Discordance Between Clinical and Pathological Staging and Grading in Upper Tract Urothelial Carcinoma
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate the concordance in tumor stage and grade between ureteroscopic (URS) biopsy and radical nephroureterectomy (RNU) in patients with upper tract urothelial carcinoma (UTUC). PATIENTS AND METHODS: Records of 1,214 UTUC patients who had undergone URS biopsy followed by RNU were included. Univariable and multivariable logistic regression analyses were performed to identify factors contributing to the pathological upstaging. RESULTS: The concordance between URS biopsy-based clinical and RNU pathological staging was 34.5%. Clinical understaging occurred in 59.5% patients. Upstaging to muscle-invasive disease occurred in 240 (41.7%) of 575 patients diagnosed with ≤cT1 disease. Of those diagnosed with muscle-invasive disease on final pathology, 89.6% had been clinically diagnosed with ≤cT1 disease. In the univariable analyses, computed tomography urography (CTU)-based invasion, ureter location, hydronephrosis, high-grade cytology, high-grade biopsy, sessile architecture, age, and women sex were significantly associated with pathological upstaging (P < .05). In the multivariable analyses, CTU-based invasion and hydronephrosis remained associated with pathological upstaging (P < .05). URS biopsy-based clinical and pathological gradings were concordant in 634 (54.2%) patients. Clinical undergrading occurred in 496 (42.4%) patients. CONCLUSIONS: Clinical understaging/undergrading and upstaging to muscle-invasive disease occurred in a high proportion of UTUC patients undergoing RNU. Despite the inherent selection bias, these data underline the challenges of accurate UTUC staging and grading. In daily clinical practice, URS biopsy and CTU offer the most accurate preoperative information albeit with limited predictive value when used alone. These findings should be considered when utilizing preoperative, risk-adapted strategies.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".