Abstract: Objective To develop and validate a predictive model for evaluating the prognosis of patients with hepatocellular carcinoma (HCC) based on deep-learning algorithms, and to evaluate its value. Methods The SEER (Surveillance, Epidemiology and Results) database of the pathologically diagnosed HCC patients in the National Cancer Institute (USA) from January 2011 to December 2015 were selected as the training cohort to construct predictive models, and the HCC patients who were also pathologically diagnosed in the People's Hospital of Guigang City during the same period were selected as the external verification cohort to evaluate the model. The main predictions were 1-, 3-, and 5-year overall survival rates. The deep-learning algorithm DeepSurv, random survival forest (RFS), Cox proportional hazard regression were used to develop the models. C-index was used to evaluate the discrimination, the calibration curve was used to evaluate the calibration, and the log-rank test was used to evaluate the ability of risk stratification. Results A total of 9, 730 patients in the training cohort and 405 patients in the external verification cohort were finally included in the study. In the training cohort, the C-index of DeepSurv algorithm in 1-, 3-, and 5-year were 0.85 (95%CI: 0.80-0.90), 0.82 (95%CI: 0.77-0.89) and 0.80 (95%CI: 0.73-0.87), respectively; in the external validation cohort, they were 0.83 (95%CI: 0.78-0.87), 0.79 (95%CI: 0.74-0.83), 0.72 (95%CI: 0.67-0.77), respectively. The C-index and calibration of DeepSurv algorithm were better than those of RFS, Cox regression and TNM staging in both the training and external validation cohort (all P<0.05). The log-rank test showed that the DeepSurv algorithm had a good capability of risk stratification (P<0.001). Conclusions The predictive model developed based on the deep learning algorithms DeepSurv can effectively predict the mortality of HCC patients, and is superior to conventional algorithms and prognostic evaluation indicators.

Key words: Hepatocellular carcinoma, Prediction model, Deep learning algorithm, Machine learning, Random forest