SOX11 Is an Effective Discriminatory Marker, When Used in Conjunction With CK20 and TTF1, for Merkel Cell Carcinoma: Comparative Analysis of SOX11, CK20, PAX5, and TTF1 Expression in Merkel Cell Carcinoma and Pulmonary Small Cell Carcinoma
Bibliographic record
Abstract
CONTEXT.—: Distinction between Merkel cell carcinoma (MCC) and pulmonary small cell carcinoma (PSmCC) can be challenging, even with the aid of immunohistochemistry (IHC) analysis of CK20 and TTF1, as these tumors occasionally lack classic immunophenotypes (CK20+/TTF1- in MCC and CK20-/TTF1+ in PSmCC). OBJECTIVE.—: To evaluate the diagnostic utility of SOX11 and PAX5 IHC for distinguishing MCCs from PSmCCs and compare it with that of CK20 and TTF1 IHC. DESIGN.—: SOX11, PAX5, CK20, and TTF1 expression (pattern, intensity, and proportion of tumor cells expressing protein) was assessed in 31 primary and 16 metastatic MCCs and 20 primary and 9 metastatic PSmCCs. RESULTS.—: SOX11 expression was present in all MCCs and was predominantly strong and diffuse. Only 19% of primary and 38% of metastatic MCCs exhibited diffuse PAX5 expression; none exhibited strong immunoreactivity. Strong and diffuse SOX11 expression was seen in less than 25% of primary and metastatic PSmCCs. PAX5 expression was rare in PSmCCs and was mostly weak and focal/patchy. SOX11 expression in at least 26% of tumor cells, with at least moderate intensity, favored the diagnosis of MCC over PSmCC (P < .001). Furthermore, SOX11 expression was more likely than CK20 expression to be strong or diffuse in sentinel lymph node (SLN) metastases of MCC, indicating that SOX11 is superior to CK20 for detecting tumor deposits in SLNs in MCC. CONCLUSIONS.—: Our findings indicate that SOX11 not only is a powerful marker for distinguishing MCCs from PSmCCs, especially when used in conjunction with CK20 and TTF1, but also has utility for screening SLNs in MCC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".