Proposal for a Novel Histological Scoring System as a Potential Grading Approach for Muscle-invasive Urothelial Bladder Cancer Correlating with Disease Aggressiveness and Patient Outcomes
Bibliographic record
Abstract
BACKGROUND: Grading of muscle-invasive bladder cancer (MIBC) according to the current World Health Organization (WHO) criteria is controversial due to its limited prognostic value. All MIBC cases except a tiny minority are of high grade. OBJECTIVE: To develop a prognostic histological scoring system for MIBC integrating histomorphological phenotype, stromal tumor-infiltrating lymphocytes (sTILs), tumor budding, and growth and spreading patterns. DESIGN, SETTING, AND PARTICIPANTS: Tissue specimens and clinical data of 484 patients receiving cystectomy and lymphadenectomy with curative intent with or without adjuvant chemotherapy. Histomorphological phenotypes, sTILs, tumor budding, and growth and spreading patterns were evaluated and categorized into four grade groups (GGs). GGs were correlated with molecular subtypes, immune infiltration, immune checkpoint expression, extracellular matrix (ECM) remodeling, and epithelial-mesenchymal transition (EMT) activity. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: GGs were associated with overall (OS), disease-specific (DSS), and progression-free (PFS) survival in univariable and multivariable analyses. Association with biological features was analyzed with descriptive statistics. RESULTS AND LIMITATIONS: Integration of two histomorphological tumor groups, three sTILs groups, three tumor budding groups, and four growth/spread patterns yielded four novel GGs that had high significance in the univariable survival analysis (OS, DSS, and PFS). GGs were confirmed as independent prognostic predictors with the greatest effect in the multivariable Cox regression analysis. Correlation with molecular data showed a gradual transition from basal to luminal subtypes from GG1 to GG4; a gradual decrease in survival, immune infiltration, and immune checkpoint activity; and a gradual increase in ECM remodeling and EMT activity. CONCLUSIONS: We propose a novel, prognostically relevant, and biologically based scoring system for MIBC in cystectomies applicable to routine pathological sections. PATIENT SUMMARY: We developed a novel approach to assess the aggressiveness of advanced bladder cancer, which allows improved risk stratification compared with the method currently proposed by the World Health Organization.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".