A selective resection algorithm for barrett’s neoplasia optimizes oncological outcomes
Bibliographic record
Abstract
Aims Accepted oncological principles advise en-bloc, R0, excision of cancer to achieve a curative resection. In Barrett’s neoplasia, this includes T1a and superficial-T1b disease. While treatment options include endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD), a mechanism for appropriate technique selection has not been described or validated. Methods We conducted a prospective multi-centre observational study to evaluate the performance of a selective resection algorithm (SRA) for Barrett’s neoplasia. To achieve an en-bloc resection, ESD was selected if there was a suspicion for≥T1a disease and the lesion was>15 mm (January 2017 to April 2024). This was compared with a historical approach (HA), where ESD was only performed in cases suspicious for deep submucosal invasion (February 2013 to December 2016). Results A total of 581 resections were performed in 542 patients. Median lesion size was 20 mm (IQR 10-30). EMR was performed in 354 (60.9%) and ESD in 227 (39.1%). The SRA cohort included 392 (67.5%) cases, and HA cohort 189 (32.5%). Histology was T1a adenocarcinoma in 177 (30.5%) and T1b in 94 (16.2%). For T1a disease, en-bloc resection (SRA 110 [83.3%] vs HA 22 [48.9%]; P<0.001), R0 excision (SRA 91 [68.9%] vs HA 17 [37.8%]; P<0.001) and curative resection (SRA 77 [58.3%] vs HA 14 [31.1%]; P=0.002) were higher in the SRA cohort. Lesions undergoing ESD were likely to be larger (29.9±17.6 mm vs 16.8±11.7 mm; P<0.001). The rates of en-bloc resection (ESD 94 [97.9%] vs EMR 38 [46.9%]; P<0.001), R0 excision (ESD 85 [88.5%] vs EMR 23 [28.4%], P<0.001) and curative resection (ESD 72 [75.0%] vs EMR 19 [23.5%]; P<0.001) were higher in the ESD group. Recurrence was lower in the ESD group (7 [7.3%] vs. 17 [20.9%], P=0.008). For T1b disease, en-bloc resection (SRA 70 [95.9%] vs. HA 9 [42.9%]; P<0.001), R0 excision (SRA 52 [71.2%] vs HA 2 [9.5%]; P<0.001) and curative resection (SRA 20 [27.4%] vs HA 0 [0%]; P=0.005) were higher in the SRA cohort. Lesions undergoing ESD were likely to be larger (33.5±18.0 mm vs 19.0±14.7 mm; P=0.007). The rates of en-bloc resection (ESD 76 [98.7%] vs EMR 3 [17.6%]; P<0.001), R0 excision (ESD 54 [70.1%] vs EMR 0 [0%], P<0.001) and curative resection (ESD 19 [24.7%] vs EMR 0 [0%]; P<0.001) were higher in the ESD group. Recurrence was lower in the ESD group (4 [5.2%] vs. EMR 4 [23.5%], P=0.014). Among all T1a and T1b-SM1 cases with favorable histology, which underwent ESD, 86/99 (86.9%) were curative resections. Conclusions A selective resection algorithm optimizes oncologic outcomes for Barrett’s adenocarcinoma and mitigates the risk of piecemeal resection of cancers. Publication History Article published online: 27 March 2025 © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".