Outcomes and prognosticators of stage 4 renal cell carcinoma with pathological T4 primary lesion using a large Canadian multi-institutional database
Bibliographic record
Abstract
INTRODUCTION: The primary objective of this study was to evaluate outcomes and prognosticators in patients who underwent radical nephrectomy (RN) or cytoreductive nephrectomy (CN), depending on the clinical stage of disease preoperatively, with a pathological T4 (pT4) renal cell carcinoma (RCC) outcome. There is little data on the outcome of this specific subset of patients. METHODS: From 2009-2016, we identified patients in the Canadian Kidney Cancer information system (CKCis) who underwent RN or CN and were found to have pT4 RCC. Clinical, operative, and pathological variables were analyzed with univariable and multivariable Cox proportional hazard models to identify factors associated with overall survival (OS). Survival curves were created using Kaplan-Meier methods and compared using the log-rank test. RESULTS: A total of 82 patients were included in the study cohort. Median patient age was 62 years (interquartile range [IQR] 55, 70). Fifty (61%) patients had clear-cell histology and 14 (17%) had sarcomatoid characteristics. Median followup was 12 months (IQR 3, 24). At last followup, eight (10%) patients are alive with no evidence of disease, 27 (33%) are alive with disease, four (5%) were lost to followup, 36 (44%) died of disease, and seven (8%) died of other causes. Tumor histological subtype (clear-cell vs. non-clear-cell) (p=0.0032), larger tumor size (cm) (p=0.012), and Fuhrman grade (G4 vs. G2-G3) (p=0.045) were significantly associated with mortality in a multivariable Cox regression model. CONCLUSIONS: For patients with pT4 RCC after RN or CN, survival is poor. Sarcomatoid features, non-clear-cell histology, and presence of systemic symptoms were associated with worse OS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".