Immunohistochemical characterization of appendiceal mucinous neoplasms and the value of special <scp>AT</scp>‐rich sequence‐binding protein 2 in their distinction from primary ovarian mucinous tumours
Bibliographic record
Abstract
AIMS: The distinction between primary ovarian mucinous tumours and appendiceal mucinous neoplasms metastatic to the ovary can be challenging, given the overlap of morphological features and immunohistochemical expression of traditional markers. Special AT-rich sequence-binding protein 2 (SATB2) has recently been described as a sensitive and specific marker of colorectal epithelium. This study was to determine its expression in appendiceal mucinous tumours and its role in their distinction from ovarian neoplasms. METHODS AND RESULTS: Immunohistochemistry was performed in tissue microarrays from 32 primary appendiceal mucinous tumours (25 low-grade appendiceal mucinous neoplasms and seven adenocarcinomas) and 40 ovarian mucinous neoplasms (20 borderline tumours and 20 adenocarcinomas). Stains were interpreted as positive or negative by scoring intensity and distribution. SATB2 was positive in 93.8% of appendiceal tumours and in only one ovarian tumour; SATB2 was 97.5% specific for appendiceal origin. CK20, CDX2 and MUC2 were strongly and diffusely positive in appendiceal tumours; ovarian tumours were also positive, but with a patchy distribution and mild intensity. CK7 was expressed in 97.5% of ovarian tumours and in 31.2% of appendiceal tumours. PAX8 was positive in 70% of ovarian tumours, and negative in all appendiceal lesions. CONCLUSIONS: SATB2 is frequently expressed in appendiceal mucinous neoplasms. In the context of a mucinous neoplasm involving the ovary, any SATB2 positivity should raise the possibility of appendiceal origin. Expression of CK20, CDX2 and MUC2 supports appendiceal origin only when diffuse and strong. These and other markers, such as CK7 and PAX8, are recommended in the work-up of ovarian mucinous tumours with any clinical or pathological features suggestive of secondary origin.
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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".