Is histological grade a useful parameter in muscle-invasive urothelial bladder cancer? Results from a multicenter study on the impact of different grading systems on disease-free survival after upfront radical cystectomy
Bibliographic record
Abstract
PURPOSE: The prognostic value of histopathological grade in muscle-invasive urothelial carcinoma (MIBC) to predict disease-specific survival (DSS) is understudied. While grading systems like WHO1973 and WHO2004 are established in non-muscle-invasive bladder cancer (NMIBC), their relevance in MIBC remains controversial. This study assessed the prognostic impact of histopathological grade on DSS in a multicenter cohort. METHODS: We included 1,123 cN0M0 MIBC patients treated with upfront radical cystectomy (1987–2020) at nine centers. Tumors were graded using WHO1973 (G1 + G2 combined as G1/2 due to low numbers vs. G3), WHO2004 (low-grade [LG] vs. high-grade [HG]), and a hybrid three-tier system. Slides were locally reviewed by uro-pathologists. DSS was analyzed using Kaplan-Meier and Cox models, adjusting for age, stage, lympho-vascular invasion, surgical margins, lymph-node status, adjuvant chemotherapy, treatment center, and era of cystectomy. RESULTS: Among all cases, 74 (6.6%) were G1/2 and 1,049 (93.4%) G3; 27 (2.4%) were LG and 1,096 (97.6%) HG. Median follow-up was 5.3 years (IQR 2.9–8.5). Univariable analyses showed significantly better DSS for LG and G1/2 tumors across grading systems. However, multivariable models showed no independent association between grade and DSS. CONCLUSION: Although LG and G1/2 MIBC tumors demonstrated superior DSS in univariable analyses, the lack of independent prognostic significance in multivariable models questions the relevance of histopathological grade in MIBC. Further studies should explore the clinical utility of grade, define new grading schemes including features of epithelial-mesenchymal transition or tumor microenvironment, and explore alternative prognostic (bio)markers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".