Role of Fine-Needle Aspiration in the Surgical Management of Pancreatic Neuroendocrine Tumors: Utility and Limitations in Light of the New World Health Organization Classification
Bibliographic record
Abstract
CONTEXT: Pancreatic neuroendocrine tumors (Panc-NETs) are rare and tend to get overshadowed by their more prevalent and aggressive ductal adenocarcinoma counterparts. The biological behavior of PancNETs is unpredictable, and thus management is controversial. However, the new World Health Organization classification has significantly contributed to the prognostic stratification of these patients. Concurrently, there have been advances in surgical techniques for benign or low-grade pancreatic tumors. These procedures include minimally invasive and parenchyma-sparing operations such as laparoscopy and enucleation. OBJECTIVE: To report on the utility and limitations of fine-needle aspiration in the preoperative evaluation and management of PancNETs. DESIGN: This was a retrospective review of our institutional tumor database from 2002 to 2012. There were 25 cases of PancNETs that were localized and staged by medical imaging and diagnosed by fine-needle aspiration. RESULTS: Fourteen patients underwent laparotomy, with some requiring only limited surgery; 4 had laparoscopic resections; 4 were serially observed without surgical intervention; and another 3 were inoperable. After a mean follow-up of 37 months, more than half of the patients had no evidence of disease, including most of those who underwent minimally invasive surgery. CONCLUSIONS: Fine-needle aspiration is a useful diagnostic adjunct to medical imaging in the preoperative evaluation and management of PancNETs. However, there are limitations with regard to grading PancNETs using this technique.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".