Multicenter Validation of Histopathologic Tumor Regression Grade After Neoadjuvant Chemotherapy in Muscle-invasive Bladder Carcinoma
Bibliographic record
Abstract
Response classification after neoadjuvant chemotherapy in muscle-invasive bladder carcinoma is based on the TNM stage at radical cystectomy. We recently showed that histopathologic tumor regression grades (TRGs) add prognostic information to TNM. Our aim was to validate the prognostic significance of TRG in muscle-invasive bladder cancer in a multicenter setting. We enrolled 389 patients who underwent cisplatin-based chemotherapy before radical cystectomy in 8 centers between 2010 and 2016. Median follow-up was 2.2 years. TRG was determined in radical cystectomy specimens by local pathologists. Central pathology review was conducted in 20% of cases, which were randomly selected. The major response was defined as ≤pT1N0. The remaining patients were grouped into partial responders (≥ypT2N0-3 and TRG 2) and nonresponders (≥ypT2N0-3 and TRG 3). TRG was successfully determined in all cases, and interobserver agreement in central pathology review was high (κ=0.83). After combining TRG and TNM, 47%, 15%, and 38% of patients were major, partial, and nonresponders, respectively. Combination of TRG and TNM showed significant prognostic discrimination of overall survival (major responder: reference; partial responder: hazard ratio 3.5 [95% confidence interval: 1.8-6.8]; nonresponder: hazard ratio 6.1 [95% confidence interval: 3.6-10.3]). This discrimination was superior compared with TNM staging alone, supported by 2 goodness-of-fit criteria (P=0.041). TRG is a simple, reproducible histopathologic measurement of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer. Integrating TRG with TNM staging resulted in significantly better prognostic stratification than TNM staging alone.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".