Patterns of Recurrence after Resection for Pancreatic Neuroendocrine Tumors: Who, When, and Where?
Bibliographic record
Abstract
BACKGROUND/AIMS: Pancreatic neuroendocrine tumors (pan-NENs) represent an increasingly common indication for pancreatic resection, but there are few data regarding possible recurrence after surgery. The aim of the study was to describe the frequency, timing, and patterns of recurrence after resection for pan-NENs with consequent implications for postoperative follow-up. METHODS: We performed a retrospective analysis of pan-NENs resected between 1990 and 2015 at The Pancreas Institute, University of Verona Hospital Trust. Predictors of recurrence were assessed. Survival analysis was conducted using the Kaplan-Meier and conditional survival (CS) methods. RESULTS: The cohort consisted of 487 patients with a median follow-up of 71 months. Recurrence developed in 12.3%: 54 (11.1%) liver metastases, 11 (2.3%) local recurrence, 10 (2.1%) nodal recurrence, and 8 (1.6%) metastases in other organs. Thirty-one (6.4%) died due to disease recurrence. Size > 21 mm, G3 grade, nodal metastasis, and vascular infiltration were independent predictors of overall recurrence. Recurrence occurred either during the first year of follow-up (n = 9), or after 10 years (n = 4). CS analysis revealed that nonfunctioning G1 pan-NEN ≤20 mm without nodal metastasis or vascular invasion had a negligible risk of developing recurrence. In the present series, after 5 years of follow-up without developing recurrence, tumor recurrence occurred only in the form of liver metastases. CONCLUSIONS: Recurrence of pan-NENs is rare and is predicted by tumor size, nodal metastasis, grading, and vascular invasion. Patients with G1 pan-NEN without nodal metastasis and vascular invasion may be considered cured by surgery. After 5 years without recurrence, follow-up should focus on excluding the development of liver metastases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".