Prediction of early recurrence of hepatocellular carcinoma within the Milan criteria after radical resection

Abstract

Approximately 50% hepatocellular carcinoma patients meeting the Milan criteria utilized to develop an improved prognostic model for predicting the recurrence in these patients. Using univariate and multivariate analysis, cytokeratin-19 and glypican-3 expression patterns, tumor number and histological grading from eight putative prognostic factors comprised the risk factor scoring model to predict the tumor recurrence. In the training cohort, the area under roc curve (AUC) value of the model was 0.715 [95% confidence interval (CI) = 0.645-0.786, P < 0.001], which was the highest among all the parameters. The performance of the model was assessed using an independent validation cohort, wherein the AUC value was 0.760 (95% CI=0.647-0.874, P < 0.001), which was higher than the other factors. The results indicated that model had high performance with adequate discrimination ability. Moreover, it significantly improved the predictive capacity for the recurrence in patients with hepatocellular carcinoma within the Milan criteria after radical resection.