Multiple biomarkers improve prediction of bladder cancer recurrence and mortality in patients undergoing cystectomy
Bibliographic record
Abstract
BACKGROUND: Tested was whether the assessment of 5 established bladder cancer biomarkers (p53, pRB, p21, p27, and cyclin E1) could improve the ability to predict disease recurrence and cancer-specific survival after radical cystectomy in patients with pTa-3N0M0 urothelial carcinoma of the bladder (UCB). METHODS: The study comprised 191 patients with pTa-3N0M0 UCB treated with radical cystectomy and bilateral lymphadenectomy (median follow-up, 3.1 years). Biomarker expression was assayed on serial tissue microarray slides using quantitative immunohistochemistry using advanced cell imaging and color detection software. Predictive accuracy was quantified using the concordance index and 200-bootstrap resamples were used to reduce overfit bias. Bootstrap-adjusted predictive accuracy estimates were compared using the Mantel-Haenszel test. RESULTS: UCB recurred in 36 (18.8%) patients and 30 (15.7%) died of bladder cancer; 157 (82.2%) patients had altered expression of at least 1 biomarker. In univariate analyses the number of altered biomarkers had the highest predictive accuracy for both disease recurrence (76.8%, P< .001) and cancer-specific mortality (78.3%, P< .001). Addition of the number of altered biomarkers increased the predictive accuracy of nomograms based on the TNM staging system for disease recurrence and cancer-specific mortality by 10.9% (83.4% vs 72.5%, P< .001) and 8.6% (86.9% vs 78.3, P< .001), respectively. CONCLUSIONS: Assessment of the number of altered biomarkers in the cystectomy specimen improves the prediction of bladder cancer recurrence and survival in patients with pTa-3N0M0 disease. Prospective evaluation of alteration in these biomarkers can help identify patients who would benefit from adjuvant treatment after radical cystectomy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".