Classifying Pulmonary and Urinary High-grade Neuroendocrine Carcinoma by CK7 Immunohistochemistry
Bibliographic record
Abstract
High-grade neuroendocrine carcinoma (HGNEC) is subclassified into small cell carcinoma (SmCC) and large cell neuroendocrine carcinoma (LCNEC). Although both are clinically aggressive, the SmCC and LCNEC need to have different treatment strategies, and accurate pathologic diagnosis is challenging. We studied a large retrospective cohort (186 cases) of HGNEC of bladder and lung to investigate the abundance of cytokeratin (CK) 7 expression and staining pattern in SmCC and LCNEC. Overall, the pulmonary and urinary HGNEC exhibited several different CK7 staining patterns, including negative staining (n=28), dot-like staining (n=73), partial membranous staining (n=26), and complete membranous staining (n=60). Overall, 88.9% (44/49) of pulmonary SmCC and 88.0% (44/50) of urinary SmCC showed negative or dot-like patterns for CK7, while 90.8% (59/65) of pulmonary LCNEC and 72.7% (16/22) of urinary LCNEC showed partial or complete membranous patterns for CK7 (χ 2 =105.05, P <0.0001). The distinct staining patterns were also present in those mixed SmCC and LCNEC. In addition, the specimen types or fixation did not affect CK7 staining patterns. In conclusion, CK7 has a high differential value for SmCC and LCNEC and could help guide personalized treatment for patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".