Follow-up strategies after trimodal treatment for muscle-invasive bladder cancer: a systematic review
Bibliographic record
Abstract
PURPOSE: Optimal follow-up strategies following trimodal treatment for muscle invasive bladder cancer play a crucial role in detecting and managing relapse and side-effects. This article provides a comprehensive summary of the patterns and risk factors of relapse, functional outcomes, and follow-up protocols. METHODS: A systematic literature search on PubMed and review of current guidelines and institutional follow-up protocols after trimodal therapy were conducted. RESULTS: Out of 200 identified publications, 43 studies (28 retrospective, 15 prospective) were selected, encompassing 7447 patients (study sizes from 24 to 728 patients). Recurrence rates in the urinary bladder varied between 14-52%; 3-16% were muscle-invasive while 11-36% were non-muscle invasive. Nodal recurrence occurred at 13-16% and distant metastases at 15-35%. After 5 and 10 years of follow-up, around 60-85% and 45-75% of patients could preserve their bladder, respectively. Various prognostic risk factors associated with relapse and inferior survival were proposed, including higher disease stage (> c/pT2), presence of extensive/multifocal carcinoma in situ (CIS), hydronephrosis, multifocality, histological subtypes, incomplete transurethral resection of bladder tumor (TURBT) and incomplete response to radio-chemotherapy. The analyzed follow-up guidelines varied slightly in terms of the number, timing, and types of investigations, but overall, the recommendations were similar. CONCLUSION: Randomized prospective studies should focus on evaluating the impact of specific follow-up protocols on oncological and functional outcomes following trimodal treatment for muscle-invasive bladder cancer. It is crucial to evaluate personalized adaption of follow-up protocols based on established risk factors, as there is potential for improved patient outcomes and resource allocation.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".