EGFR and Ki-67 expression in oral squamous cell carcinoma using tissue microarray technology
Bibliographic record
Abstract
OBJECTIVE: Our aim was to validate the use of tissue microarrays (TMA) in oral squamous cell carcinomas (OSCC) to analyse epidermal growth factor receptor (EGFR) and Ki-67 expression. We also analysed the relationship that the expression of these markers may have with clinical, pathological and survival variables. PATIENTS AND METHODS: The study sample comprised 39 unselected patients diagnosed and treated for OSCC. We analysed Ki-67 and EGFR expression by immunohistochemistry on formalin-fixed, paraffin-embedded surgical specimens. Whole sections (WS) were compared with double 1.5 mm core-tissue microarrays. RESULTS: High EGFR expression was observed both on TMA (in 98% of the cases) and WS (in 100% of the cases) with substantial agreement kappa value (0.720). EGFR expression was not significantly associated with clinical, pathological and survival variables on TMA and WS. Ki-67 analysis showed a Spearman correlation of 0.741 with a Ki-67 mean labelling index of 45% in TMA and 56.8% in WS. We found a significant relationship between gender and Ki-67 labelling index on WS (P = 0.022) and TMA (P = 0.002). Clinical stage was the only parameter in multivariate analysis that had a significant predictive value. CONCLUSION: We demonstrate that dual 1.5 mm core TMA is a valid, rapid, economical and tissue-saving way to study OSCC biopsies and that it presents strong correlation with the WS. EGFR overexpression in OSCC suggests that these tumours may be a candidate for therapy investigation directed to EGFR.
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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 | 0.001 | 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 | 0.001 |
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".