Neoadjuvant systemic therapy in patients undergoing nephroureterectomy for urothelial cancer: a multidisciplinary systematic review and critical analysis
Bibliographic record
Abstract
INTRODUCTION: The benefit of neoadjuvant systemic therapy (NAST) is not yet supported by randomized controlled trials in upper tract urothelial carcinoma (UTUC), but the evidence is increasing. This narrative systematic review was conducted to evaluate the available evidence on the role of NAST in patients undergoing radical nephroureterectomy (RNU) for UTUC. EVIDENCE ACQUISITION: We searched for all relevant articles or conference abstracts published and indexed in PubMed, Embase, and Scopus on July 19, 2021. The study was reported according to the PRISMA criteria and designed within the PICOS framework. We included studies comparing patients with non-metastatic UTUC who received neoadjuvant chemotherapy (NAC) or immunotherapy (NAI) with patients who underwent definitive surgery alone or surgery plus adjuvant systemic therapy. Prospective uncontrolled studies were also included. EVIDENCE SYNTHESIS: We identified 27 reports (NAC, N.=24 and NAI, N.=3) published between 2010 and 2021. Twenty of the 24 studies on NAC were retrospective comparative analyses, whereas the remaining four were prospective single-arm studies. One of the three NAI studies exclusively enrolled patients with UTUC. NAC was associated with improved survival and better pathological response relative to surgery alone, but there was no clear advantage when compared to surgery plus adjuvant chemotherapy. Overall, the drug-induced toxicity and risk of disease progression were acceptable but the inherent bias across study designs, inadequate reporting and heterogeneous definition of primary outcomes render it difficult to synthesize results, compare centers, and inform practice. CONCLUSIONS: The current level of evidence supporting NAST for UTUC is relatively low and the inability to predict responsiveness and thereby pinpoint the optimal candidates remains a major challenge. There is a need to compare NAST to adjuvant therapies using clearly defined primary endpoints as minimum reporting standards developed by a multidisciplinary team.
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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 | 0.031 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".