Association of tumor hypoxia with lower survival after radiotherapy for muscle-invasive bladder cancer.
Bibliographic record
Abstract
292 Background: Hypoxia may influence clinical outcomes in many human tumors, including muscle invasive urothelial carcinoma (UC) of the bladder. Hypoxia inducible factor-1 (HIF1), glucose transporter protein-1 (GLUT1) and carbonic anhydrase IX (CAIX) are all upregulated by hypoxia. This study examined the relationship between biomarkers of hypoxia or proliferation (Ki67) and clinical outcome in UC patients treated with radiotherapy (RT). Methods: 70 patients were treated with curative intent using RT alone or RT plus neoadjuvant or concurrent chemotherapy (RTCT) between 1997 and 2006. Archived, paraffin-embedded, transurethral biopsy material was available for 44 patients (35 men and 9 women). Tumor was clinically confined to the bladder wall (cT2) in 28 patients and involved perivesical tissues or adjacent organs (cT3/T4) in 16. Tissue micro-arrays were constructed and immunohistochemical analyses performed to assess biomarker expression, which was scored independently by 3 observers in a semi-quantitative manner. The biomarker scores were correlated with clinico-pathologic data extracted from the medical record and with patient outcome. Median follow-up was 4.2 years. Results: HIF1, GLUT1 and CAIX expression was seen in 43%, 96% and 65% of tumors respectively. The median Ki67 expression was 60%. There were no correlations between biomarkers expression and clinico-pathologic prognostic factors. The cystoscopic complete response rate was 71% at 2-3 months post-RT. The 4-year actuarial local control (LC) and overall survival (OS) rates were 48% and 55% respectively. High Ki67 expression was strongly predictive of better LC by univariate and multivariate analyses (70% vs 20% at 4 years, HR 0.95, p= 0.002), whereas no hypoxic marker predicted LC. High CAIX expression was predictive of inferior OS by multivariate analysis (HR 1.6, p=0.04). Conclusions: Tumor hypoxia influences the outcome of patients with bladder cancer treated with RT or RTCT and is a potential therapeutic target. High Ki67 has been reported previously to be a marker of sustained LC in the bladder following RTCT but the biologic mechanism underlying this association is unknown and requires further investigation.
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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 | 0.000 | 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) | 0.001 | 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".