The Role of Cytoreductive Nephrectomy in Renal Cell Carcinoma with Sarcomatoid Histology: A Case Series and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Renal cell carcinoma with sarcomatoid dedifferentiation represents a rare histological entity characterized by aggressive behavior, limited efficacy of tyrosine kinase inhibitors or mTOR inhibitors, and poor outcome. The immune checkpoint inhibitor therapy regimen combining ipilimumab with nivolumab represents a new standard of care for this patient population due to a hitherto unprecedented response rate and overall survival. On the other hand, the role of cytoreductive nephrectomy in metastatic renal cell carcinoma, in particular, with sarcomatoid histology, remains controversial. PATIENT AND METHODS: In the present case series, we report six patients with locally advanced or synchronous metastatic sarcomatoid renal cell carcinoma and intermediate or poor International Metastatic RCC Database Consortium (IMDC) risk score, five of whom were successfully subjected to cytoreductive nephrectomy. RESULTS: All six patients received the combination regimen of ipilimumab with nivolumab. Five of these patients underwent upfront cytoreductive nephrectomy followed by systemic treatment without any significant delay, with a durable treatment outcome. Notably, two patients with poor prognostic features achieved a long-term major partial response to therapy. We also performed a review of the literature on optimal treatment strategies for patients with sarcomatoid renal cell carcinoma. CONCLUSION: Herein, we highlight the feasibility of performing cytoreductive nephrectomy in patients with intermediate/poor prognosis metastatic renal cell carcinoma with sarcomatoid dedifferentiation followed by immunotherapy with ipilimumab and nivolumab. To enhance the chances of immunotherapy success, cytoreductive nephrectomy should also be considered for patients presenting with a disease with adverse prognostic parameters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".