Identifying the Optimal Number of Neoadjuvant Chemotherapy Cycles in Patients with Muscle Invasive Bladder Cancer
Bibliographic record
Abstract
PURPOSE: We investigated the pathological response rates and survival associated with 3 vs 4 cycles of cisplatin-based neoadjuvant chemotherapy (NAC) in patients with cT2-4N0M0 muscle invasive bladder cancer. MATERIALS AND METHODS: In this cohort study we analyzed clinical data of 828 patients treated with NAC and radical cystectomy between 2000 and 2020. A total of 384 and 444 patients were treated with 3 and 4 cycles of NAC, respectively. Pathological objective response (pOR; ypT0-Ta-Tis-T1 N0), pathological complete response (pCR; ypT0 N0), cancer-specific survival and overall survival were investigated. RESULTS: pOR and pCR were achieved in 378 (45%; 95% CI 42, 49) and 207 (25%; 95% CI 22, 28) patients, respectively. Patients treated with 4 cycles of NAC had higher pOR (49% vs 42%, p=0.03) and pCR (28% vs 21%, p=0.02) rates compared to those treated with 3 cycles. This effect was confirmed on multivariable logistic regression analysis (pOR OR 1.46 p=0.008, pCR OR 1.57, p=0.007). On multivariable Cox regression analysis, 4 cycles of NAC were significantly associated with overall survival (HR 0.68; 95% CI 0.49, 0.94; p=0.02) but not with cancer-specific survival (HR 0.72; 95% CI 0.50, 1.04; p=0.08). CONCLUSIONS: Four cycles of NAC achieved better pathological response and survival compared to 3 cycles. These findings may aid clinicians in counseling patients and serve as a benchmark for prospective trials. Prospective validation of these findings and assessment of cumulative toxicity derived from an increased number of cycles are needed.
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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 | 0.003 |
| 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) | 0.000 | 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".