Abstract
BACKGROUND The Integrated Liver Inflammatory Score (ILIS), which includes 5 serum indicators (albumin, bilirubin, neutrophil count, alpha-fetoprotein [AFP], and alkaline phosphatase [ALP]), is a novel inflammation-based predictive model associated with poor survival in hepatocellular carcinoma (HCC) patients. Our study aimed to assess the prognostic value of ILIS in HCC patients undergoing radical hepatectomy and establish a nomogram and artificial neural network based on their ILIS scores. MATERIAL AND METHODS This multicenter retrospective study included patients from 2 institutions from 2007 to 2017. Independent risk factors associated with Recurrence-free survival (RFS) and overall survival (OS) were identified through univariate and multifactor analysis in the training and validation groups, respectively. Afterward, column line graphs and artificial neural networks (ANN) were constructed and validated using the validation group. RESULTS A total of 432 patients were included in this study (275 in the training group and 157 in the validation group). In both cohorts, ILIS was correlated with pathological features such as tumor size, degree of differentiation, Child-Pugh class classification, and BCLC staging. Moreover, ILIS was identified as an independent risk factor for OS. ILIS-based nomograms and artificial neural networks also showed the prognostic value of ILIS. CONCLUSIONS Preoperative ILIS is an independent and effective predictor of prognosis in HCC patients treated with radical hepatectomy, as shown by the fact that higher ILIS are associated with worse patient prognosis. We have also established nomograms and ANNs that predict HCC prognosis with high accuracy.