Incidence and Predictors of Secondary Upper Tract Urothelial Cancer in Patients with High-Risk Non-Muscle Invasive Urinary Bladder Cancer and its Impact on Imaging Surveillance: A Retrospective Analysis with 1501 Patients
Bibliographic record
Abstract
Objectives: We aimed to study the incidence and predictors of upper tract urothelial cancer (UTUC) in patients with high-risk non-muscle invasive bladder cancer (HR-NMIBC). Methods: Patients who had HR-NMIBC were reviewed to identify those who subsequently developed UTUC. Complete transurethral resection was performed, and biopsies were collected for histopathology followed by intravesical chemoimmunotherapy. Patients were screened annually by computed tomography (CT) for UTUC. Results: Data for 1501 patients were reviewed. UTUC developed in 59 (4%) after a median of 20 months after HR-NMIBC. Most patients were symptomatic, but UTUC was discovered on routine follow-up imaging in 28%. On bivariate analysis, only multiple bladder tumors and the number of bladder recurrences were predictors for UTUC (P = 0.01 and P = 0.008, respectively). Multiple bladder tumors and ≥ 3 bladder recurrences remained significant on multivariable analysis. Conclusion: UTUC after HR-NMIBC is uncommon (4%). Despite routine follow-up CT imaging, recurrence was detected due to symptoms in most patients, and based on imaging only in 28%. Imaging surveillance can be prioritized in patients with multiple bladder tumors and those with ≥ 3 bladder recurrences. For the other patients, the benefit of imaging surveillance has to be weighed against the risks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".