Pretreatment Risk Stratification for Endoscopic Kidney-sparing Surgery in Upper Tract Urothelial Carcinoma: An International Collaborative Study
Bibliographic record
Abstract
BACKGROUND: Several groups have proposed features to identify low-risk patients who may benefit from endoscopic kidney-sparing surgery in upper tract urothelial carcinoma (UTUC). OBJECTIVE: To evaluate standard risk stratification features, develop an optimal model to identify ≥pT2/N+ stage at radical nephroureterectomy (RNU), and compare it with the existing unvalidated models. DESIGN, SETTING, AND PARTICIPANTS: This was a collaborative retrospective study that included 1214 patients who underwent ureterorenoscopy with biopsy followed by RNU for nonmetastatic UTUC between 2000 and 2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We performed multiple imputation of chained equations for missing data and multivariable logistic regression analysis with a stepwise selection algorithm to create the optimal predictive model. The area under the curve and a decision curve analysis were used to compare the models. RESULTS AND LIMITATIONS: Overall, 659 (54.3%) and 555 (45.7%) patients had ≤pT1N0/Nx and ≥pT2/N+ disease, respectively. In the multivariable logistic regression analysis of our model, age (odds ratio [OR] 1.02, 95% confidence interval [CI] 1.0-1.03, p = 0.013), high-grade biopsy (OR 1.81, 95% CI 1.37-2.40, p < 0.001), biopsy cT1+ staging (OR 3.23, 95% CI 1.93-5.41, p < 0.001), preoperative hydronephrosis (OR 1.37 95% CI 1.04-1.80, p = 0.024), tumor size (OR 1.09, 95% CI 1.01-1.17, p = 0.029), invasion on imaging (OR 5.10, 95% CI 3.32-7.81, p < 0.001), and sessile architecture (OR 2.31, 95% CI 1.58-3.36, p < 0.001) were significantly associated with ≥pT2/pN+ disease. Compared with the existing models, our model had the highest performance accuracy (75% vs 66-71%) and an additional clinical net reduction (four per 100 patients). CONCLUSIONS: Our proposed risk-stratification model predicts the risk of harboring ≥pT2/N+ UTUC with reliable accuracy and a clinical net benefit outperforming the current risk-stratification models. PATIENT SUMMARY: We developed a risk stratification model to better identify patients for endoscopic kidney-sparing surgery in upper tract urothelial carcinoma.
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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) | 0.000 | 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) | 0.000 | 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".