Objective  To investigate the predictive value of the combined tumor burden score (TBS) and platelet-albumin-bilirubin (PALBI) score model for postoperative tumor recurrence in liver transplant recipients with hepatocellular carcinoma (HCC).

Methods  The general information of 158 recipients diagnosed with HCC and underwent liver transplantation at the 900th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from 2008 to 2021 was collected. Lasso regression analysis combined with multivariate Cox regression analysis were used to identify independent risk factors for postoperative tumor recurrence after liver transplantation with HCC. A nomogram prediction model was constructed based on variables selected by Lasso regression analysis, and the predictive performance of the model was verified by calibration curve and clinical decision curve. The optimal cut-off values for postoperative tumor recurrence in liver transplant recipients with HCC were determined by receiver operating characteristic (ROC) curve, and Kaplan-Meier analysis was used to compare survival differences among different groups.

Results  Among the 158 liver transplant recipients with HCC, 82 experienced tumor recurrence, with a recurrence rate of 51.9% and a median tumor-free survival time of 10 (4, 25) months. Results of Lasso regression analysis and multivariate Cox regression analysis showed that AFP≥400 ng/mL, TBS and PALBI score were all independent risk factors for postoperative tumor recurrence in liver transplant recipients with HCC (all P＜0.05). The combined high TBS-high PALBI score showed the highest predictive value (hazard ratio 6.909, 95% confidence interval 3.067-15.563, P＜0.001). A nomogram prediction model was constructed based on six variables selected by Lasso regression analysis. Calibration curve showed good consistency between the model's predicted results and the ideal curve. Decision curve analysis indicated that the nomogram prediction model provided the highest clinical benefit for predicting 1-year tumor-free survival after liver transplantation with HCC. Time-dependent ROC curves at 1, 3 and 5 years after surgery showed that TBS-PALBI model had good predictive performance, with no significant difference in area under the curve (AUC) compared with TBS-PALBI-AFP model. The optimal cut-off values for predicting postoperative tumor recurrence were determined by ROC curve, with a PALBI score cut-off of −2.334 and a TBS cut-off of 5.305. Recipients were divided into a low TBS-low PALBI score group (n=47) and a low/high TBS-low/high PALBI score group (at least one score was high) (n=111). Kaplan-Meier survival analysis showed that the low TBS-low PALBI score group had a higher tumor-free survival rate than the low/high TBS-low/high PALBI score group, with a significant difference (P＜0.05).

Conclusions  TBS-PALBI model provides a novel, simple and effective tool for assessing the prognosis of liver transplant recipients with HCC. The nomogram model constructed based on this has significant advantages in predictive performance and may serve as a reference for guiding individualized treatment plans and improving clinical outcomes.