Systemic inflammatory response markers improve the discrimination for prognostic model in hepatocellular carcinoma.

Abstract

Background/purpose of the study: We aimed to evaluate the performance of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and their combination (combined NLR-PLR, CNP) in predicting overall survival (OS) and recurrence-free survival (RFS) in a large cohort of unselected hepatocellular carcinoma (HCC) patients.

Methods: Training and validation cohort data were retrieved from the Italian Liver Cancer (ITA.LI.CA) database. The optimal cut-offs of NLR and PLR were calculated according to the multivariable fractional polynomial and the minimum p value method. The continuous effect and best cut-off categories of NLR and PLR were analyzed using multivariable Cox regression analysis. A shrinkage procedure adjusted over-fitting hazard ratio (HR) estimates of best cut-off categories. C-statistic and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination properties of the biomarkers when added to clinical survival models.

Results: 2,286 patients were split into training (n = 1,043) and validation (n = 1,243) cohorts. The optimal cut-offs for NLR and PLR were 1.45 and 188, respectively. NLR (HR 1.58, 95% CI 1.11-2.28, p = 0.014) and PLR (HR 1.79, 95% CI 1.11-2.90, p = 0.018) were independent predictors of OS. When incorporated into a clinical prognostic model that includes age, alpha-fetoprotein (AFP), the CHILD-Pugh score, and the Barcelona Clinic Liver Cancer (BCLC) staging system, CNP had a significant incremental value in predicting OS (IDI 1.3%, p = 0.04). Data were confirmed in the validation cohort. Neither NLR nor PLR significantly predicted RFS in the training cohort.

Conclusions: NLR, PLR, and CNP independently predicted shorter OS in HCC patients. The addition of CNP to the survival prediction model significantly improved the model's accuracy in predicting OS.