Diagnostic challenges and treatment strategies in the management of upper-tract urothelial carcinoma
Bibliographic record
Abstract
Upper-tract urothelial carcinoma (UTUC) is a rare disease, posing many challenges for the treating physician due to the lack of strong evidence-based recommendations. However, novel molecular discoveries and a better understanding of the clinical behavior of the disease lead to a continuous evolution of therapeutic landscape in UTUC. The aim of the review is to provide a comprehensive update of the current diagnostic modalities and treatment strategies in UTUC with a special focus on recent developments and challenges. A comprehensive literature search including relevant articles up to August 2020 was performed using the MEDLINE/PubMed database. Despite several technological improvements, accurate staging and outcome prediction remain major challenges and hamper appropriate risk stratification. Kidney-sparing surgery can be offered in low risk UTUC; however, physician and patient must be aware of the high rate of recurrence and risk of progression due to tumor biology and understaging. The value and efficacy of intracavitary therapy in patients with UTUC remains unclear due to the lack of high-quality data. In high-risk diseases, radical nephroureterectomy with bladder cuff excision and template lymph node dissection is the standard of care. Perioperative systemic chemotherapy is today accepted as a novel standard for advanced cancers. In metastatic or unresectable disease, the therapeutic landscape is rapidly changing due to several novel agents, such as checkpoint inhibitors. While several diagnostic and treatment challenges remain, progress in endoscopic technology and molecular knowledge have ushered a new age in personalized management of UTUC. Novel accurate molecular and imaging biomarkers are, however, still needed to guide decision making as tissue acquisition remains suboptimal. Next generation sequencing and novel agents are promising to rapidly improve patient outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".