p53 and Ki‐67 as markers of radioresistance in head and neck carcinoma
Bibliographic record
Abstract
BACKGROUND: p53 and Ki-67 are regarded as potential interesting predictors of radioresistance, although their exact influence awaits confirmation on a large cohort of uniformly treated patients. METHODS: In a retrospective cohort of 304 patients with squamous cell carcinoma of the head and neck who were treated with radical radiotherapy, the expression levels of p53 and Ki-67 were assessed by immunohistochemistry. Local control and survival curves were generated for p53 and Ki-67 using the Kaplan-Meier method. The difference between curves was calculated in univariate and multivariate analyses. RESULTS: The overexpression of p53 was associated with local treatment failure (P = 0.01) but not with survival (P = 0.09). In a Cox analysis, p53 overexpression remained an independent predictor of local failure, with a relative risk of local failure of 1.5 (P = 0.05). Low proliferation (Ki-67 < 20%) was a significant factor in local failure for patients with tumors of the oral cavity only (P = 0.01). Patients with both unfavorable immunohistochemical markers (p53 overexpression and low proliferation) had a 45% rate of local control compared with a 67% rate for all other combinations (P = 0.002). This association was even more significant in patients with T1-T2 lesions (45% vs. 77%; P = 0.0002). CONCLUSIONS: The results support the role of p53 as an independent predictor of local failure in patients with squamous cell carcinoma of the head and neck who are treated by radical radiotherapy, suggesting that it may predict radioresistance. Combined with p53, Ki-67 may help in the better selection of patients for radiotherapy, especially for patients with early-stage tumors. Prospective studies are now needed to confirm these results and to define better the role of these markers in the management of patients with head and neck carcinoma.
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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 | 0.003 |
| 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 | 0.000 |
| 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".