Potential role of predictive models in assessment of liver inflammation in patients with hepatocellular carcinoma: a two-center cohort study

BACKGROUND: Hepatic inflammation in patients with hepatocellular carcinoma (HCC) remains unclear. This study aimed to construct a clinically expedient predictive model to grade hepatic inflammation in HCC patients. METHODS: This is a two-center retrospective cohort study of HCC patients comprising Derivation cohort and External Validation cohort of 1201 and 505 patients, respectively. Variables of liver inflammation identified through uni- and multi-variate logistic regression analyses were incorporated into predictive nomograms and applied to Derivation cohort, subject to internal and external validation. RESULTS: Liver fibrosis severity score, portal hypertension severity, and model for end-stage liver disease-sodium independently predicted hepatic inflammation grade. Performance for distinguishing G1 and non-G1 (≥ G2) patients was good with C-index of 0.810 and 0.817 in Derivation and External Validation cohort, respectively. The nomogram performed poorly to predict grade G2, G3 and G2 + G3, but performed well to predict G4. CONCLUSIONS: Our nomogram exhibited good performance for scaling hepatic inflammation (G1 and G4) in HCC, and could be employed as adjunctive diagnostic tools to guide HCC management strategy.