Urinary Tumor DNA-Based Diagnosis and Surveillance for Nonmuscle-Invasive Bladder Cancer—Current Landscape and Future Directions
Bibliographic record
Abstract
Bladder cancer has a significant impact on patients, in terms of both morbidity and financial burden. This is especially true for patients with nonmuscle-invasive bladder cancer, who undergo long-term surveillance via cystoscopy and imaging, resulting in significant costs and risks. To address this issue, urinary tumor DNA analysis, or “urinary liquid biopsy,” has emerged as a potential solution to reduce the testing burden and mitigate many of the costs and risks. Over time, urinary tumor DNA analysis has undergone several refinements. However, existing FDA-approved urinary biomarker assays currently lack the sensitivity and specificity to significantly impact clinical decision-making. Subsequent iterations of these technologies have attempted to bridge this gap by improving their diagnostic accuracy, and ultimately, clinical utility. Here, we discuss the current role as well as future directions of urinary tumor DNA analysis for the detection and long-term surveillance of nonmuscle-invasive 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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".