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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".