Misinterpreted Myoepithelial Carcinoma of Salivary Gland
Bibliographic record
Abstract
Myoepithelial carcinoma (MECA) is an underrecognized challenging entity with a broad morphologic spectrum. Misinterpreting MECA is not uncommon as distinguishing it from its mimics, especially cellular myoepithelial-rich pleomorphic adenoma (PA), can be difficult. We described 21 histologically challenging cases of MECAs (16 MECA ex-PA and 5 MECA de novo). All MECAs ex-PA were intracapsular or minimally invasive except for 3 cases. Eighteen (86%) were initially misinterpreted as benign neoplasms, including PA (10), atypical PA (5), and myoepithelioma (3). The remaining 3 were initially diagnosed as malignant (MECA ex-PA) but were histologically challenging. Histologic features that were found most helpful in recognizing the malignant nature of MECA included: uniformly cellular myoepithelial proliferation with an expansile nodular lobulated pattern (all cases) and alternate hypocellular and hypercellular zonal distribution (76% of cases). Among the 16 MECA patients with follow-up, 14 (87.5%) progressed: 10 developed local recurrence and 5 distant metastases. In contrast, only one of 33 patients with cellular PA (control group) recurred locally. Ten of the 14 MECAs that progressed were MECA ex-PA, and 12 (85%) had an initial benign diagnosis. Two patients with MECA ex-PA died of their disease; one had an initial diagnosis of PA. MECA is a histologically challenging entity that closely mimics PA and seems to carry a significant risk of recurrence. Areas of clonal appearing cellular myoepithelial growth with an expansile nodular lobulated pattern and zonal cellular distribution distinguish the majority of MECAs and may serve as useful diagnostic histologic features to differentiate MECA from its benign mimics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".