Novel inflammation-based prognostic nomograms for individualized prediction in hepatocellular carcinoma after radical resection

Background The prognosis for patients with hepatocellular carcinoma (HCC) after liver resection ranges widely and is unsatisfactory. This study aimed to develop two novel nomograms that combined tumor characteristics and inflammation-related indexes to predict overall survival (OS) and recurrence-free survival (RFS). Methods In total, 3,071 patients who underwent radical resection were recruited. Independent risk factors were identified by Cox regression analysis and used to conduct prognostic nomograms. The C-index, time-dependent areas under the receiver operating characteristic curve (time-dependent AUC), decision curve analysis (DCA), and calibration curves were used to assess the performance of the nomograms. Results Multivariate analysis revealed that alpha-fetoprotein (AFP), resection margin, neutrophil times γ-glutamyl transpeptidase-to-lymphocyte ratio (NrLR), platelet-to-lymphocyte ratio (PLR), γ-glutamyl transpeptidase-to-platelet ratio (GPR), tumor size, tumor number, microvascular invasion, and Edmondson-Steiner grade were the independent risk factors associated with OS. The independent risk factors associated with RFS were hepatitis, AFP, albumin-bilirubin (ALBI), NrLR, PLR, PNI, GPR, tumor size, tumor number, microvascular invasion, and Edmondson-Steiner grade. The C-index of the nomograms in the training and validation cohort were 0.71 [95% confidence interval (CI): 0.70-0.73] and 0.71 (95% CI: 0.69-0.74) for the OS, and 0.71 (95% CI: 0.70-0.73) and 0.74 (95% CI: 0.72-0.76) for RFS, respectively. The C-index, time-dependent AUC, and DCA of the nomograms showed significantly better predictive performances than those of commonly used staging systems. The models could stratify patients into three different risk groups. The web-based tools are convenient for clinical practice. Conclusions Two novel nomograms in which integrated inflammation-related indexes and accessible clinical parameters were developed to predict OS and RFS in HCC patients who underwent radical resection. Such models will help guide postoperative individualized follow-up and adjuvant therapy.