Abstract
BACKGROUND: The heterogeneity of hepatocellular carcinoma (HCC) leads to the unsatisfying predictive performance of current staging systems. HCC patients with pathological tumor micronecrosis have an immunosuppressive microenvironment. We aimed to develop novel prognostic models by integrating micronecrosis to predict the survival of HCC patients after hepatectomy more precisely. METHODS: We enrolled 765 HCC patients receiving curative hepatic resection. They were randomly divided into a training cohort (n= 536) and a validation cohort (n = 229). We developed two prognostic models for postoperative recurrence-free survival (RFS) and overall survival (OS) based on independent factors identified through multivariate Cox regression analyses. The predictive performance was assessed using the Harrell concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve, compared with six conventional staging systems. RESULTS: The RFS and OS nomograms were developed based on tumor micronecrosis, tumor size, albumin-bilirubin grade, tumor number and prothrombin time. The C-indexes for the RFS nomogram and OS nomogram were respectively 0.66 (95% CI, 0.62-0.69) and 0.74 (95% CI, 0.69-0.79) in the training cohort, which was significantly better than those of the six common staging systems (0.52-0.61 for RFS and 0.53-0.63 for OS). The results were further confirmed in the validation group, with the C-indexes being 0.66 and 0.77 for the RFS and OS nomograms, respectively. CONCLUSION: The two nomograms could more accurately predict RFS and OS in HCC patients receiving curative hepatic resection, thereby aiding in formulating personalized postoperative follow-up plans.