An Overview of Practical Issues in the Diagnosis of Gastroenteropancreatic Neuroendocrine Pathology
Bibliographic record
Abstract
CONTEXT: Although somewhat uncommon, neuroendocrine tumors of the gastrointestinal tract and pancreas have come under scrutiny in recent times. With advances in imaging techniques, more of these tumors are being removed and sent for pathologic evaluation. It is important for the diagnostic pathologist to be aware of recent developments in this field. OBJECTIVE: This overview focuses on nomenclature/terminology, classification, practical issues related to recent developments in immunohistochemical markers that aid diagnosis and may relate to prognosis, and molecular advances. DATA SOURCES: Currently available literature and personal experience in the field of neuroendocrine pathology. CONCLUSIONS: The preferred terminology is neuroendocrine/tumor/carcinoma and it is recommended that the World Health Organization classification be used, taking note of the site variations that may occur. A large number of immunohistochemical markers are available but a core panel that is relevant to the site should be used. Cytokeratin 19 positivity is an independent marker of aggressive behavior in pancreatic neuroendocrine tumors. Gastrointestinal neuroendocrine tumors arise via the CpG island methylator phenotype pathway, whereas their pancreatic counterparts arise as a result of chromosomal instability. The MEN1 gene is implicated in both syndromic and sporadic forms of these tumors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".