Primary gallbladder neuroendocrine neoplasm: A case report of grade 1 well-differentiated neuroendocrine tumor
Bibliographic record
Abstract
INTRODUCTION: Neuroendocrine neoplasm (NENs) make up approximately 2-3 % of gallbladder malignancies, while only 0.5 % of all NENs develop in the gallbladder. Most Gallbladder neuroendocrine neoplasms (GB-NENs) are discovered incidentally during pathological examinations post-cholecystectomy. CASE PRESENTATION: 70-year-old male presents with an incidentally discovered 2.2 cm enhancing intraluminal soft tissue mass on abdominal CT scan. The mass demonstrates restricted diffusion on MR imaging, concerning for gallbladder malignancy. Radical cholecystectomy, confirms primary gallbladder neuroendocrine tumor (GB-NET). No adjuvant therapy was recommended at multidisciplinary cancer conference review. The patient is currently disease free at 18 months follow up. DISCUSSION: The management of GB-NEN remains challenging, due to the lack of specific clinical manifestations and typical imaging features preoperatively. GB-NENs are usually asymptomatic, and the paucity of reported imaging characteristics makes prospective diagnosis of GB-NENs challenging. GB-NEN tend to be larger in size, demonstrating well defined, intact mucosa, with a thick rim of hyperintensity on diffusion weighted images (DWI). Distinguishing between gallbladder neuroendocrine carcinoma (GB-NEC) and gallbladder neuroendocrine tumor (GB-NET) on pathologic evaluation is essential in developing a treatment plan. GB-NETs have superior survival compared to GB-NECs. GB-NETs can be managed utilizing a cholecystectomy with portal lymphadenectomy +/- segment 4b/5 liver resection. CONCLUSION: GB-NETs may achieve curative resection, if identified at an early disease stage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".