[Research of artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma]

Objective: To apply artificial intelligence technology in clinical real-world data of patients with primary hepatocellular carcinoma, explore the precise treatment of disease and build up artificial intelligence-based clinical decision support system. Methods: A total of 5 642 patients with primary hepatocellular carcinoma admitted to West China Hospital from July 2004 to June 2016 with complete follow-up records were included in the study. A merged model composed of multiple sub-classifiers was adopted to calculate therapy recommendation coefficient, and receiver operator characteristic curve was analyzed. Survival risk and recurrence risk were predicted by DeepSurv algorithm, and Kaplan-Meier survival curves were further compared among low, middle and high risk groups. Siamese-Net was applied to find similar patients. Results: The Top-1 and Top-2 accuracy of therapy recommendation coefficient reached 82.36% and 94.13% respectively. In internal verification of West China Hospital, the above-mentioned value reached 95.10% in accordance with multi-disciplinary team results. The C-index derived from survival risk model was 0.735 (95%CI:0.70-0.77), and the difference of Kaplan-Meier in pairwise comparison was of statistical significance under log-rank test (P<0.001). Meanwhile, the C-index derived from recurrence risk model was 0.705 (95%CI:0.68-0.73), and the difference of Kaplan-Meier in pairwise comparison was of statistical significance under log-rank test (P<0.001). Conclusions: The artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma has can accurately make therapy recommendation and prognosis prediction for primary hepatocellular carcinoma.