Long-Term Recurrence Risk, Metastatic Potential, and Length of Cystoscopic Surveillance of Low-Grade Nonmuscle-Invasive Bladder Cancer
Bibliographic record
Abstract
PURPOSE: Patients with Ta low-grade (LG) nonmuscle-invasive bladder cancer (NMIBC) rarely develop metastases or die of it. Long-term data are scant and length of follow-up poorly defined. MATERIALS AND METHODS: This retrospective study included 521 patients diagnosed with primary TaLG NMIBC (n = 491) or papillary urothelial neoplasm of low malignant potential (n = 30) from 1989 to 2019 at an academic center. Patient data were acquired using patient records chart review and a bladder cancer informatics registry at the center. Risk of recurrence and progression in stage to muscle invasion, metastases, and death due to bladder cancer (BC) were analyzed. RNAseq assessed the transcriptomic profiles of 4 TaLG NMIBCs that metastasized. Interobserver variability in pathological grading (WHO 2004/2022 and 1973, n = 80) was blindly assessed by 3 expert pathologists. RESULTS: The median follow-up was 9.6 (95% CI: 8.6-10.2) years. Among 521 patients (73% men, median age 67.0 years), 350 recurred, 57 progressed in stage, 20 developed metastases, and 15 died of BC (median 9.6 years after diagnosis). Cancer-specific survival probabilities were 0.99, 0.98, and 0.96 at 5, 10, and 15 years, respectively. Fifty patients who were recurrence free for the first 5 years developed late recurrences and 2 of them died of BC. Metastatic TaLG NMIBC had more adverse transcriptomic findings in keeping with higher-grade tumors despite being phenotypically similar to indolent tumors. Grading concordance for the 2004/2022 system and WHO 1973 was 0.78 (95% CI: 0.65-0.90) and 0.41 (95% CI: 0.32-0.50), respectively. CONCLUSIONS: This study with long-term data challenges the assumption that primary TaLG NMIBC nearly never progresses to lethal disease if followed long enough. However, the risk of BC-related mortality is extremely low in patients who are recurrence free for the first 5 years. Minimizing variability in pathological grading remains an unmet need.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".