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Record W4323351200 · doi:10.1093/jcag/gwac036.156

A156 OUTCOMES FOLLOWING ENDOSCOPIC RESECTION OF GASTRIC NEUROENDOCRINE TUMOURS FROM A TERTIARY-CARE ACADEMIC CENTRE

2023· article· en· W4323351200 on OpenAlexaffabout
Sonia Gupta, Gurmun Singh Brar, Kai Zheng, Shaheed W. Hakim, Christopher Teshima, Gary R. May, Calvin Law, Julie Hallet, J. Mosko

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsSunnybrook Health Science CentreSt. Michael's HospitalHealth Sciences CentreSt Joseph's Health CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineEndoscopic mucosal resectionEndoscopyCohortRetrospective cohort studyIncidence (geometry)Neuroendocrine tumorsSurgeryTherapeutic endoscopyStomachInternal medicine

Abstract

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Abstract Background Gastric neuroendocrine tumours (G-NET) are rare cancers derived from neuroendocrine cells of the stomach. A steady increase in the incidence of these tumours has been observed. Current treatment and surveillance strategies are guided by various tumour characteristics including size, grade, and depth of invasion. There exists conflicting evidence, however, on the rates of recurrence from positive resection margins following primary endoscopic resection. Thus, it remains uncertain whether complete endoscopic resection (R0) of these indolent tumours is clinically significant and whether follow-up endoscopic or surgical intervention is justified. Purpose Our aim is to characterize current management patterns and clinical outcomes in patients undergoing endoscopic resection of G-NETs. Method We conducted a retrospective, single-centre cohort study at The Centre for Advanced Therapeutic Endoscopy and Endoscopic Oncology at St. Michael’s Hospital, Toronto, Ontario. Consecutive patients over the age of 18 who underwent endoscopic resection of histologically proven G-NETs between 2011 and 2020 were included. Data on patient, endoscopic, and tumour characteristics were collected through electronic chart review. Descriptive statistics were conducted for data analysis. Result(s) A total of 155 foregut neuroendocrine tumours were endoscopically resected during the study period, of which 108 were identified as G-NETs. 95.3% were classified as Type I. Mean tumour size was 8.93 ± 5.27 mm. Cap-assisted EMR was performed most frequently (n=51), followed by conventional EMR (n=35). ESD was performed in eight cases. Seven intra-procedural perforations occurred, of which all were closed endoscopically. One patient experienced post-procedural perforation requiring ICU and surgery. Positive resection margins (R1) were found in 25% of cases (n=27), of which 78% were assessed at surveillance endoscopy 1 (SE1). Six patients with R1 margins were referred for surgical evaluation and four were lost to follow-up. 78% of all resected G-NETs were followed at SE1 with a median interval of 196 days (range, 23 to 3373). SE1 recurrence rate at the primary resection site was 14% (n=12), of which two were from routine scar biopsies in the absence of endoscopically identifiable recurrence. All visible recurrences at these sites (n=10) were managed with repeat endoscopic resection. Patient and tumour characteristics in the evaluation of G-NET recurrence are presented in Table I. Image Conclusion(s) G-NET recurrence occurs in less than 15% of patients at surveillance endoscopy following endoscopic resection in spite of a predictably higher R1 resection rate. Patient, endoscopic, and tumour factors including method of resection and margin status do not appear to impact the development of early recurrence. Given the indolent nature of these tumours, patients with positive resection margins can be followed conservatively. Further investigation is warranted to determine the optimal duration and surveillance strategy for these patients. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.290
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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