Immunostaining as a Diagnostic Aid in Cytopathologic Study of Upper Urinary Tract Urothelial Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To evaluate preoperative diagnosis of low-grade urothelial carcinoma (LGUC) and urothelial neoplasms of unknown malignant potential (UMP UN) of the upper urinary tract (UUT) and its role in disease management, especially in the context of nephron-sparing treatment possibilities. STUDY DESIGN: Wash and brush ureteral specimens of LGUC/UMP UN of the UUT with histopathologic correlation were retrieved at our institution for 7 years and studied along with 7 ureteral specimens from nonneoplastic ureteral lesions. RESULTS: Of 30 specimens from 25 LGUC/UMP UN, 5 were negative for tumor cells and 3 showed cytologic atypia. The remaining 22 contained tumor cells with characteristic features of urothelial carcinoma, including hard and soft criteria. The 4 hard criteria included branching stromal cores, dyshesive cell networks, 3-dimensional papillary clusters with stromal core and atypia associated with CK20-positive cells. The 2 soft criteria were hypercellularity and atypia in CK20-negative cells. All LGUC/UMP UN of the UUT were associated with at least 1 hard criterion or both soft criteria. CONCLUSION: Branching stromal cores, 3-dimensional papillary clusters, dyshesive cell networks and CK20-positive atypia immunostaining appear specific for LGUC/UMP UN of the UUT but are seen in few cases. Combined soft and hard criteria will increase sensitivity to 83%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".