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Background: The Italian Liver Cancer (ITA.LI.CA) prognostic system for patients with hepatocellular carcinoma (HCC) has recently been proposed and validated. We sought to explore the relationship among the ITA.LI.CA prognostic variables (ie tumour stage, functional score based on performance status and Child-Pugh score, and alpha-fetoprotein), treatment selection and survival outcome in HCC patients. Patients and Methods: We analysed 4,867 consecutive HCC patients undergoing six main treatment strategies (liver transplantation, LT; liver resection, LR; ablation, ABL; intra-arterial therapy, IAT; Sorafenib, SOR; and best supportive care, BSC) and enrolled during 2002-2015 in a multicenter Italian database. In order to control pretreatment imbalances in observed variables, a machine learning methodology was used and inverse probability of treatment weights (IPTW) was calculated. An IPTW-adjusted multivariate survival model that included ITA.LI.CA prognostic variables, treatment period and treatment strategy was then developed. The survival benefit of HCC treatments was described as a hazard ratio (95% confidence interval), using BSC as a reference value and as predicted median survival. Results: After the IPTW, the six treatment groups became well balanced for most baseline characteristics. In the IPTW-adjusted multivariate survival model, treatment strategy was found to be the strongest survival predictor, irrespective of ITA.LI.CA prognostic variables and treatment period. The survival benefit of different therapies over BSC was: LT=0.19 (0.18-0.20); RES=0.40 (0.37-0.42); ABL 0.42 (0.40-0.44); IAT=0.58 (0.55-0.61); SOR=0.92 (0.87-0.97). This multivariate model was then used to predict median survival for each therapy within each ITA.LI.CA stage. Conclusion: The concept of therapeutic hierarchy was established within each ITA.LI.CA stage.