A Novel Nomogram for Predicting Recurrence of Hepatocellular Carcinoma After Liver Transplantation.

Abstract

Background: Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) remains a critical issue with limited predictive accuracy. We aimed to develop a nomogram integrating clinical indicators, pathological factors, serum tumor markers, and inflammatory markers.

Methods: Retrospective data of patients with HCC were collected at Beijing You'an Hospital from January 2015 to December 2022. Univariate and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses, and calibration diagrams were used to assess the predictive capacity of the nomogram.

Results: A total of 395 patients with HCC after LT were included in the present study. The nomogram model was established based on seven independent risk factors: LT criteria, hepatic steatosis, microvascular invasion (MVI), Edmondson-Steiner grade, creatinine (Cr), aspartate aminotransferase (AST), and systemic immune-inflammation index (SII). The area under the curve (AUC) for 1 year, 3 years, and 5 years predicting postoperative recurrence-free survival (RFS) was 0.811, 0.826, and 0.828. Decision curve analyses (DCAs) showed excellent clinical utility for the model.

Conclusions: We developed a novel, simple, and clinically relevant nomogram for early prediction and diagnosis of post-transplant HCC recurrence.