<i>Fibroblast Growth Factor Receptor 3</i> Mutation Analysis on Voided Urine for Surveillance of Patients with Low-Grade Non-Muscle–Invasive Bladder Cancer
Bibliographic record
Abstract
PURPOSE: Mutations in the fibroblast growth factor receptor 3 (FGFR3) have been found in 70% of the low-grade non-muscle-invasive bladder cancer (NMI-BC) tumors. We aim to determine the potential of FGFR3 mutation analysis on voided urine to detect recurrences during surveillance of patients with low-grade NMI-BC. EXPERIMENTAL DESIGN: FGFR3 mutation status of the study inclusion tumor was determined from 200 low-grade NMI-BC patients. Patients with an FGFR3-mutant inclusion tumor were selected for analysis and monitored by cystoscopy, and voided urine samples were collected. FGFR3 mutation analysis was done on 463 prospectively collected urines. Sensitivity and predictive value of the assay were determined for detection of concomitant recurrences. Longitudinal and Cox time-to-event analyses were done to determine the predictive value for detection of future recurrences. RESULTS: Median follow-up was 3.5 years. The sensitivity of the assay for detection of concomitant recurrences was 26 of 45 (58%). Of the 105 positive urine samples, 85 (81%) were associated with a concomitant or a future recurrence. An FGFR3-positive urine was associated with a 3.8-fold (P < 0.0001) higher risk of having a recurrence in the Cox analysis. In contrast, only 41 of 358 (11%) FGFR3-negative urine samples were associated with a recurrence. Positive predictive value increased from 25% to 90% in patients having consecutive FGFR3-positive urine tests. CONCLUSIONS: FGFR3 mutation analysis on voided urine is a simple and noninvasive diagnostic method for detection of recurrences during surveillance of patients presenting with a low-grade FGFR3-mutant NMI-BC tumor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".