Surveillance guidelines based on recurrence patterns for upper tract urothelial carcinoma
Bibliographic record
Abstract
INTRODUCTION: Upper tract urothelial carcinoma (UTUC) accounts for 5% of all urothelial tumours. Due to its rarity, evidence regarding postoperative surveillance is lacking. The objective of this study was to develop a post-radical nephroureterectomy (RNU) surveillance protocol based on recurrence patterns in a large, multi-institutional cohort of patients. METHODS: Retrospective clinical and pathological data were collected from 1029 patients undergoing RNU over a 15-year period (1994-2009) at 10 Canadian academic institutions. A multivariable model was used to identify prognostic clinicopathological factors, which were then used to define risk categories. Risk-based surveillance guidelines were proposed based on actual recurrence patterns. RESULTS: Overall, 555 (49.9%) patients developed recurrence, including 289 (25.9%) in the urothelium and 266 (23.9%) with loco-regional and distant recurrences. Based on multivariable analysis, three risk groups were identified: 1) low-risk patients with pTa-T1, pN0 disease, and no adverse histological features (high tumour grade, lymphovascular invasion [LVI], tumour multifocality); 2) intermediate-risk patients with pTa-T1, pN0 disease with one or more of the adverse histological features; and 3) high-risk patients with a ≥pT2 tumour and/or nodal involvement. Low-, intermediate-, and high-risk patients were free of urothelial recurrence at three years in 72%, 66%, and 63%, respectively, and free of regional/distant recurrence in 93%, 87%, and 62%, respectively. The risks of loco-regional and distant recurrences (p<0.0001) and time to death (p<0.0001) were significantly different between the low-, intermediate-, and high-risk patients. CONCLUSIONS: Based on recurrence patterns in a large, multicentre patient cohort, we have proposed an evidence-based, risk-adapted post-RNU surveillance protocol.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".