ENDOSCOPIC ULTRASOUND-FINE NEEDLE ASPIRATION (EUS-FNA) DIAGNOSTIC ACCURACY IN THE EVALUATION OF PANCREATIC NEUROENDOCRINE NEOPLASMS (PNEN) GRADING
Bibliographic record
Abstract
Aims Prognosis of pNENs depends on staging and grading, which is based on the cyto-histological Ki67 labelling. EUS-FNA is considered the gold standard technique to obtain a cytological specimen in pre-therapeutic setting that can be used to evaluate ki67. The aim of this study was to establish the diagnostic accuracy of preoperative EUS-FNA Ki67 evaluation in a cohort of pNEN patients. Methods This is a retrospective study from a prospectively collected database on patients who underwent surgery for pNENs from 2006 to 2019 and EUS-FNA Ki67 labelling. EUS-FNA and surgery Ki67 (eKi67 and sKi67, respectively) values and grading, were compared and the diagnostic accuracy of EUS-FNA was evaluated with sKi67 as gold standard. eKi67 and sKi67 correlation was evaluated by Pearson’s index. Results 112 pNEN patients were enrolled. Correlation between eKi67 and sKi67 values was good (coefficient r= 0.78). On EUS-FNA specimens 56/112 (50%) patients were classified as G1, 53 (47.3%) as G2 and 3 (2.7%) as G3, while on surgery they were respectively 59 (52.7%), 50 (44.6%) and 3 (2.7%). In 9.8% grade was increased from G1 to G2 by surgical histology, while in 12.8% it was diminished from G2 to G1. No misclassification occurred in G3 patients. Considering only patients with small tumours (< 2 cm), similar misclassification rate was observed respectively in 9.5% (4/48) and 21.4% (n=9) of cases. Sensitivity, specificity, positive and negative predictive values and accuracy of eKi67 to correctly classify G2 patients were respectively 78.4%, 77.4%, 73.6%, 81.4% and 77.7%. No predictive factors of misclassification were found at multiple regression analysis. Conclusions This study represents the largest cohort of surgical pNEN patients with preoperative eKi67 evaluation. We found a good correlation between eKi67 and sKi67, but about 20% of patients are not correctly allocated in grading classes. This should be carefully considered especially in small tumours undergoing observation.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 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".