Efficacy and Safety of Lenvatinib in Combination With Other Tyrosine Kinase Inhibitors for Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis evaluates the efficacy and safety of lenvatinib, both as monotherapy and in combination with other (tyrosine kinase inhibitors) TKIs, compared with other TKIs in metastatic renal cell carcinoma (RCC) treatment. METHODS: We searched for relevant studies from inception to February 2024 using PubMed, Web of Science, Cochrane Library, and Scopus. Eligible studies reported on the efficacy and safety of lenvatinib alone or in combination with other TKIs versus other TKIs for metastatic RCC. Primary outcomes included progression-free survival (PFS) and overall survival (OS). Secondary outcomes included objective response rate (ORR), adverse events (AEs), and health-related quality of life (HRQOL). RESULTS: Seventeen studies met the inclusion criteria. Lenvatinib, especially in combination therapies, significantly improved PFS (HR: 0.46, 95% CI: 0.38-0.54, P <0.001) and OS (HR: 0.80, 95% CI: 0.70-0.91, P <0.001) compared with other TKIs. Quality of life analyses showed mixed results, with EQ-5D demonstrating significant improvement (HR: 1.21, 95% CI: 0.90-1.53, P <0.001), while EORTC QLQ-C30 was not statistically significant. ORR analysis indicated a higher likelihood of achieving a complete or partial response with lenvatinib (OR: 2.04, 95% CI: 1.15-2.93, P =0.00). The analysis of total AEs above grade 3 showed no significant difference between lenvatinib and other TKIs (OR: -0.08, 95% CI: -0.21 to 0.06, P =0.26). CONCLUSIONS: Lenvatinib significantly enhances survival outcomes in metastatic RCC patients compared with other TKIs. While associated with various adverse events, its safety profile is comparable to other TKIs.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| 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".