Biological and prognostic implications of biopsy upgrading for high‐grade upper tract urothelial carcinoma at nephroureterectomy
Bibliographic record
Abstract
OBJECTIVES: Technical limitations of ureteroscopic (URS) biopsy has been considered responsible for substantial upgrading rate in upper tract urothelial carcinoma (UTUC). However, the impact of tumor specific factors for upgrading remain uninvestigated. METHODS: Patients who underwent URS biopsy were included between 2005 and 2020 at 13 institutions. We assessed the prognostic impact of upgrading (low-grade on URS biopsy) versus same grade (high-grade on URS biopsy) for high-grade UTUC tumors on radical nephroureterectomy (RNU) specimens. RESULTS: This study included 371 patients, of whom 112 (30%) and 259 (70%) were biopsy-based low- and high-grade tumors, respectively. Median follow-up was 27.3 months. Patients with high-grade biopsy were more likely to harbor unfavorable pathologic features, such as lymphovascular invasion (p < 0.001) and positive lymph nodes (LNs; p < 0.001). On multivariable analyses adjusting for the established risk factors, high-grade biopsy was significantly associated with worse overall (hazard ratio [HR] 1.74; 95% confidence interval [CI], 1.10-2.75; p = 0.018), cancer-specific (HR 1.94; 95% CI, 1.07-3.52; p = 0.03), and recurrence-free survival (HR 1.80; 95% CI, 1.13-2.87; p = 0.013). In subgroup analyses of patients with pT2-T4 and/or positive LN, its significant association retained. Furthermore, high-grade biopsy in clinically non-muscle invasive disease significantly predicted upstaging to final pathologically advanced disease (≥pT2) compared to low-grade biopsy. CONCLUSIONS: High tumor grade on URS biopsy is associated with features of biologically and clinically aggressive UTUC tumors. URS low-grade UTUC that becomes upgraded to high-grade might carry a better prognosis than high-grade UTUC on URS. Tumor specific factors are likely to be responsible for upgrading to high-grade on RNU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".