Metastasis-directed Therapy in the Management of Urothelial Carcinoma: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In this prospectively registered meta-analysis (PROSPERO: CRD42024501283), we pooled data on patients treated with metastasis-directed therapies (MDTs) for metastatic urothelial cancer (mUC). METHODS: On January 24, 2024, we searched PubMed (MEDLINE), Scopus, and Google Scholar for studies on consolidative MDT in patients with mUC. The search was updated on August 25, 2024. Reports of MDT for brain metastases were excluded. The survival data were synthesised with a distribution-free approach using individual patient data extracted from Kaplan-Meier plots. The risk of bias was assessed using the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool. KEY FINDINGS AND LIMITATIONS: We included 19 retrospective and one prospective study, encompassing a total of 616 patients, published between 2003 and 2024. The median age ranged between 56 and 72 yr. Most patients were treated with surgical metastasectomy (73%), and approximately half received MDT as a first line of treatment for metastases. The pooled 2- and 5-yr overall survival (OS) rates were 64% (95% confidence interval [CI] 58-71%) and 38% (95% CI 33-45%). The median OS was 46.2 mo in patients treated for lung metastases (95% CI 28.7-62.6), 31.2 mo in those treated for lymph node metastases (95% CI 16.1-51.8), and 29 mo in those with mixed-location metastases (95% CI 23.8-38.4). The main limitations were heterogeneity, lack of data from comparative studies, and low quality of the evidence. CONCLUSIONS AND CLINICAL IMPLICATIONS: Many patients with mUC selected for MDT achieve long-term survival, particularly those with lung metastases. Although a causal association cannot be established, MDT emerges as a promising research direction, especially in combination with novel systemic therapies capable of eliciting deep, sustained responses.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".