Defining Biological Borderline Resectable Non-functioning Pancreatic Neuroendocrine Tumors (NF-PanNETs)
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop and validate a preoperative predictive model to identify patients at high risk of early recurrence (ER), with a view to establish a framework for biological borderline resectability of non-functioning pancreatic neuroendocrine tumors (NF-PanNETs). BACKGROUND: Radical surgery is curative for most localized NF-PanNETs, but a subset of patients experiences ER. No standardized criteria define preoperative high-risk disease. METHODS: A retrospective multicentric study was conducted at 3 tertiary centers. Patients undergoing curative resection for localized NF-PanNETs were included, and preoperative clinicopathologic and imaging variables were analyzed. ER was defined as a recurrence within 24 months. A classification tree model was developed, and performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A total of 496 patients were analyzed, with 290 in the derivation cohort and 206 in the validation cohort. ER occurred in 55 patients (11%), including 26 (9%) in the derivation and 29 (14%) in the validation cohort. The median disease-free survival for ER patients was 16 months (interquartile range: 10-20 months). Neoplastic venous thrombosis was the strongest predictor of ER, with an ER probability of 71%. Among patients without venous thrombosis, those with a Ki-67 index ≥5% and tumor size ≥3 cm had an ER probability of 41% in case of adenopathy and 19% otherwise. The model achieved an AUC of 0.91 in the derivation cohort and 0.84 in the validation cohort. CONCLUSIONS: This externally validated model provides a reliable preoperative tool to identify NF-PanNETs at high risk of ER and introduces the concept of biological borderline resectable NF-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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".