Canadian experience of neoadjuvant chemotherapy on bladder recurrences in patients managed with trimodal therapy for muscle-invasive bladder cancer
Bibliographic record
Abstract
INTRODUCTION: Bladder preservation with trimodal therapy (TMT) has emerged as a feasible alternative to radical cystectomy in patients with muscle-invasive bladder cancer. Neoadjuvant chemotherapy (NAC) was proven to cause pathological downstaging. For this reason, we evaluated whether receipt of NAC decreases local bladder recurrences in TMT patients. METHODS: We retrospectively analyzed our TMT database for all patients treated between 2003 and 2017. Patients were treated with maximal transurethral resection of bladder tumor (TURBT) followed by chemotherapy/radiotherapy with or without NAC. Baseline demographic and tumor characteristics were recorded. Rates of local and systemic recurrence were analyzed per receipt of NAC. Overall recurrence-free survival (RFS) and bladder (b)RFS were analyzed using the Kaplan-Meier method and Cox proportional hazards modelling. RESULTS: Median age and followup periods were 72 years and 3.6 years, respectively. Fifty-four patients had NAC and concurrent chemoradiation (NAC-TMT) vs. 70 patients who had concurrent chemoradiation only (TMT). Carcinoma in situ (CIS) was present in 31% of the patients in NAC-TMT group compared to 24% in TMT group (p=0.40). After treatment, 24 (44%) and 31 (44%) patients in NAC-TMT and TMT groups, respectively, had bladder tumor recurrence. Overall RFS at three years was 46% and 50% in NAC-TMT and TMT groups, respectively (p=0.70). BRFS at three years was 55% and 69% in NAC-TMT and TMT groups, respectively (p=0.27). Multivariable analyses found that the presence of concomitant CIS (hazard ratio [HR] 2.13; 95% confidence interval CI 1.06-4.27; p=0.0036) was the primary factor associated with local bladder recurrence. CONCLUSIONS: Receipt of NAC does not obviate the risk of bladder recurrence post-TMT. Patients with CIS should be monitored especially closely for local recurrence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".