Pancreatic grade 3 neuroendocrine tumors behave similarly to neuroendocrine carcinomas following resection: a multi-center, international appraisal of the WHO 2010 and WHO 2017 staging schema for pancreatic neuroendocrine lesions
Bibliographic record
Abstract
BACKGROUND: In 2017, the WHO updated their 2010 classification of pancreatic neuroendocrine tumors, introducing a well-differentiated, highly proliferative grade 3 tumor, distinct from neuroendocrine carcinomas. The aim of this study was to investigate the clinical significance of this update in a large cohort of resected tumors. METHODS: Using a multicenter, international dataset of patients with pancreatic neuroendocrine lesions, patients were classified both according to the WHO 2010 and 2017 schema. Multivariable survival analyses were performed, and the models were evaluated for discrimination ability and goodness of fit. RESULTS: Excluding patients with a known germline MEN1 mutation and incomplete data, 544 patients were analyzed. The performance of the WHO 2010 and 2017 models was similar, however surgically resected grade 3 tumors behaved very similarly to neuroendocrine carcinomas. CONCLUSION: The addition of a grade 3 NET classification may be of limited utility in surgically resected patients, as these lesions have similar postoperative survival compared to carcinomas. While the addition may allow for a more granular evaluation of novel treatment strategies, surgical intervention for high grade tumors should be considered judiciously.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".