Application of the combined tumor burden score and platelet-albumin-bilirubin score model for predicting postoperative tumor recurrence in liver transplant recipients with hepatocellular carcinoma
10.12464/j.issn.1674-7445.2025073
- VernacularTitle:肿瘤负荷评分联合血小板-白蛋白-胆红素评分模型在预测肝癌肝移植受者术后肿瘤复发的应用
- Author:
Weidong ZHU
1
;
Junyang XIAO
1
;
Xiaoji QIU
1
;
Lizhi LÜ
2
,
3
;
Jianwei CHEN
2
,
3
;
Fang YANG
2
,
3
Author Information
1. Fuzong Clinical Medical College of Fujian Medical University, Fuzhou 350025, China.
2. Fuzong Clinical Medical College of Fujian Medical University, Fuzhou 350025, China
3. .
- Publication Type:OriginalArticle
- Keywords:
Combined tumor burden score and platelet-albumin-bilirubin score model;
Hepatocellular carcinoma;
Liver transplantation;
Tumor recurrence;
Predictive model;
Nomogram model;
Risk factor;
Survival analysis
- From:
Organ Transplantation
2025;16(4):556-564
- CountryChina
- Language:Chinese
-
Abstract:
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 alpha-fetoprotein (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.
