Current management and future perspectives of metastatic renal cell carcinoma
Bibliographic record
Abstract
Over the last number of years, the treatment of metastatic renal cell cancer has evolved tremendously with the advent of targeted therapy. Previously, immunotherapies, such as interferon alpha and interleukin-2, were the only treatment options available for this chemoresistant malignancy. Currently, seven additional agents, including sunitinib, sorafenib, axitinib, pazopanib, bevacizumab, everolimus and temsirolimus, have been approved for use in metastatic renal cell cancer, with several more in development. The efficacy of these agents depends primarily on inhibition of the vascular endothelial growth factor and mammalian target of rapamycin pathways, and have drastically improved the outcomes of patients diagnosed with metastatic renal cell cancer. This article reviews the major treatment advances that have occurred for metastatic renal cell cancer with the advent of targeted treatments, summarizes the evidence to support their use and addresses clinical issues that have arisen with them. To help guide clinicians in their decision-making with these emerging therapeutic choices, the evidence for sequencing and combining these agents, and the need for biomarkers will be addressed. The role of surgical management options, such as cytoreductive nephrectomy and metastectomy, in the era of targeted treatment is also reviewed. Several novel treatments are also on the horizon, which might serve as future avenues for treatment advancement in metastatic renal cell cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".