Merkel cell carcinoma frequently shows histologic features of basal cell carcinoma: a study of 30 cases
Bibliographic record
Abstract
BACKGROUND: Merkel cell carcinoma (MCC) is a basaloid cutaneous neoplasm that may be mistaken for basal cell carcinoma (BCC). METHODS: Thirty MCCs were examined for areas that histologically resembled BCC. RESULTS: One of the histologic features of BCC (either a mucinous stroma or stromal artifactual retraction) was identified in all MCCs. A mucinous stroma was found in 28 MCCs (93%), stromal artifactual retraction in 27 (90%), mucin-containing gland-like spaces within tumor nests in 8 (27%), focal peripheral palisading in 8 (27%), epidermal involvement in 3 (10%) and dystrophic calcification in 1 MCC (3%). The cytologic features and absence of widespread peripheral palisading were the most reliable discriminators between MCC and BCC on routine sections. Squamous cell carcinoma was identified in four cases (13%). Two cases (7%) contained pagetoid intraepidermal spread (IES) of MCC. In one case, there was IES over the entire epidermal surface associated with intranuclear clearing, resembling the intranuclear cytoplasmic inclusions (INI) common in melanocytic tumors. INI were identified in six MCCs (20%). CONCLUSIONS: MCCs frequently contain areas that histologically resemble BCC and other more common cutaneous malignancies. This can lead to diagnostic errors, particularly in small fragmented curettage specimens or frozen sections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".