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Joint Multi-Task Learning for Survival Prediction of Gastric Cancer Patients using CT Images

Accurate pre-operative overall survival (OS) prediction of gastric patients is of great significance for personalized treatment. To facilitate improvement of survival prediction, we propose a novel joint multi-task network equipped with multilevel features simultaneously predicting clinical tumor and node stages. Two independent datasets including a training set (377 patients) and a test set (122 patients) are used to evaluate our proposed network. The results indicated that the multi-task network exploits its recipe by capturing multi-level features, and sharing prognostic information from correlated tasks of clinical stages prediction, which enable our network to predict OS accurately. Our method outperforms the existing methods with the highest c-index (training: 0.73; test: 0.72). Meanwhile, our method shows better prognostic value with the highest hazard ratio (training: 3.77; test: 4.28) for dividing patients into high- and low-risk groups.