Current management of metastatic renal cell carcinoma: evolving new therapies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Targeted therapies have recently replaced cytokine treatments as the gold standard for management of metastatic renal cell carcinoma (mRCC). Currently approved treatments include the tyrosine kinase inhibitors sunitinib, pazopanib, axitinib, sorafenib, cabozantinib and lenvatinib; the vascular endothelial growth factor (VEGF) inhibitor bevacizumab; the mammalian target of rapamycin (mTOR) inhibitors everolimus and temsirolimus; and the immunologic nivolumab. The purpose of this review is to provide an updated analysis of the clinical data supporting the use of these agents in the first-line and second-line setting. RECENT FINDINGS: In the first-line setting, pazopanib may be better tolerated than sunitinib, an individualized dosing sunitinib regimen based on toxicity might improve survival and cabozantinib appears to be an emerging option. In the second-line setting, three new therapies (cabozantinib, lenvatinib/everolimus and nivolumab) have shown superiority against everolimus, the previous standard therapy. The International Metastatic RCC Database Consortium prognostic model may be useful in guiding the selection of subsequent therapy and patients eligible for metastasectomy. SUMMARY: Targeted therapies are the standard treatment for mRCC. Despite advancements in survival, progression-free survival and tolerability, these targeted therapies remain largely noncurative. Further characterization of the RCC oncogenic pathway, and the ongoing clinical trials should help optimize the management of mRCC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".