Preventive medicine of von Hippel–Lindau disease-associated pancreatic neuroendocrine tumors
Bibliographic record
Abstract
Pancreatic neuroendocrine tumors (PanNETs) are rare in von Hippel–Lindau disease (VHL) but cause serious morbidity and mortality. Management guidelines for VHL-PanNETs continue to be based on limited evidence, and survival data to guide surgical management are lacking. We established the European-American-Asian-VHL-PanNET-Registry to assess data for risks for metastases, survival and long-term outcomes to provide best management recommendations. Of 2330 VHL patients, 273 had a total of 484 PanNETs. Median age at diagnosis of PanNET was 35 years (range 10–75). Fifty-five (20%) patients had metastatic PanNETs. Metastatic PanNETs were significantly larger (median size 5 vs 2 cm; P < 0.001) and tumor volume doubling time (TVDT) was faster (22 vs 126 months; P = 0.001). All metastatic tumors were ≥2.8 cm. Codons 161 and 167 were hotspots for VHL germline mutations with enhanced risk for metastatic PanNETs. Multivariate prediction modeling disclosed maximum tumor diameter and TVDT as significant predictors for metastatic disease (positive and negative predictive values of 51% and 100% for diameter cut-off ≥2.8 cm, 44% and 91% for TVDT cut-off of ≤24 months). In 117 of 273 patients, PanNETs >1.5 cm in diameter were operated. Ten-year survival was significantly longer in operated vs non-operated patients, in particular for PanNETs <2.8 cm vs ≥2.8 cm (94% vs 85% by 10 years; P = 0.020; 80% vs 50% at 10 years; P = 0.030). This study demonstrates that patients with PanNET approaching the cut-off diameter of 2.8 cm should be operated. Mutations in exon 3, especially of codons 161/167 are at enhanced risk for metastatic PanNETs. Survival is significantly longer in operated non-metastatic VHL-PanNETs.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".