Impact of neoadjuvant chemotherapy on survival prognosis and pathological downstaging in patients presenting with high-risk upper tract urothelial carcinoma
Bibliographic record
Abstract
PURPOSE: Controversy remains with using neoadjuvant chemotherapy (NAC) in patients with upper tract urothelial carcinoma (UTUC). Thus, we conducted a systematic review and meta-analysis of the peer-reviewed literature to assess the role of NAC in high-risk UTUC patients. METHODS: PubMed, EMBASE, and the Cochrane Library were the main sources of relevant studies. The search terms included: "Upper urothelial carcinoma"; "UTUC"; "Urinary tract cancer"; and "Transitional cell carcinoma" combined with "Neoadjuvant chemotherapy" and "adjuvant chemotherapy". We studied the relationship of UTUC and NAC. Overall survival, recurrent-free survival, cancer-specific survival and pathological response rate in patients treated with/without NAC before radical surgery were evaluated. RESULTS: Five trials were selected, and included 532 participants. Each of the included studies was retrospective. The combined analysis showed that when compared to controls, the pooled hazard ratios of overall survival, recurrence-free survival and cancer-specific survival were 0.47 (95% confidence interval [CI], 0.34-0.64; P < .00001); 0.50 (95% CI, 0.37-0.66; P < .00001); and 0.37 (95% CI, 0.25-0.54; P < .00001), respectively. It indicates the beneficial effects of NAC for the prognosis of survival in UTUC. Additionally, NAC was significantly associated with T-stage down-grading (T3/4 to ≤T2; OR=7.58 [4.66, 12.33]; P < .0001) and pathological lymph node status (N+ to N0; OR = 6.24 [2.57,15.15]; P < .00001). CONCLUSIONS: NAC treatment before radical nephron-ureterectomy significantly improves survival prognosis in patients with high-risk upper tract urothelial carcinoma. However, considerable prospective and randomized studies are needed to confirm this perspective.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".