Outcomes of Metastatic Chromophobe Renal Cell Carcinoma (chrRCC) in the Targeted Therapy Era: Results from the International Metastatic Renal Cell Cancer Database Consortium (IMDC)
Bibliographic record
Abstract
Background: Treatment outcomes are poorly characterized in patients with metastatic chromophobe renal cell cancer (chrRCC), a subtype of renal cell carcinoma. Objective: This retrospective series aims to determine metastatic chrRCC treatment outcomes in the targeted therapy era. Methods: A retrospective data analysis was performed using the IMDC dataset of 4970 patients to determine metastatic chrRCC treatment outcomes in the targeted therapy era. Results: 109/4970 (2.2%) patients had metastatic chrRCC out of all patients with mRCC treated with targeted therapy. These patients were compared with 4861/4970 (97.8%) clear cell mRCC (ccRCC) patients. Patients with metastatic chrRCC had a similar OS compared to patients with ccRCC (23.8 months (95% CI 16.7 – 28.1) vs 22.4 months (95% CI 21.4 – 23.4), respectively ( p = 0.0908). Patients with IMDC favorable (18%), intermediate (59%) and poor risk (23%) had median overall survivals of 31.4, 27.3, and 4.8 months, respectively ( p = 0.028). Conclusions: To the authors’ knowledge, this is the largest series of metastatic chrRCC patients and these results set new benchmarks for survival in clinical trial design and patient counseling. The IMDC criteria risk categories seem to stratify patients into appropriate favourable, intermediate, and poor risk groups, although larger patient numbers are required. It appears that outcomes between metastatic chrRCC and ccRCC are similar when treated with conventional targeted therapies. Patients with metastatic chrRCC can be treated with tyrosine kinase inhibitors and enrolled in clinical trials to further measure outcomes in this rare patient population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".