Neoadjuvant Chemotherapy Prior to Trimodality Therapy for Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and objective: Neoadjuvant chemotherapy (NACT) before radical cystectomy improves outcomes in muscle-invasive bladder cancer (MIBC), but its value before trimodality therapy (TMT; maximal transurethral resection of the bladder tumor plus concurrent chemoradiation) is uncertain. This review aims to evaluate whether the addition of NACT to TMT is associated with improved survival in patients with MIBC. Methods: We systematically searched the MEDLINE, Embase, and Web of Science databases (February 2025). Eligible studies reported adjusted estimates for overall (OS), cancer-specific (CSS), or disease-free (DFS)/recurrence-free survival in patients undergoing TMT, comparing those who received versus those who did not receive NACT. Random-effect meta-analyses pooled multivariable hazard ratios (HRs). The risk of bias was assessed with ROBINS-I (PROSPERO registration: CRD420251120157). Key findings and limitations: = 116). Overall certainty is limited by nonrandomized design, heterogeneity in patient selection, TMT protocols and response definitions, and the modest number of adjusted studies. Conclusions and clinical implications: Across adjusted observational data, addition of NACT before TMT was not associated with a survival benefit. However, patients who respond to NACT experience a substantially better prognosis. Prospective trials testing modern neoadjuvant strategies-particularly immunotherapy- and targeted therapy-based regimens-within the standardized TMT protocols are warranted. Patient summary: We reviewed studies in which chemotherapy was given before a bladder-sparing approach, called trimodality therapy (tumor removal plus chemoradiation). Overall, this treatment approach did not improve survival, although patients who responded to the pretreatment did better. Newer drugs, including immunotherapy, should be tested in this setting.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".