The Role of c-MET as a Biomarker in Patients with Bladder Cancer Treated with Radical Chemo-Radiotherapy
Bibliographic record
Abstract
BACKGROUND: Bladder cancer is a highly aggressive cancer, and muscle invasive urothelial carcinoma (MIUC) requires aggressive strategy. Concomitant chemo-radiotherapy (CRT) appears as a therapeutic option that allows bladder sparing. No biomarker is currently available to optimally select patients for CRT. METHODS: We retrospectively enrolled patients with MIUC who were treated in a curative setting with CRT. Based on c-MET expression in pre-treatment tumor tissue, patients were stratified into two groups: no expression of c-MET (group A) and expression of c-MET (group B). We evaluated the outcome of these patients based on c-MET expression. RESULTS: After a median follow-up of 40 months, 13 patients were enrolled in this analysis, 8 in group A and 5 in group B. The disease recurrence was 25% in group A and 100% in group B. Compared to group A, patients from group B experienced more frequent and more rapid recurrence in terms of metastases; the 3-year metastatic recurrence rate was 13% and 100%, respectively. The c-MET expression was also associated with a higher rate of cancer-related deaths. CONCLUSIONS: In this retrospective analysis, c-MET expression was associated with worse disease-free survival and survival in patients treated radically with CRT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".