5-year DFS rate in low-risk patients who did or did not receive CCRT (91.9% versus 81.3%, P = .19). Conclusion Serial MRI before and after IC can effectively predict survival in LA-NPC. The radiomics-clinical prognostic model developed using a GCN-based deep learning method showed good risk discrimination capabilities and may facilitate risk-adaptive therapy. ©RSNA, 2025.