Advanced Urologic Cancer Consensus Conference (AUC3) 2025: Expert consensus on the management of renal cell and urinary tract cancers
Bibliographic record
Abstract
The therapeutic landscape for renal cell carcinoma (RCC) and urinary tract cancer (UTC) has transformed dramatically, creating complexity in treatment selection and sequencing. The 2025 Advanced Urologic Cancer Consensus Conference was convened to establish evidence-based expert consensus recommendations for optimal management. A multidisciplinary panel of 51 experts participated in a modified Delphi process addressing questions developed through iterative consensus-building covering RCC and UTC management. Voting occurred before and after the conference, and analyses focused on postmeeting responses. Consensus was defined as ≥75% agreement, with strong consensus as >90%. Strong consensus was found on the use of adjuvant pembrolizumab for higher risk RCC (pathologic T2 [pT2], grade 4; pT3-pT4, any grade; pTXN1; or fully resected metastatic disease) and on neoadjuvant therapy before cystectomy for localized UTC. There was strong consensus on the use of enfortumab vedotin plus pembrolizumab as frontline therapy for metastatic UTC and the use of platinum-based chemotherapy postprogression in biomarker-negative UTC. For RCC, there was consensus on the role of single-agent vascular endothelial growth factor receptor-tyrosine kinase inhibitor therapy after progression on frontline immune checkpoint inhibitor/vascular endothelial growth factor receptor-tyrosine kinase inhibitor therapy or dual immune checkpoint inhibitor therapy. However, there was a lack of consensus on other critical areas in the management of RCC and UTC. The 2025 Advanced Urologic Cancer Consensus Conference provides evidence-informed guidance for complex clinical scenarios while identifying critical research priorities. The group recognizes that the lack of consensus across multiple areas highlights the need for improved patient selection and prospective studies enabling optimal combination and sequencing approaches. This iterative annual process will address evolving treatment paradigms to optimize outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".