Genomic and clinical determinants of recurrence in localized clear cell renal cell carcinoma (ccRCC).
Bibliographic record
Abstract
664 Background: Multiple clinical risk scores and gene expression models have predicted recurrence in localized ccRCC. However, few studies explored genomic alterations (GA) predicting recurrence. Methods: We assessed genomic and clinical correlates of disease-free survival (DFS) in surgically treated localized ccRCC using a targeted next generation sequencing (NGS) platform (Oncopanel/PROFILE) and publicly available NGS and clinical data from TCGA. Univariable and stepwise multivariable Cox regression models (stratified by database) were performed. Results: 478 patients (123 patients from our institution and 355 patients from TCGA) were included. 150 (31.4%) patients experienced a DFS event (recurrence or death) and 94 (19.7%) died at 3.1 years (yrs) of median follow-up. Median DFS was 6.3 (5.4-7.2) yrs and the 5-yr overall survival rate was 70.8% (64.9-76.7). On multivariable analysis, 4 clinical factors and mutations in 3 genes were significantly associated with recurrence (Table). Conclusions: Our study suggests that PTEN, BAP1 and KDM5C GA may improve on clinical factors for prediction of localized ccRCC recurrence. Further work is needed to determine if these GA could improve existing validated risk models. [Table: see text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".