S1797 Outcomes Following Endoscopic Resection of Duodenal Neuroendocrine Tumors
Bibliographic record
Abstract
Introduction: Duodenal neuroendocrine tumors (D-NET) are rare cancers derived from neuroendocrine cells of the duodenum. A steady increase in the incidence of these tumors has been observed. Current management strategies are guided by various tumor characteristics including size, grade, and depth of invasion. There exists conflicting evidence, however, on the rates of recurrence after positive resection margins (R1) following endoscopic resection. Thus, it remains uncertain whether complete endoscopic resection (R0) of these indolent tumors is clinically significant and whether follow-up endoscopic or surgical intervention is justified. Our aim is to characterize the management and clinical outcomes in patients undergoing endoscopic resection of D-NETs. Methods: We conducted a retrospective, single-centre cohort study. Consecutive patients over the age of 18 who underwent endoscopic resection of histologically proven D-NETs between 2011 and 2020 were included. Data on patient, endoscopic, and tumor characteristics were collected through electronic chart review. Descriptive statistics were conducted for data analysis. Results: A total of 47 D-NETs were endoscopically resected amongst 43 patients (Table 1). Mean tumor size was 9.88 ± 6.86 mm. Conventional endoscopic mucosal resection (EMR) was performed most frequently (55%, n=26/47), followed by caP-assisted EMR (30%, n=14/47). Hybrid endoscopic submucosal dissection (ESD)/EMR was performed in 1 case. A total of 2 intra-procedural perforations occurred, both of which were successfully closed endoscopically. One patient with a peri-ampullary D-NET experienced significant intra-procedural bleeding requiring endoscopic intervention and endotracheal intubation resulting in a brief hospitalization. 57% of all resected D-NETs were followed at surveillance endoscopy 1 (SE1) at a median interval of 199 days (range, 84 to 830). R1 margins were found in 26 cases (55%), of which 16 were assessed at SE1. Of these, 1 patient was electively treated with APC and 2 were referred to surgery. Tumor recurrence occurred in 2 patients. Conclusion: D-NETs recur in less than 5% of patients at initial surveillance endoscopy following endoscopic resection. In spite of a high R1 resection rate, our study suggests that patients with these indolent tumors who have positive margins can be managed conservatively with surveillance endoscopy. Further investigation is warranted to determine the optimal duration and interval surveillance strategy for these patients. Table 1. - Characterizing Endoscopic Management and Clinical Outcomes for D-NETs (n=47) Variable Value Baseline demographics Age in years, mean (SD) 64 (10.4) Female sex, n (%) 18 (41.9) Charlson comorbidity index, mean (SD) 1.59 (2.26) NET positive on previous biopsy, n (%) 33 (70.2) Endoscopic ultrasound performed prior to resection, n (%) 30 (63.8) Method of resection Conventional EMR, n (%) 26 (55.3) CaP-assisted EMR, n (%) 14 (29.7) Hybrid ESD/EMR, n (%) 1 (2.1) Technical success rate, % 100 Tumor grade (WHO classification, 2019) G1, n (%) 26 (55.3) G2, n (%) 17 (36.2) G3, n (%) 0 (0) Unspecified, n (%) 4 (8.5) Tumor size < 5 mm, n (%) 4 (8.5) 5 – 9 mm, n (%) 22 (46.8) > 10 mm or greater, n (%) 16 (34.0) Unspecified, n (%) 5 (10.6) Tumor invasion Muscularis propria, n (%) 3 (6.4) Lymphovascular invasion, n (%) 2 (4.3) Perineural invasion, n (%) 0 (0) Tumor resection margins R1, n (%) 26 (55.3) R0, n (%) 16 (34.0) Cannot be assessed or unspecified, n (%) 5 (10.6) Total number lesions surveilled at SE1, n (%) 27 (57.4) Time to SE1 in days, median (range) 199 (84 – 830) Management of R1 margins Surveillance endoscopy alone, n (%) 14 (53.8) Endoscopic intervention, n (%) 1 (3.8) Surgical referral, n (%) 2 (7.7) Lost to follow-up, n (%) 9 (34.6) Patients with recurrence at SE1, n (%) 2 (4.6) Time to last recurrence-free endoscopy in years, median (range) 2.6 (0.4 – 6.6) D-NET, duodenal neuroendocrine tumor; EMR, endoscopic mucosal resection; ESD, endoscopic submucosal dissection; SE1, first surveillance endoscopy; SD, standard deviation; WHO, World Health Organization; R1, positive resection margin; R0, complete resection.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".