Best practices in diagnostic immunohistochemistry: myoepithelial markers in breast pathology.
Bibliographic record
Abstract
CONTEXT: Numerous immunohistochemical stains have been shown to exhibit exclusive or preferential positivity in breast myoepithelial cells relative to their luminal/epithelial counterparts. These myoepithelial markers provide invaluable assistance in accurately classifying breast proliferations, especially in core biopsies. Although numerous myoepithelial markers are available, they differ in their sensitivity, specificity, and ease of interpretation, which may be attributed, to a large extent, to the variable immunoreactivity of these markers in stromal cells including myofibroblasts, vessels, luminal/epithelial cells, and tumor cells. OBJECTIVE: To review commonly used myoepithelial markers in breast pathology and a selection of diagnostic scenarios where they may be useful. DATA SOURCES: The information outlined in this review article is based on our experiences with routine cases and a review of English-language articles published between 1987 and 2008. CONCLUSIONS: To demonstrate the presence or absence of myoepithelial cells, a panel-based approach of 2 or more markers is recommended. Markers that most effectively combine sensitivity, specificity, and ease of interpretation include smooth muscle myosin heavy chains, calponin, p75, p63, P-cadherin, basal cytokeratins, maspin, and CD10. These markers, however, display varying cross-reactivity patterns and variably reduced expression in the myoepithelial cells bordering in situ carcinomas. The choice of a myoepithelial marker should be dependent on a combination of factors, including published evidence on its diagnostic utility, its availability, performance characteristics that have been achieved in a given laboratory, and the specific diagnostic scenario. When its use is deemed necessary, immunohistochemistry for myoepithelial cells in breast pathology is most effective when conceptualized as supplemental, rather than central to routine morphologic interpretation.
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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 | 0.038 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".