Concomitant pancreatic neuroendocrine tumors in hereditary tumor syndromes: who, when and how to operate?
Bibliographic record
Abstract
Abstract Pancreatic neuroendocrine tumors (pNETs) might present as part of a complex of hereditary (familial) syndromes caused by germline mutations such as multiple endocrine neoplasia type 1 (MEN1), von Hippel–Lindau syndrome (VHL), tuberous sclerosis, and neurofibromatosis syndromes. Hereditary pNETs are frequently misdiagnosed because their presentation may mimic other more common diseases, resulting in diagnostic delays. Although non-operative (conservative) management could be advocated in select cases in most patients, hereby avoiding surgery without loss of oncological safety, some cases still need operative intervention before malignancy develops. The objective of this review is to address the most recent literature and the evidence it provides for the indications, timing and options of operative treatment for concomitant pNETs in hereditary tumor syndromes. Complete sequencing of the whole gene is recommended for suspected hereditary pNETs. Proven functional pNETs with hereditary tumor syndromes is a good indication for surgical treatment. Conservative management for MEN1 patients with a non-functional pNET of 2 cm or smaller is associated with a low risk of malignant transformation and metastasis development. VHL-related pNETs patients with tumor size >1.5 cm or a missense mutation or any mutation type in exon 3 may benefit from surgical intervention. The parenchyma-sparing surgical strategy should be preferentially performed whenever possible in all hereditary syndromes. The decision to recommend surgery to prevent malignant transformation and tumor spread, which is based on multidisciplinary expertise and the patient's preference, should be balanced with operative mortality and morbidity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".