Ki‐67 index enhances the prognostic accuracy of the urothelial superficial bladder carcinoma risk group classification
Bibliographic record
Abstract
Approximately 80% of bladder tumors are urothelial superficial papillary carcinomas (USPC). Despite a generally good prognosis, these tumors have a strong propensity to recur and about 1/3 of them compared to disease progression. Histological assessment of these superficial tumors is not sufficiently discriminator in predicting prognosis; therefore, we decided to evaluate the prognostic significance of p53 and Ki-67 immunoexpression in low-grade (GI-II) USPC in order to predict the potential outcome of these tumors. P53 and Ki-67 immunoexpression were studied in function of recurrence-free and progression-free survival in 159 primary superficial bladder tumors. A prognostic risk model based on grade, stage and multifocality was also evaluated. P53 accumulation was significantly related to tumor progression (p=0.006). High Ki-67 index (>/=18%) and multifocality were significantly related to recurrence (both p=0.0001) and progression-free survival (both p=0.0001) and were independent prognostic factors in the multivariate analysis. The prognostic risk model based on grade, stage and multifocality was not an efficient discriminator of outcome. Adding the Ki-67 index into the risk model, single pTa/T1-GI Ki-67 positive tumors, usually classified as low risk, were reclassified as of intermediate risk. After this reclassification, the risk group model identified a subgroup of pTa/T1-G1 with a high risk of recurrence and progression. Ki-67 index is a reliable prognostic marker in urothelial superficial bladder carcinoma and, when included into a risk profile classification of the low-grade USPC, the accuracy of the prognostic discrimination is enhanced.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".