Clinicopathological analysis of 18 cases of secretory carcinoma of the salivary glands
Bibliographic record
Abstract
Secretory carcinoma (SC) is a rare salivary gland tumor that featured by ETV6::NTRK3 gene fusion, and was included in the WHO Classification of Head and Neck Tumors since 2017. Nevertheless, the description of SCs by WHO is still vague. This study examined 18 SC cases by using both histomorphology and molecular pathology for diagnostic determination, especially immunohistochemical features of SCs. Based on WHO characteristics, 18 patients with SC admitted between 2001 and 2022 were included in this study. Main histomorphological patterns, FISH analyses of the ETV6::NTRK3 gene fusion, and immunohistochemical analyses of S100, mammaglobin, DOG1, ADFP, CA6 and Ki-67 were performed. Among the 18 SC patients, the median age of onset was 39.22 years. Grossly, the average tumor size in 2.96 cm with various texture from soft to tough. The majority patients were positive for S100, mammaglobin, and negative for DOG1, except for one patient negative for S100 (Case 18). All patients were positive for ADFP, and the majority patients were negative for CA6, except for Case 9. Two cases were found recurrence, and the tumor were found both in parotid gland with local invasion. Combined with the results of previous studies, we proposed that the combination of all five markers, S100, mammaglobin, DOG1, ADFP and CA6, could contribute more to differential diagnosis of SCs with other salivary carcinomas, especially with AciCC. The prognosis of SCs is optimistic in most cases, but larger patient cohort and long-term follow-up are still needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".