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Updated: Jun 23, 2026

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Comparative efficacy and safety of first-line treatments for advanced hepatocellular carcinoma: a Bayesian network
Yun Su1,2, Tongze Cai3, Jingxuan Wei4
1The First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China.
Background:
The therapeutic landscape of advanced hepatocellular carcinoma (HCC) has expanded beyond sorafenib, with multiple combination regimens now demonstrating improved outcomes. However, the absence of direct head-to-head comparisons complicates treatment selection in clinical practice.
Methods:
We performed a Bayesian network meta-analysis of phase III randomized controlled trials to compare the efficacy and safety of first-line treatments for patients with advanced HCC. Overall survival (OS), progression-free survival (PFS), and grade ≥ 3 adverse events were assessed. Treatment rankings were estimated using surface under the cumulative ranking curve values.
Results:
Sixteen trials comprising 8, 753 patients were included. Hepatic arterial infusion chemotherapy (HAIC)-based regimens, particularly sorafenib plus HAIC and HAIC_FO, achieved the highest rankings for both PFS and OS. Several immunotherapy-based combinations also demonstrated significant survival benefits across multiple clinical subgroups without a corresponding increase in severe adverse events. Subgroup analyses suggested consistent benefits of selected regimens across age, sex, macrovascular invasion, extrahepatic spread, and ECOG performance status.
Conclusions:
HAIC-based strategies and selected immunotherapy combinations appear to provide superior survival outcomes as first-line treatment for advanced HCC. Given limitations related to comparator selection and evolving evidence, future head-to-head randomized trials and updated network analyses are warranted to refine optimal treatment strategies.
Systematic Review Registration:
https://www.crd.york.ac.uk/prospero/, identifier CRD420251124082.
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