Background: The ideal candidates for resection are patients with solitary hepatocellular carcinoma (HCC); however, postoperative recurrence rate remains high. We aimed to establish prognostic models to predict HCC recurrence based on readily accessible clinical parameters and multi-institutional databases.
Patients and Methods: A total of 485 patients undergoing curative resection for solitary HCC were recruited from two independent institutions and the Cancer Imaging Archive database. We randomly divided the patients into training (n=323) and validation cohorts (n=162). Two models were developed: one using pre-operative and one using pre- and post-operative parameters. Performance of the models was compared with staging systems.
Results: Using multivariable analysis, albumin-bilirubin grade, serum alpha-fetoprotein and tumor size were selected into the pre-operative model; albumin-bilirubin grade, serum alpha-fetoprotein, tumor size, microvascular invasion and cirrhosis were selected into the postoperative model. The two models exhibited better discriminative ability (concordance index: 0.673– 0.728) and lower prediction error (integrated Brier score: 0.169– 0.188) than currently used staging systems for predicting recurrence in both cohorts. Both models stratified patients into low- and high-risk subgroups of recurrence with distinct recurrence patterns.
Conclusion: The two models with corresponding user-friendly calculators are useful tools to predict recurrence before and after resection that may facilitate individualized management of solitary HCC.
Keywords: hepatocellular carcinoma, resection, recurrence, survival, modelling