Sarcomatoid Dedifferentiation as a Predictor of Cancer-Specific Mortality in Surgically Treated Localized Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: In contemporary surgically treated patients with localized high-grade (G3 or G4) clear-cell renal cell carcinoma (ccRCC), it is not known whether presence of sarcomatoid dedifferentiation is an independent predictor and/or an effect modifier, when cancer-specific mortality (CSM) represents an endpoint. METHODS: Within the Surveillance, Epidemiology, and End Results database, all surgically treated localized high-grade ccRCC patients treated between 2010 and 2020 were identified. Univariable and multivariable Cox-regression models were used. RESULTS: In 18,853 surgically treated localized high-grade (G3 or G4) ccRCC patients, 5-year CSM-free survival was 87% (62% vs. 88% with vs. without sarcomatoid dedifferentiation, p < 0.001). Presence of sarcomatoid dedifferentiation was an independent predictor of higher CSM (hazard ratio [HR] 1.8, p < 0.001). In univariable survival analyses predicting CSM, presence versus absence of sarcomatoid dedifferentiation in G3 versus G4 yielded the following hazard ratios: HR 1.0 in absent sarcomatoid dedifferentiation in G3; HR 2.7 (p < 0.001) in absent sarcomatoid dedifferentiation in G4; HR 3.9 (p < 0.001) in present sarcomatoid dedifferentiation in G3; HR 5.1 (p < 0.001) in present sarcomatoid dedifferentiation in G4. Finally, in multivariable Cox-regression analyses, the interaction terms defining present versus absent sarcomatoid dedifferentiation in G3 versus G4 represented independent predictors of higher CSM. CONCLUSIONS: In contemporary surgically treated patients with localized high-grade ccRCC, sarcomatoid dedifferentiation is not only an independent multivariable predictor of higher CSM, but also interacts with tumor grade and results in even better ability to predict CSM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".