S5228 Real-World Use of EUS-Guided Ethanol Ablation for Small pNETs: Patient-Centered Alternatives to Surgery
Bibliographic record
Abstract
Introduction: Pancreatic neuroendocrine tumors (pNETs) are increasingly diagnosed incidentally, with small, non-functional lesions often presenting a management dilemma. While surgical resection remains the standard of care for localized disease, it carries significant morbidity, particularly for lesions in the pancreatic head. Endoscopic ultrasound-guided ethanol ablation (EUS-EA) has emerged as a minimally invasive, pancreas-sparing alternative for select patients, including those who are medically inoperable, prefer organ-preserving therapy, or have hereditary tumor predisposition syndromes such as multiple endocrine neoplasia type 1 (MEN1). Despite growing international experience, real-world data on EUS-EA in Canadian practice remain limited. Case Description: We retrospectively reviewed 3 cases (ages 34-87) of biopsy-confirmed WHO Grade 1 pNETs measuring ≤1.5 cm, including one with MEN1 syndrome. All patients underwent EUS-guided ethanol ablation using 0.5-1.2 mL of 100% ethanol under conscious sedation. Procedures were performed by interventional endoscopists. Follow-up imaging at 3-6 months assessed treatment response, and patients were monitored clinically for complications and symptom recurrence. Discussion: All 3 patients underwent technically successful EUS-EA without procedure-related adverse events. Two achieved complete radiographic response after a single session. One elderly patient with recurrent insulinoma required a second ablation for residual disease, with resolution of hypoglycemia without recurrence. Functional outcomes were favorable across all cases. One patient had confirmed MEN1; genetic counseling and endocrine follow-up were arranged. No pancreatitis or bleeding occurred. This case series demonstrates the feasibility, efficacy, and safety of EUS-EA performed by interventional endoscopists for small, low-grade pNETs in a Canadian tertiary care setting. These findings support EUS-EA as a surgery-sparing option for carefully selected patients, particularly those unfit for resection or seeking minimally invasive alternatives. Our results align with emerging data on the use of ablative therapies in pNETs, demonstrating high technical success and favorable functional outcomes. Ongoing surveillance and larger prospective studies are warranted to define long-term durability and recurrence rates.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".