Multidisciplinary systemic and local therapies for metastatic renal cell carcinoma: a narrative review
Bibliographic record
Abstract
INTRODUCTION: Systemic and local therapies for patients with metastatic renal cell carcinoma (mRCC) are often challenging despite the evolution of multimodal cancer therapies in the last decade. In this review, we will focus on recent multidisciplinary approaches for patients with mRCC. AREAS COVERED: Systemic therapies for patients with mRCC have been garnering attention particularly after the approval of immuno-oncology (IO) agents, including anti-programmed death 1/programmed death-ligand 1. IO combinations have significantly prolonged overall survival in patients with mRCC in the first-line setting. Regarding local therapies, cytoreductive nephrectomy (CN) has become less common in the post-Cancer du Rein Metastatique Nephrectomie et Antiangiogéniques (CARMENA) trial era, even though CN may still benefit selected patients with mRCC. In addition, metastasis-directed local therapies, namely metastasectomy or stereotactic radiotherapy, particularly for oligo-metastatic lesions or brain metastases, may have a prognostic impact. Several ablative techniques are also evolving while maintaining high local control rates with acceptable safety. EXPERT OPINION: Multimodal cancer therapies are essential for conquering complex cases of mRCC. Modern systemic therapies including IO-based combination therapy as well as local therapies including CN, metastasectomy, stereotactic radiotherapy, and ablative techniques appear to improve oncologic outcomes of patients with mRCC, although appropriate patient selection is indispensable.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".