The appropriate method of hepatectomy for hepatocellular carcinoma within University of California San Francisco (UCSF) criteria through neural network analysis

Abstract

BackgroundThis study aimed to find effective treatments for the patient within UCSF criteria. MethodsThis study enrolled 1006 patients meeting UCSF criteria, undergoing hepatic resection (HR), divided into two groups: single tumor group and multiple tumors group. We compared and analyzed the risk factors between these two groups’ long-term outcomes, through log-rank test, cox proportional hazards model and using neural network analysis to identify the independent risk factors. ResultsThe 1-, 3-, and 5-year OS rates in single tumor were significantly higher than multiple tumors (95.0%, 73.2% and 52.3% versus 93.9%, 69.7% and 38.0%, respectively, p < 0.001). The 1-, 3- and 5-year RFS rates were 90.3%, 60.7%, and 40.1% in single tumor and 83.4%, 50.7% and 23.8% in multiple tumors, respectively (p < 0.001). And tumor type, anatomic resection and MVI were the independent risk factors for the patient within UCSF criteria. MVI was the most important risk factor affecting OS and RFS rates in neural network analysis. The method of hepatic resection and the number of tumors were also affected OS and RFS rates. ConclusionAnatomic resections should be applied to patients within UCSF criteria, especially for patients with single MVI negative tumours.