Prognostic value of extranodal extension and other lymph node parameters in patients with upper tract urothelial carcinoma.
Bibliographic record
Abstract
281 Background: The aim of the current study was to assess the prognostic value of extranodal extension (ENE) and other lymph node (LN) parameters in a large multicenter cohort of patients with LN metastasis (LNM) following radical nephroureterectomy (RNU). Methods: Retrospective analysis of 222 patients with LNM treated with RNU for upper tract urothelial carcinoma (UTUC) without neoadjuvant therapy. Microscopically, each LN metastasis was evaluated for presence of ENE. Results: The median number of LNs removed, number of positive LNs, and LN density were 4 (IQR: 8), 2 (IQR: 2), and 51.3% (IQR: 71.7%), respectively. Overall, 110 patients (49.5%) had ENE. Presence of ENE was associated with more advanced pT stage (p=0.026). In multivariable analyses, ENE was associated with disease recurrence (p=0.01) and cancer-specific mortality (p=0.013). LN density, when stratified by 30% cutoff, was associated with disease recurrence and cancer-specific mortality (p=0.048 and p=0.049) in univariable, but not in multivariable analyses. Addition of ENE to a multivariable model including pT stage and tumor architecture improved predictive accuracy for disease recurrence from 70.3% to 74.5% (p<0.001). Addition of ENE to a multivariable model including age, pT stage, and tumor architecture improved predictive accuracy for cancer-specific mortality from 70.6% to 74.4% (p<0.001). Conclusions: ENE is a powerful predictor of clinical outcomes in UTUC patients with LNM. While other LN parameters seem to have limited clinical value, ENE could help risk stratify UTUC patients with LNM for better counseling and clinical trial design.
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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".