Urodrill - a novel MRI-guided endoscopic biopsy technique to sample and molecularly classify muscle-invasive bladder cancer without fractionating the specimen during transurethral resection
Bibliographic record
Abstract
The current diagnostic pathway for patients with muscle-invasive bladder cancer (MIBC), which involves with computed tomography urography, cystoscopy, and transurethral resection of the bladder (TURB) to histologically confirm MIBC, delays definitive treatment. The Vesical Imaging-Reporting and Data System (VI-RADS) has been suggested for MIBC identification using magnetic resonance imaging (MRI), but a recent randomized trial reported misclassification in one-third of patients. We investigated a new endoscopic biopsy device (Urodrill) for histological confirmation of MIBC and assessment of molecular subtype by gene expression in patients with VI-RADS 4 and 5 lesions on MRI. In ten patients, Urodrill biopsies were guided by MR images to the muscle-invasive portion of the tumor via a flexible cystoscope under general anesthesia. During the same session, conventional TURB was subsequently performed. A Urodrill sample was successfully obtained in nine of ten patients. MIBC was verified in six of nine patients, and seven of nine samples contained detrusor muscle. In seven of eight patients for whom a Urodrill biopsy sample was subjected to RNA sequencing, single-sample molecular classification according to the Lund taxonomy was feasible. No complications related to the biopsy device occurred. A randomized trial comparing this new diagnostic pathway for patients with VI-RADS 4 and 5 lesions and the current standard (TURB) is warranted. Patient summary: We report on a novel biopsy device for patients with muscle-invasive bladder cancer that facilitates histology analysis and molecular characterization of tumor samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".