Validation of an mRNA-based Urine Test for the Detection of Bladder Cancer in Patients with Haematuria
Bibliographic record
Abstract
BACKGROUND: In patients with haematuria, a fast, noninvasive test with high sensitivity (SN) and negative predictive value (NPV), which is able to detect or exclude bladder cancer (BC), is needed. A newly developed urine assay, Xpert Bladder Cancer Detection (Xpert), measures five mRNA targets (ABL1, CRH, IGF2, UPK1B, and ANXA10) that are frequently overexpressed in BC. OBJECTIVE: To validate the performance of Xpert in patients with haematuria. DESIGN, SETTING, AND PARTICIPANTS: Voided precystoscopy urine specimens were prospectively collected at 22 sites from patients without prior BC undergoing cystoscopy for haematuria. Xpert, cytology, and UroVysion procedures were performed. Technical validation was performed and specificity (SP) was determined in patients without BC. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Test characteristics were calculated based on cystoscopy and histology results, and compared between Xpert, cytology, and UroVysion. RESULTS AND LIMITATIONS: We included 828 patients (mean age 64.5 yr, 467 males, 401 never smoked). Xpert had an SN of 78% (95% confidence interval [CI]: 66-87) overall and 90% (95% CI: 76-96) for high-grade (HG) tumours. The NPV was 98% (95% CI: 97-99) overall. The SP was 84% (95% CI: 81-86). In patients with microhaematuria, only one HG patient was missed (NPV 99%). Xpert had higher SN and NPV than cytology and UroVysion. Cytology had the highest SP (97%). In a separate SP study, Xpert had an SP of 89% in patients with benign prostate hypertrophy and 92% in prostate cancer patients. CONCLUSIONS: Xpert is an easy-to-use, noninvasive test with improved SN and NPV compared with cytology and UroVysion, representing a promising tool for identifying haematuric patients with a low likelihood of BC who might not need to undergo cystoscopy. PATIENT SUMMARY: Xpert is an easy-to-perform urine test with good performance compared with standard urine tests. It should help identify (micro)haematuria patients with a very low likelihood to have bladder cancer.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
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".