Stepwise Application of Urine Markers to Detect Tumor Recurrence in Patients Undergoing Surveillance for Non-Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
BACKGROUND: The optimal use of urine markers in the surveillance of non-muscle-invasive bladder cancer (NMIBC) remains unclear. Aim of the present study was to investigate the combined and stepwise use of the four most broadly available urine markers to detect tumor recurrence in patients undergoing surveillance of NMIBC. PATIENTS AND METHODS: 483 patients with history of NMIBC were included. Cytology, UroVysion, fluorescence in situ hybridization (FISH), immunocytology (uCyt+), and NMP22 ELISA were performed before surveillance cystoscopy. Characteristics of single tests and combinations were assessed by contingency analysis. RESULTS: 128 (26.5%) patients had evidence of tumor recurrence. Sensitivities and negative predictive values (NPVs) of the single tests ranged between 66.4-74.3 and 82.3-88.2%. Two-marker combinations showed sensitivities and NPVs of 80.5-89.8 and 89.5-91.2%. A stepwise application of the two-test combinations with highest accuracy (cytology and FISH; cytology and uCyt+; uCyt+ and FISH) showed NPVs for high-risk recurrences (G3/Cis/pT1) of 98.8, 98.8, and 99.1%, respectively. CONCLUSIONS: Combinations of cytology, FISH, immunocytology, and NMP22 show remarkable detection rates for recurrent NMIBC. Stepwise two-test combinations of cytology, FISH, and immunocytology have a low probability of missing a high-risk tumor. The high sensitivities may justify the use of these combinations in prospective studies assessing the use of urine markers to individualize intervals between cystoscopies during follow-up.
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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 | 0.005 |
| 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 | 0.000 | 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 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".