Gemcitabine and cisplatin neoadjuvant chemotherapy for muscle-invasive urothelial carcinoma: Predicting response and assessing outcomes.
Bibliographic record
Abstract
336 Background: To evaluate gemcitabine-cisplatin (GC) neoadjuvant chemotherapy (NAC) for pathologic response (pR) and cancer-specific outcomes following radical cystectomy (RC) for muscle-invasive bladder cancer (MIBC) and identify clinical parameters associated with pR. Methods: We studied 150 consecutive cases of MIBC that received GC NAC followed by open RC (2000-2013). A cohort of 121 patients treated by RC alone was used for comparison. Pathologic response and cancer-specific survival (CSS) were compared. We created the Johns Hopkins Hospital Dose Index (JHH-DI) to characterize chemotherapeutic dosing regimens and accurately assess sufficient neoadjuvant dosing regarding patient tolerance. Results: No significant difference was noted in 5-year CSS between GC NAC (58%) and non-NAC cohorts (61%). The median follow-up was 19.6 months (GC NAC) and 106.5 months (non-NAC). Patients with residual non-muscle-invasive disease after GC NAC exhibit similar 5-year CSS relative to patients with no residual carcinoma (p=0.99). NAC pR (≤pT1) demonstrated improved 5-year CSS rates (90.6% vs. 27.1%, p<0.01) and decreased nodal positivity rates (0% vs. 41.3%, p<0.01) compared to non-responders (≥pT2). Clinicopathologic outcomes were inferior in NAC pathologic non-responders (pNR) compared to the entire RC-only treated cohort. A lower pNR rate was seen in patients tolerating sufficient dosing of NAC as stratified by the JHH-DI (p=0.049), congruent with NCCN guidelines. A multivariate decision tree model demonstrated age ≤60 years and clinical stage cT2 as significant of NAC response (p<0.05). Conclusions: Pathologic non-responders fare worse than patients proceeding directly to RC alone. Multiple predictive models incorporating clinical, histopathologic, and molecular features are currently being developed to identify patients most likely to benefit from GC NAC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".