Ki-67 expression predicts radiotherapy failure in early glottic cancer.
Bibliographic record
Abstract
BACKGROUND: Early-stage laryngeal squamous cell carcinoma is managed with radiotherapy or endoscopic surgery. Although cure rates are high, radiation failures often require total laryngectomy for salvage. Biomarkers that can predict tumour radioresistance may be useful in modifying the treatment approach for individual patients. METHODS: Retrospective patient chart review yielded 75 patients with T1-T2 glottic squamous cell carcinoma treated with radiation therapy at the London Health Sciences Centre. Pretreatment tumour biopsies were immunostained for B-cell lymphoma 2 (Bcl-2), Ki-67, and epidermal growth factor receptor (EGFR) to correlate biomarker expression with disease-free survival (DFS). RESULTS: Ki-67 expression was strongly associated with recurrence following radiation and independently predicted poor DFS (hazard ratio 4.86, 95% CI 1.58-15.00; p = .006). EGFR and Bcl-2 were not associated with a risk of recurrence. CONCLUSIONS: Ki-67 expression identified a subset of patients with increased risk of local recurrence after radiation therapy. Ki-67 expression can potentially guide improved personalized treatments for patients with early-stage glottic squamous cell carcinomas.
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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".