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. 2025 Aug;19(4):915-928.
doi: 10.1007/s12072-025-10806-6. Epub 2025 Mar 25.

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

Alba Rocco #  1 , Costantino Sgamato #  2 , Filippo Pelizzaro #  3   4 , Vittorio Simeon  5 , Pietro Coccoli  2 , Debora Compare  2 , Elisa Pinto  3   4 , Giorgio Palano  3   4 , Francesco Giuseppe Foschi  6 , Giovanni Raimondo  7 , Gabriele Missale  8 , Gianluca Svegliati-Baroni  9 , Franco Trevisani  10   11 , Eugenio Caturelli  12 , Maurizia Rossana Brunetto  13 , Gianpaolo Vidili  14 , Alberto Masotto  15 , Donatella Magalotti  16 , Claudia Campani  17 , Antonio Gasbarrini  18 , Francesco Azzaroli  11   19 , Gian Ludovico Rapaccini  18 , Bernardo Stefanini  11   20 , Rodolfo Sacco  21 , Andrea Mega  22 , Edoardo Giovanni Giannini  23   24 , Giuseppe Cabibbo  25 , Mariella Di Marco  26 , Maria Guarino  27 , Paolo Chiodini  5 , Fabio Farinati  3 , Gerardo Nardone  28
Affiliations

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

Alba Rocco et al. Hepatol Int. 2025 Aug.

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.

Keywords: Free survival; Hepatocellular carcinoma; Lymphocyte ratio; Neutrophil; Platelet; Prognosis; Recurrence; Survival; To.

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Conflict of interest statement

Declarations. Conflict of interest: The authors have no relevant conflicts of interest. Human and animal rights: The ITA.LI.CA database management conforms to the past and current Italian legislation regarding privacy, and the present study conforms to the ethical guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the ITA.LI.CA coordinating centre, Alma Mater Studiorum University of Bologna, on 15th May 2012 (approval number 99/2012/O/Oss). Informed consent: Written informed consent was obtained from all participants.

Figures

Fig. 1
Fig. 1
Flow-chart of the study
Fig. 2
Fig. 2
Continuous association and best cut-off of NLR and PLR biomarkers. Smoothed graph of the association of NLR (panel a) and PLR (panel c) following evaluation of the linearity assumption using fractional polynomials; note the logarithmic shape of NLR. The best cut-off value minimizes the p value of the HR for NLR (panel b) and PLR (panel d). Value crossing HR = 1 represents the median value for each biomarker
Fig. 3
Fig. 3
Kaplan–Meier curves of survival data of HCC patients (n = 1,043) according to NLR (a) and PLR (b) best cut-offs and CNP (c). Time on the x-axis represents months of observation (follow-up extended up to 206 months). The table with subjects at risk is reported for each biomarker at each specific time point. P value is a log-rank test
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