Poorly differentiated cutaneous apocrine carcinomas: histopathological clues and immunohistochemical analysis for the diagnosis of this unusual neoplasm
Bibliographic record
Abstract
Primary cutaneous apocrine carcinoma (PCAC) is a rare cutaneous malignancy that is derived from apocrine glands. Histologically, these tumours can appear well-differentiated where diagnosis should be relatively straightforward. However, occasionally these tumours can exhibit high-grade features, and in such instances the diagnosis can be challenging. A retrospective analysis of 12 cases of poorly differentiated PCAC, obtained from large academic institutions, was performed, and summarised below. Immunohistochemical studies were performed in all cases with antibodies against CK7, p63, CAM 5.2, GCDFP-15, GATA3, CEA, PR, ER, HER2, calponin, SMA, androgen receptor and EMA. All 12 cases were poorly differentiated; however, there were some histopathological clues to the diagnosis of apocrine carcinoma; namely, the presence of focal glandular formation, acrosyringial involvement and the presence of single 'pagetoid' cells within epidermis. All tumours were consistently positive for CK7, GATA3 and GCDFP-15 and negative for p63. The tumours had variable expression of CAM5.2, CEA, ER, PR, HER2, androgen receptor and EMA. In three cases, there was a preservation of the myoepithelial cell layer (with calponin and SMA), which also confirmed the primary cutaneous origin. PCAC is a difficult neoplasm to diagnose, as it can appear identical to metastatic carcinomas. We describe 12 cases of poorly differentiated PCAC, highlighting their salient clinical, histopathological and immunohistochemical features, and discuss the potential diagnostic pitfalls in distinguishing this entity from other malignant neoplasms. Our results indicate that a combination of thorough histological inspection coupled with an adequate battery of immunohistochemical stains is necessary to support the diagnosis of PCAC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".