Ameloblastoma, calcifying epithelial odontogenic tumor, and glandular odontogenic cyst show a distinctive immunophenotype with some myoepithelial antigen expression
Bibliographic record
Abstract
BACKGROUND: Odontogenic neoplasms have some morphologic overlap with salivary gland neoplasms, many of which show myoepithelial differentiation. In the 1980s, an ultrastructural study identified a population of myoepithelial-like cells in calcifying epithelial odontogenic tumor. Myoepithelial derived tumors have since been shown to have distinct immunohistochemical profiles. METHODS: We examined a series of odontogenic neoplasms, including 11 ameloblastomas, four calcifying epithelial odontogenic tumors, five glandular odontogenic cysts (GOCs), and five keratocystic odontogenic tumors with a panel of myoepithelial-associated immunohistochemical stains. We also assessed representative control examples of oral mucosa, odontogenic rests, and dentigerous cysts. RESULTS: All of the neoplastic and non-neoplastic oral epithelium-derived entities share a p63-positive, high molecular weight cytokeratin (CK5/6)-positive immunophenotype. Calponin reactivity was at least focally present in two of four calcifying epithelial odontogenic tumors, three of five GOCs, and 10 of 11 ameloblastomas; the sole completely non-reactive ameloblastoma represents a lung metastasis. One case of calcifying epithelial odontogenic tumor was focally positive for glial fibrillary acidic protein. However, other more definitive markers of myoepithelial differentiation, including S-100 and smooth muscle actin, were negative. Two of three calcifying epithelial odontogenic tumors and five of five GOCs were also positive for a low molecular weight cytokeratin (CK7). CONCLUSIONS: Ameloblastomas, GOCs, and calcifying epithelial odontogenic tumors show a distinctive immunophenotype which overlaps with that of myoepithelial-derived salivary gland neoplasms but does not provide definitive support for myoepithelial differentiation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".