Accurate preoperative prediction of overall survival (OS) risk of human cancers based on CT images is greatly significant for personalized treatment. Deep learning methods have been widely explored to improve automated prediction of OS risk. However, the accuracy of OS risk prediction has been limited by prior existing methods. To facilitate capturing survival-related information, we proposed a novel knowledge-guided multi-task network with tailored attention modules for OS risk prediction and prediction of clinical stages simultaneously. The network exploits useful information contained in multiple learning tasks to improve prediction of OS risk. Three multi-center datasets, including two gastric cancer datasets with 459 patients, and a public American lung cancer dataset with 422 patients, are used to evaluate our proposed network. The results show that our proposed network can boost its performance by capturing and sharing information from other predictions of clinical stages. Our method outperforms the state-of-the-art methods with the highest geometrical metric. Furthermore, our method shows better prognostic value with the highest hazard ratio for stratifying patients into high- and low-risk groups. Therefore, our proposed method may be exploited as a potential tool for the improvement of personalized treatment.
Keywords: Computed tomography (CT); Deep learning; Neural network; Overall survival.
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