Clinical Significance of Tumor Location for Ureteroscopic Tumor Grading in Upper Tract Urothelial Carcinoma
Bibliographic record
Abstract
Background: Although previous literature shows tumor location as a prognostic factor in upper tract urothelial carcinoma (UTUC), there remains uninvestigated regarding the impact of tumor location on grade concordance and discrepancies between ureteroscopic (URS) biopsy and final radical nephroureterectomy (RNU) pathology. Methods: In this international study, we retrospectively reviewed the records of 1,498 patients with UTUC who underwent diagnostic URS with concomitant biopsy followed by RNU between 2005 and 2020. Tumor location was divided into four sections: the calyceal-pelvic system, proximal ureter, middle ureter, and distal ureter. Patients with multifocal tumors were excluded from the study. We performed multiple comparison tests and logistic regression analyses. Results: Overall, 1,154 patients were included; 54.4% of those with low-grade URS biopsies were upgraded on RNU. In the multiple comparison tests, middle ureter tumors exhibited the highest probability of upgrading, meanwhile pelvicalyceal tumors exhibited the lowest probability of upgrading (73.7% vs 48.5%, p = 0.007). Downgrading was comparable across all tumor locations. On multivariate analyses, middle ureteral location was significantly associated with a low probability of grade concordance (odds ratio [OR] 0.59; 95% confidence interval [CI], 0.35–1.00; p = 0.049) and an increased risk of upgrading (OR 2.80; 95% CI, 1.20–6.52; p = 0.017). The discordance did not vary regardless of caliceal location, including the lower calyx. Conclusions: Middle ureteral tumors diagnosed to be low grade had a high probability to be undergraded. Our data can inform providers and their patients regarding the likelihood of undergrading according to tumor location, facilitating patient counseling and shared decision making regarding the choice of kidney sparing vs RNU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".