Endoscopic mucosal resection for Barrett’s neoplasia: Long-term outcomes from the largest Canadian single-center experience
Bibliographic record
Abstract
Background and study aims: Endoscopic mucosal resection (EMR) remains an important treatment for high-grade dysplasia (HGD) and early esophageal adenocarcinoma (EAC) in Barrett's esophagus (BE). However, there are limited data regarding long-term recurrence rates. This study aimed to investigate the neoplasia recurrence rate following EMR with long-term follow-up. Methods: This was a retrospective cohort study at a tertiary-referral center in Canada. Patients with Barrett's neoplasia (HGD/EAC) treated with EMR between January 2001 and December 2023 were included. The primary outcome was long-term neoplasia recurrence rate after complete remission of neoplasia (CRN). Secondary outcomes were residual/metachronous neoplasia rate at first follow-up, CRN rate, and long-term rate of patients successfully managed by endoscopy. Results: A total of 552 patients (83.7% male, mean age 66.3 years) were included (HGD: 22.5%, EAC: 77.5%). After EMR, 475 patients were deemed to have had successful endoscopic resection (low lymph-node metastasis risk with tumor-free deep margin), 455 of whom underwent surveillance follow-up. At first follow-up, residual/metachronous neoplasia was observed in 20.9% (95/455), but 95.6% (435/455) eventually achieved CRN after undergoing a median of two EMR sessions (interquartile range: 1-4). As a primary outcome, the 5-year neoplasia recurrence rate was 10.5%, the 10-year rate was 21.6%, and the 15-year rate was 34.9%. During surveillance, neoplasia recurrence was observed in 38 patients, but 68.4% of them (26/38) were managed with endoscopic therapy. The overall rate of patients successfully managed by endoscopy was 93.0% (423/455). Conclusions: While the success rate of EMR for BE is excellent, this study highlights substantial long-term risk of neoplastic recurrence, underscoring the need for indefinite surveillance for patients who had HGD or EAC.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".