Beyond the Obvious: Spectrum of Neuroendocrine Proliferation and Tumors in the Biliary Tree - A Case Series
Bibliographic record
Abstract
Abstract Introduction/Objective Neuroendocrine tumors (NETs) in the biliary system are extremely uncommon and often pose a diagnostic challenge due to their non-specific presentation and radiological features. While typically NETs form mass lesions causing biliary obstruction, to the best of our knowledge, microscopic neuroendocrine cell proliferation without mass formation has not been formally reported in the literature. This study aims to describe the clinicopathological features of a series of NETs/neuroendocrine cell proliferation involving the biliary tree. Methods/Case Report Surgical resections of NETs/neuroendocrine cell proliferation affecting the extrahepatic bile ducts and gallbladder were identified from the electronic pathology databases of five institutions between January 2010 and May 2023. The clinical data was obtained through chart review, and histologic findings were reviewed by a gastrointestinal pathologist at each institution. Results (if a Case Study enter NA) The study involved six patients with a mean age of 66 years (range: 58-75 years, males:3, females:3). The patients presented with abdominal pain or biliary obstruction. Pathologic examination revealed that three cases showed visible mass-forming lesions (1.6-2.4 cm) in the gallbladder or common bile duct, diagnosed as well-differentiated NETs. The remaining three cases had no grossly visible masses but showed incidental microscopic neuroendocrine cell proliferation/NETs (either in cystic duct or common bile duct) ranging from 0.2-0.4 cm, all associated with biliary lithiasis. All patients recovered after surgical resection without metastasis or recurrence, except for one patient who died a month after surgery due to an unrelated cause (multisystem organ failure). Conclusion This study highlights the spectrum of neuroendocrine proliferation in the biliary tree, ranging from microscopic incidental findings to grossly visible mass-forming NETs, which may result in variable clinical management and outcomes. Notably, incidental neuroendocrine proliferations were found to be associated with biliary lithiasis in all 3 cases, raising a possibility of a potential neuroendocrine metaplastic pathogenesis, which should be explored more via additional studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".