Multi-Center Assessment of Lymph-Node Density and Nodal-Stage to Predict Disease-Specific Survival in Patients with Bladder Cancer Treated by Radical Cystectomy
Bibliographic record
Abstract
BACKGROUND: Prognostic tools in pathological-node (pN) patients after radical cystectomy (RC) are needed. OBJECTIVES: To evaluate the prognostic impact of lymph node (LN)-density on disease-specific survival (DSS) in patients with bladder cancer (BC) undergoing RC with pelvic lymph node dissection. METHODS: We analyzed a multi-institutional cohort of 1169 patients treated with upfront RC for cT1-4aN0M0 urothelial BCat nine centers. LN-densitywas calculated as the ratio of the number of positive LNs×100% to the number of LNs removed. The optimal LN-density cut-off value was defined by creating a time-dependent receiver operating characteristic (ROC) curve in pN patients. Univariable and multivariable Cox' regression analyses were used to assess the effect of conventional Tumor Nodes Metastasis (TNM) nodal staging system, LN-density and other LN-related variables on DSS in the pN-positive cohort. RESULTS: Of the 1169 patients, 463 (39.6%) patients had LN-involvement. The area under the ROC curve was 0.60 and the cut-off for LN-density was set at 20%, 223 of the pN-positive patients (48.2%) had a LN-density ≥ 20%. In multivariable models, the number of LN-metastases (HR 1.03, p = 0.005) and LN-density, either as continuous (HR 1.01, p = 0.013) or as categorical variable (HR 1.37, p = 0.014), were independently associated with worse DSS, whereas pN-stage was not. CONCLUSIONS: LN-density ≥ 20% was an independent predictor of worse DSS in BC patients with LN-involvement at RC. The integration of LN-density and other LN-parameters rather than only conventional pN-stage may contribute to a more refined risk-stratification in BC patients with nodal involvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".