Urinary biomarkers in multicentric studies: Shaping the future of bladder cancer diagnosis and follow‐up
Bibliographic record
Abstract
Background and Objective: Bladder cancer (BC), a prevalent malignancy, poses significant diagnostic and surveillance challenges due to its high recurrence rates and reliance on cystoscopy, an invasive procedure for diagnosis and monitoring. While urine-based genomic and proteomic biomarkers offer promising non-invasive alternatives, their clinical implementation remains limited. This review synthesizes evidence from multicentric studies on urinary biomarkers for BC and evaluates their potential in reducing unnecessary invasive cystoscopies. Methods: A comprehensive review of literature was conducted searching for multicentric studies on urine-based genomic and proteomic biomarkers for BC detection and/or surveillance. MEDLINE/Pubmed, Embase and Scopus databases and BJUI, UroToday and European Urology Oncology registries were searched using National Library of Medicine Medical Subject Headings (MeSH) terms. Emphasis was placed on the comparative performance of diagnostic platforms across different research and clinical settings. Key Findings and Limitations: The literature search yielded 51 reports that were included for analysis. Multicentre studies enhance the generalizability of findings by addressing inter-laboratory variability and population diversity. This review underscores the importance of standardization, comparative performance analyses that these studies provide, and the potential for cost-effective non-invasive diagnostic tools. However, despite FDA approvals, no biomarker has replaced cystoscopy in clinical settings due to an inconsistent and insufficient combination of sensitivity, specificity and cost-effectiveness parameters. The performance of AssureMDX and Enhanced CxBladder tests showed the most promise, but further large-scale, standardized validation is still necessary. Conclusions and Clinical Implications: Urine-based biomarkers have the potential to improve early BC detection and surveillance while reducing reliance on invasive procedures and costs related to the disease. Future efforts should prioritize cost-effective, large-scale multicentric studies to facilitate the adoption of these biomarkers into routine practice.
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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 | 0.352 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".