Nomogram Based on Systemic Immune Inflammation Index and Prognostic Nutrition Index Predicts Recurrence of Hepatocellular Carcinoma After Surgery

Background: Surgery is a potential cure for hepatocellular carcinoma (HCC), but its postoperative recurrence rate is high and the prognosis is poor. At present, there is a lack of reliable predictive indicators. This study aims to develop a simple, practical, and effective predictive model. Materials and Methods: Retrospectively analyzed the clinical data of patients with HCC undergoing partial hepatectomy at the Third Affiliated Hospital of Soochow University from January 2010 to December 2015 and constructed a nomogram. The performance of the model was evaluated by C-index, ROC curve and calibration curve. The results were verified by the validation cohort data collected in the same center from January 2016 to January 2017 and compared with the traditional staging systems. Results: A total of 403 patients were enrolled in this study, including 238 cases in the training cohort and 65 cases in the validation cohort. Through the univariate and multivariate COX regression analysis in the training cohort, five independent risk factors of age, AFP, tumor size, satellite nodules, SII and PNI were filtrated and included in the nomogram. The C-index was 0.701 (95% CI: 0.654-0.748) in the training cohort and 0.705 (95% CI: 0.619-0.791) in the validation cohort. The AUC of 1- and 3-year were 0.706 and 0.716 in the training cohort and 0.686 and 0.743 in the validation cohort. The calibration curves showed a good agreement. Compared with traditional AJCC8th and BCLC staging systems, our nomogram showed better predictive ability. Conclusions: Our nomogram is simple, practical and reliable. According to our nomogram, predicting the risk of recurrence and stratifying management of HCC patients will bring the greatest survival benefit for patients.