Cytohistological diagnosis of pancreatic serous cystadenoma: a multimodal approach
Bibliographic record
Abstract
AIMS: Serous cystadenomata (SCAs) are benign pancreatic cystic neoplasms that present a diagnostic challenge despite many investigational approaches. Notwithstanding the promise of molecular diagnostics, these tests have limited accessibility in day-to-day surgical pathology practices. We aim to corroborate and build on recent evidence which suggests that positive α-inhibin immunohistochemistry (IHC) is a helpful adjunct in the biopsy confirmation of pancreatic SCA. METHODS: We retrospectively reviewed 22 fine-needle aspirates/biopsies from 14 patients (mean age 65 years, 47-83 years) with pancreatic multicystic lesions radiologically suspicious for SCA (location: 6 body, 2 head, 4 tail, 1 neck, 1 uncinate; cyst size: mean 3.7 cm, 2.0-7.6 cm), as well as an additional 10 pancreatic resection specimens with confirmed SCA; α-inhibin IHC was performed on all cell blocks, biopsy slides and representative resection specimen sections. Where available, associated cyst fluid was analysed for correlative vascular endothelial growth factor A (VEGF-A) and carcinoembryonic antigen levels. RESULTS: An α-inhibin IHC sensitivity of 80% was observed in the cases with resection confirmed SCA. Of the fine-needle aspirate/biopsy specimens, 59% (13/22) contained epithelial cells strongly positive for α-inhibin. When selecting for specimens that exhibited distinct strips of epithelium, the α-inhibin strong positivity rate increased to 73% (8/11). VEGF-A values were supportive of false-negative α-inhibin IHC in three cases and true-negative α-inhibin IHC in one case. CONCLUSION: This study postulates a diagnostic algorithm to confirm pancreatic SCA which may help to decrease unnecessary follow-up endoscopy/surgical resection and would decrease the associated morbidity, mortality and financial costs in patients with this otherwise benign condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".