Efficacy of Lenvatinib Versus Sorafenib in the Treatment of Unresectable Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Hepatocellular carcinoma (HCC) was the third leading cause of cancer-related deaths in the world. Current global treatment recommendations suggest lenvatinib and sorafenib have been approved to treat unresectable HCC. Studies comparing lenvatinib versus sorafenib for unresectable HCC have shown conflicting results and no structured review has yet evaluated its efficacy and safety. This article aims to estimate the efficacy of lenvatinib and sorafenib in patients with unresectable HCC. METHODS: This research was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) strategy. Literature searches were conducted through PubMed, ScienceDirect, Google Scholar, Cochrane Library, SpringerLink, and Ebsco. After quality assessment using the Newcastle-Ottawa Scale (NOS) and Cochrane Risk-of-bias, also data extraction, Review Manager 5.4 and RStudio 2024.04.1 software were used for analysis of overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR). RESULTS: A total of 9 studies were included, comprising 3,821 samples. All studies were retrospective studies. Our meta-analysis showed that OS and PFS in patients receiving lenvatinib were significantly better than patients receiving sorafenib with a protective hazard ratio (HR) of 0.70 (95%CI: 0.57-0.87, p=0.001) and 0.65 (95%CI: 0.54-0.78; p < 0.00001) respectively. Moreover, in the viral patients group, lenvatinib showed similar OS compared with sorafenib (HR=1.02; 95%CI: 0.77-1.36, p=0.87). Lenvatinib exhibited better ORR (OR = 7.87; 95%CI: 2.02-30.75; p = 0.003) and DCR (OR = 1.99; 95%CI: 1.53-2.60; p < 0.00001) compared with sorafenib. CONCLUSION: Lenvatinib provided significant benefits in OS, PFS, ORR, and DCR compared to sorafenib in patients with unresectable HCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".