Multi-Institutional Validation of the Predictive Value of Ki-67 Labeling Index in Patients With Urinary Bladder Cancer
Bibliographic record
Abstract
Several small single-center studies have reported a prognostic role for Ki-67 labeling index in advanced urothelial carcinoma of the urinary bladder. To investigate whether Ki-67 was a useful biomarker of oncological outcome after radical cystectomy for urothelial carcinoma, we assessed its expression in tumor tissue from 713 patients treated with radical cystectomy and bilateral lymphadenectomy at six centers. A high Ki-67 labeling index was independently associated with established features of aggressive urothelial carcinoma, disease recurrence, and cancer-specific survival. Addition of Ki-67 labeling index improved the accuracy of standard multivariate outcome prediction models, as measured by Harrell concordance index, by 2.9% for disease recurrence and 2.4% for bladder cancer-specific survival (P < .001, two-sided Mantel-Haenszel) -- a statistically and potentially clinically significant margin. In conclusion, routine assessment of Ki-67 expression status along with assessment of other established predictors of urothelial carcinoma outcome has the potential to improve identification of patients who are at increased risk for disease progression after radical cystectomy and thus may benefit from perioperative systemic chemotherapy.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".