Pancreatic Neuroendocrine Tumors—A Descriptive Study of the Presenting Features in a 20-Year Surgical Resection Cohort at a Tertiary Institution
Bibliographic record
Abstract
OBJECTIVES: Pancreatic neuroendocrine tumors (pNETs) are uncommon, comprising 3%-7% of pancreatic tumors. With increasing incidence due to advanced imaging techniques, there is a need for detailed characterization of these tumors. This study aims to describe the clinical features, diagnostic evaluations, and pathology characteristics of pNETs in a large cohort from a single tertiary center, and to compare these findings with other larger cohort studies. METHODS: We conducted a retrospective analysis of 866 patients with pNETs who underwent surgical resection at Mayo Clinic, Rochester, from March 2000 to December 2019. Data on demographics, clinical presentation, laboratory tests, imaging, and pathology were extracted and analyzed. Descriptive statistics were used to summarize the data. RESULTS: The cohort had a median age of 57 years. Nonfunctional tumors were much more prevalent (77.5%), with functional tumors primarily being insulinomas (75.9%). Common presenting symptoms included gastrointestinal (45.3%) and nongastrointestinal symptoms (30.7%). Chromogranin A levels were elevated in 57.5% of patients. Imaging revealed enhancing lesions in most cases, with computed tomography scans performed in 90.9% of patients. Endoscopic ultrasound (EUS) identified tumors in 98.1% of cases, with EUS-FNA showing a sensitivity of 82%. Ki-67 index, used in 58.1% of cases, indicated grade 2 tumors as the most common (55.9%). Metastasis was observed in 39.4% of patients at the time of diagnosis, predominantly in the liver. CONCLUSION: This study provides a comprehensive description of pNET characteristics in a large surgical cohort. Findings highlight the predominance of nonfunctional tumors and the importance of imaging and EUS in diagnosis. The data can aid in inter-institutional comparisons and enhance understanding of pNETs, contributing to improved patient management and future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".