NPC-SurvAI: A fully automated deep learning framework for prognostic prediction and risk stratification in patients
Jingjing You1, Hongru Ou2, Yongxin Zhang3
1Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China; Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands.
Background And Purpose:
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).
Methods:
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.
Results:
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).
Conclusions:
NPC-SurvAI has the potential to automatically stratify patients with diverse prognoses, which helps clinicians in optimizing treatment decisions and surveillance.
