Biomarkers for detection and surveillance of bladder cancer
Bibliographic record
Abstract
INTRODUCTION: Bladder cancer is the fourth most common cancer in men and the ninth most common cancer in women in Canada. Early detection of tumours is essential for improved prognosis and long-term survival. The standard method for detection and surveillance is cystoscopy together with urine cytology. Cystoscopy is relatively sensitive but is expensive and invasive. Urinary cytology is a noninvasive method that has poor sensitivity but high specificity; it is relied on for the detection of carcinoma in situ. Currently, several urinary-based bladder tumour biomarkers with USFDA/Health Canada approval are available commercially, but none have been widely adopted by urologists despite their offering high sensitivity and/or specificity. We present here a review of recent studies evaluating 7 commercial biomarker assays for the detection and/or surveillance of bladder cancer. RESULTS: SENSITIVITY AND SPECIFICITY RANGES, RESPECTIVELY, FOR EACH MARKER WERE REPORTED AS FOLLOWS: BTA Stat (Polymedco), 52.5%-78.0% and 69.0%-87.1%; BTA Trak (Polymedco), 51%-100% and 73%-92.5%; cytology, 12.1%-84.6% and 78.0%-100%; hematuria dipstick, 47.0%-92.6% and 51.0%-84.0%; NMP22 Bladder Cancer Test (Matritech), 34.6%-100% and 60.0%-95.0%; NMP22 BladderChek (Matritech), 49.5%-65.0% and 40.0%-89.8%; ImmunoCyt/uCyt+ (DiagnoCure), 63.3%-84.9% and 62.0%-78.1%; ImmunoCyt/uCyt+ and cytology, 81.0%-89.3% and 61.0%-77.7%; and UroVysion (Abbott Molecular)/florescence in situ hybridization, 68.6%-100% and 65.0%-96.0%. CONCLUSION: We find that no currently available bladder cancer urinary marker is sensitive enough to eliminate the need for cystoscopy. In addition, cytology remains integral to the detection of occult cancer. However, owing to their relatively high sensitivities, these markers may be used to extend the period between cystoscopies in the surveillance of patients with transitional cell carcinoma. Further study is required to determine which markers, alone or in panel, would best accomplish this.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".