Are Cystic Pancreatic Neuroendocrine Tumors an Indolent Entity Results from a Single-Center Surgical Series
Bibliographic record
Abstract
INTRODUCTION: Cystic pancreatic neuroendocrine tumors (CPanNETs) represent an uncommon variant of pancreatic neuroendocrine tumors (PanNETs). Due to their rarity, there is a lack of knowledge with regard to clinical features and postoperative outcome. METHODS: The prospectively maintained surgical database of a high-volume institution was queried, and 46 resected CPanNETs were detected from 1988 to 2015. Clinical, demographic, and pathological features and survival outcomes of CPanNETs were described and matched with a population of 92 solid PanNETs (SPanNETs) for comparison. RESULTS: CPanNETs accounted for 7.8% of the overall number of resected PanNETs (46/587). CPanNETs were mostly sporadic (n = 42, 91%) and nonfunctioning (39%). Two functioning CPanNETs were detected (4.3%), and they were 2 gastrinomas. The median tumor diameter was 30 mm (range 10-120). All tumors were well differentiated, with 38 (82.6%) G1 and 8 (17.4%) G2 tumors. Overall, no CPanNET showed a Ki-67 >5%. A correct preoperative diagnosis of a CPanNET was made in half of the cases. After a median follow-up of >70 months, the 5- and 10-year overall survival of resected CPanNETs was 93.8 and 62.5%, respectively, compared to 92.7 and 84.6% for SPanNETs (p > 0.05). The 5- and 10-year disease-free survival rates were 94.5 and 88.2% for CPanNETs and 81.8 and 78.9% for SPanNETs, respectively (p > 0.05). CONCLUSION: In the setting of a surgical cohort, CPanNETs are rare, nonfunctional, and well-differentiated neoplasms. After surgical resection, they share the excellent outcome of their well-differentiated solid counterparts for both survival and recurrence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".