Real-Time Urethral and Ureteral Assessment during Radical Cystectomy Using Ex-Vivo Optical Imaging: A Novel Technique for the Evaluation of Fresh Unfixed Surgical Margins
Bibliographic record
Abstract
Background: Our study aims to assess the feasibility and the reproducibility of fluorescent confocal microscopy (FCM) real-time assessment of urethral and ureteral margins during open radical cystectomy (ORC) for bladder cancer (BCa). Methods: From May 2020 to January 2022, 46 patients underwent ORC with intraoperative FCM evaluation. Each specimen was intraoperatively stained for histopathological analysis using FCM, analyzed as a frozen section (FSA), and sent for traditional H&E examination. Sensitivity, specificity, positive predictive value (PPV), and the negative predictive value (NPV) of FCM and FSA were assessed and compared with H&E for urethral and ureteral margins separately. Results: The agreement was evaluated through Cohen’s κ statistic. Urethral diagnostic agreement between FCM and FSA showed a κ = 0.776 (p < 0.001), while between FCM and H&E, the agreement was κ = 0.691 (p < 0.001). With regard to ureteral margins, an overall agreement of κ = 0.712 (p < 0.001) between FCM and FSA and of κ = 0.481 (p < 0.001) between FCM and H&E was found. Conclusions: FCM proved to be a safe, feasible, and reproducible method for the intraoperative assessment of urethral and ureteral margins during ORC. Compared to standard FSA, FCM showed adequate diagnostic performance in detecting urethral and ureteral malignant involvement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".