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Updated: Jan 13, 2026
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Metabolic burden-based clinical-radiological model for predicting postoperative recurrence of hepatitis B-related
Beixuan Zheng1, Heqing Wang2, Yuyao Xiao1
1Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Objectives:
To establish a metabolic burden-based clinical-radiological model for predicting postoperative recurrence in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) at Barcelona Clinic Liver Cancer (BCLC) stages 0-A.
Materials And Methods:
This retrospective multi-center study included HBV-related HCC (BCLC 0-A) undergoing curative surgery. Metabolic burden was defined as the cumulative number of metabolic abnormalities. Trend test assessed dose-dependent relationship. Predictors were identified via univariate and multivariate Cox regression analyses, and a nomogram was developed. The model underwent internal validation (5-fold, 100 times cross) and external validation. Performance was evaluated using C-index, calibration curves, and decision curve analysis.
Results:
The internal and external cohorts consisted of 363 patients (55.9 ± 10.7 years, 295 males) and 74 patients (55.5 ± 10.2 years, 55 males). Recurrence risk increased by 1.53 times (p = 0.049) and 1.64 times (p = 0.018) for patients with 2 and 3-4 metabolic abnormalities (ptrend = 0.022). Independent predictors included tumor burden score > 2.4 (HR = 2.40, p = 0.003), metabolic abnormalities ≥ 2 (HR = 1.49, p = 0.023), aspartate transaminase/alanine transaminase ratio > 1 (HR = 1.51, p = 0.012), albumin-bilirubin grade 2 (HR = 1.70, p = 0.020), arterial rim enhancement (HR = 1.87, p = 0.002) and mosaic appearance (HR = 1.55, p = 0.033). C-indices for predicting 2- and 5-year recurrence were 0.728 (95% CI: 0.726-0.729) and 0.674 (95% CI: 0.673-0.675) in training sets, 0.716 (95% CI: 0.711-0.720) and 0.657 (95% CI: 0.653-0.660) in internal validation sets, and 0.710 (95% CI: 0.602-0.855) and 0.683 (95% CI: 0.594-0.798) in external cohort. The model showed higher predictive efficacy (p < 0.001 for all) and better clinical net benefit compared to BCLC and CNLC staging systems in the very early/early-stage of HCCs.
Conclusion:
The metabolic burden-based clinical-radiological model effectively predicts postoperative recurrence in HBV-related HCC.
Critical Relevance Statement:
Patients with HBV-related HCC who have two or more coexisting metabolic abnormalities may have a higher risk of postoperative recurrence. The metabolic burden-based clinical-radiological model is valuable in predicting postoperative recurrence KEY POINTS: Metabolic abnormalities were dose-dependently related to the risk of postoperative recurrence. The clinical-radiological model showed well-predictive efficacy in validation cohorts. The clinical-radiological model displayed higher efficacy compared to existing staging systems for the very early/early-stage of HCCs.
