Prevalence of histological features resembling autoimmune pancreatitis in neoplastic pancreas resections
Bibliographic record
Abstract
AIMS: Types 1 and 2 autoimmune pancreatitis (AIP) can mimic pancreatic neoplasia. Due to the small quantity of tissue in mass-targeted pancreas biopsies, inflammatory features may raise the differential of AIP. However, the frequency of AIP-like histology in neoplastic pancreas is not well characterised. Therefore, the specificity of inflammatory lesions on biopsy with respect to the diagnosis of AIP is uncertain. METHODS AND RESULTS: Neoplastic pancreas resections performed at our institution between 2008 and 2019 were retrospectively reviewed. Features of AIP types 1 and 2 were assessed in the non-neoplastic areas. If features of immunoglobulin (Ig)G4-associated AIP were seen, IgG4 immunohistochemistry was performed. We identified 163 neoplastic pancreas resections. Of these, 34 had one or more types of inflammatory lesions in non-neoplastic pancreatic tissue. Dense lymphoplasmacytic inflammation mimicking type 1 AIP was found in six cases with mild to moderately increased IgG4-positive plasma cells. Neutrophilic infiltrates in small intralobular ducts were found in 20 cases. Mild extralobular ductitis or duct microabscess was found in 10 specimens. Marked neutrophilic duct destruction that resembled granulocytic epithelial lesions was found in 12 cases. Some cases showed multiple features. CONCLUSION: Approximately 20% of neoplastic pancreas resections showed focal areas that could raise the differential of AIP. More cases showed neutrophilic predominant inflammation as seen in type 2 autoimmune pancreatitis, compared to dense lymphoplasmacytic infiltrates seen in type 1 AIP. Pathologists must be cautious when making a diagnosis of AIP on biopsy tissue based on histological findings alone.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".