Abstract
OBJECTIVE: To investigate the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SII) for early recurrence after liver resection in patients with hepatitis B-related hepatocellular carcinoma. METHODS: A retrospective study was conducted on 162 patients who underwent hepatitis B-related hepatocellular carcinoma (HCC) resection between January 2013 and April 2016. The Youden index was utilized to calculate the optimal cut-off value. The Pearson Chi-square test was applied to analyze the relationship between inflammatory indexes and common clinical and pathological features. The Kaplan-Meier method and Log-Rank test were implemented to compare the recurrence-free survival rate within 2 years of the population. The Cox regression analysis was used to identify the risk factors for early postoperative recurrence. RESULTS: The best cut-off values of SIRI, PLR, NLR and SII were 0.785, 86.421, 2.231 and 353.64, respectively. Tumor diameter, degree of tumor differentiation, vascular invasion, SIRI>0.785, PLR>86.421, NLR>2.231 and SII>353.64 were risk factors for early recurrence. Combining the above seven risk factors to construct a joint index, the AUC of the joint prediction model was 0.804. The areas under the ROC curves of SIRI, PLR, NLR, and SII were 0.659, 0.725, 0.680, and 0.723, respectively. There was no significant difference in the predictive ability between the single inflammatory index models, but the predictive performance of the joint prediction model was significantly higher than that of the single inflammatory index models. The patients with lower SIRI, PLR, NLR, SII and joint index value had longer recurrence-free survival within 2 years. CONCLUSION: The joint index CIP, constructed by combining preoperative SIRI, PLR, NLP and SII with pathological features, can better predict the early recurrence of HBV-related HCC patients after surgery, which is beneficial in identifying high-risk patients and assisting clinicians to make better clinical choices.