The role of immunohistochemistry for smooth-muscle actin, p63, CD10 and cytokeratin 14 in the differential diagnosis of papillary lesions of the breast
Bibliographic record
Abstract
BACKGROUND: Histological differentiation of mammary papillary lesions can be difficult. The evaluation of myoepithelial cells can be helpful, with benign papilloma showing a continuous myoepithelial cell layer, which becomes attenuated or absent in malignant papillary lesions. METHODS: A large series of 100 papillomas (28 papillomas with florid epithelial hyperplasia) and 68 papillary carcinomas (9 invasive, 44 in situ, and 15 ductal carcinomas in situ (DCIS) involving papillomas) of the breast were stained for myoepithelial cells by immunohistochemistry using antibodies to smooth-muscle actin (SMA), p63, CD10 and cytokeratin (CK) 14. RESULTS: In the papillomas, using these four antibodies, myoepithelial cells were positive in 88%, 99%, 91% and 95% of cases, respectively, with SMA showing marked stromal component cell staining and CD10 showing epithelial and stromal staining. CK14 also showed epithelial staining in 71% of papillomas and 96% of papillomas with florid epithelial hyperplasia. In the papillary carcinomas, 36 (53%) cases showed staining of myoepithelial cells that were scattered, discontinuous and diminished in number and the remaining 32 (47%) cases did not show myoepithelial cells. Invasive papillary carcinoma has the lowest proportion (33%) with myoepithelial cells, and DCIS involving papillomas had the highest proportion (87%). CONCLUSIONS: p63 had the highest sensitivity and did not cross-react with stromal cells and only rarely with epithelial cells. CK14 has the added ability to distinguish between florid epithelial hyperplasia involving papilloma and DCIS involving papillomas. CK14 and p63 may be used as an adjunct in assessing difficult papillary lesions of the breast.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".