NPC-SurvAI: A fully automated deep learning framework for prognostic prediction and risk stratification in patients with nasopharyngeal carcinoma.

Abstract

Deep learning can non-invasively depict the radiological phenotype of tumor. We aimed to propose an end-to-end deep learning framework called NPC-SurvAI to perform prognosis assessment using MRI in nasopharyngeal carcinoma (NPC). This retrospective study included 2180 NPC patients who underwent baseline MRI. The NPC-SurvAI comprised an AttVNet for image segmentation and a DenseNet-ICAM for prognosis evaluation, including progression-free survival (PFS) and overall survival (OS). The clinical model was built with age, T-stage, N-stage, and EBV DNA. The image and combined models were developed by the NPC-SurvAI framework. The integrated area under the curve (iAUC) and thetime-dependent AUC (tAUC) were leveraged to measure the predictive accuracy. K-means clustering and Kaplan-Meier survival analysis were utilized to stratify patients into subtypes and compare their prognoses. In the validation cohorts, the AttVNet achieved average Dice similarity coefficients of 0.726-0.764 tumor segmentation. The dynamic change curves of the AUCs over time suggested that the combined model outperformed both the clinical and image models in predicting PFS (iAUC: 0.838-0.884 vs 0.788-0.844 vs 0.738-0.798) and OS (iAUC: 0.842-0.894 vs 0.793-0.853 vs 0.754-0.807) at any time point from 1 to 8 years. Specially, the combined model achieved time-AUCs of 0.844-0.930 for 3-year PFS and 0.827-0.896 for 5-year PFS; 0.838-0.978 for 3-year OS and 0.788-0.871 for 5-year OS. Additionally, patients could be stratified into two subtypes with different survivals (all P < 0.05). NPC-SurvAI has the potential to automatically stratify patients with diverse prognoses, which helps clinicians in optimizing treatment decisions and surveillance.