Introduction

Endoscopic Submucosal Dissection (ESD) is the best endoscopic technique that enables en bloc resection of superficial colorectal neoplasms.Despite its effectiveness, ESD is not very widespread in Western countries due to technical complexity, long procedure times, and a steep learning curve. These barriers have created a significant gap in adoption and outcomes between Eastern and Western centers. Underwater ESD (U-ESD) has been introduced to simplify the procedure. This study aimed to evaluate the effectiveness of U-ESD versus conventional gas-based ESD (G-ESD) in a Western center, focusing on procedural efficiency and safety.

Aims & Methods

This is a single-center, prospective randomized controlled trial conducted at Fondazione Campus Bio-Medico in Rome between December 2024 and April 2025.Forty patients with non-pedunculated colorectal lesions were randomly assigned in a 1:1 ratio to undergo either Underwater ESD (U-ESD) or conventional ESD with insufflation of Carbon Dioxide (CO2) (G-ESD).The primary outcome was total procedure time. Secondary outcomes included dissection speed, en bloc and R0 resection rates, use of hemostatic forceps, and the incidence of adverse events such as delayed bleeding, perforation, and post-ESD coagulation syndrome (PECS).

Results

A total of 40 patients referred to our endoscopic unit for ESD between December 2024 and April 2025. The mean size of the colorectal lesions was 57.4 mm and 40.7 mm in the U-ESD and G-ESD groups. The location of the lesions was in the rectum in 15% and 55% of the U-ESD and C-ESD groups, respectively; in the right side of the colon in 35% and 20% of the U-ESD and G-ESD groups, respectively. The morphology of LST was classified as LST-nongranular in 20 % and 25% of the U-ESD and G-ESD groups, respectively; as LST granular in 60% and 35% of the U-ESD and G-ESD groups, respectively. Fibrosis (grade 1 and 2) was present in 45% and 30% of the U-ESD and G-ESD groups, respectively. The mean total procedure time was shorter in the U-ESD group compared with the G-ESD group (53.3 ±33 minutes vs 78.7 ± 25.6 minutes; mean difference,–25.4 minutes; P=0.009).Mean dissection speed was faster in the U-ESD group compared with the G-ESD group (42 ± 26.3 mm2/min vs 13.3 ± 8 mm2/min; mean difference, 28.7 mm2/min; P= 0.001). En bloc and R0 resection rates in both groups were 100%. The use of a hemostatic forceps was significantly lower in U-ESD compared G-ESD (40% vs 90%, P= 0.001). Delayed bleeding occurred in only one patient in G-ESD group and was managed successfully by endoscopic clipping. No perforations occurred in either group. PECS was noted in only one patient in the G-ESD group.

Conclusion

UESD has shown a significantly shorter procedure time and faster dissection speed compared with GESD. These both empower the possibility of considering U-ESD as the preferred colorectal ESD method for colorectal lesions.

Introduction

The BIOSTOP Trial is a prospective, multicenter, randomized, controlled, open-label study conducted in Norway to evaluate the two-year outcomes of tumor necrosis factor-alpha (anti-TNF) antibody withdrawal in patients with ulcerative colitis (UC) in sustained clinical and endoscopic remission. Prior non-randomized studies have suggested that approximately half of such patients maintain remission after anti-TNF discontinuation and that reintroduction of anti-TNF therapy is generally effective and safe in the event of relapse.

Aims & Methods

A total of 174 UC patients in confirmed clinical and endoscopic remission were enrolled across 19 gastroenterology centers between June 2017 and December 2020 and randomized 1:1 to either continue or discontinue anti-TNF therapy. Eligibility criteria included at least 12 months of first-line anti-TNF maintenance treatment, clinical remission, fecal calprotectin <200 µg/g, and endoscopic remission (Mayo endoscopic score 0 or 1). Two-year follow-up data were available for 172 patients (86 in each group). In 156 of the patients endoscopies were available for central reading (77 in the withdrawal group and 79 in the maintenance group). The primary endpoint was sustained endoscopic remission at two years.

Results

At two years, central reading of follow-up colonoscopies confirmed endoscopic remission in 64 of 77 patients (83%, 95% CI 71.8 – 93.1) in the anti-TNF withdrawal group, compared to 77 of 79 patients (96 %, 95 % CI 92.1 – 100.8) in the anti-TNF maintenance group. The non-inferiority primary endpoint of the study was thus not met. During the study period, 48% of patients in the withdrawal group and 88% in the maintenance group remained in sustained clinical and endoscopic remission without relapse. Among the 45 patients in the withdrawal group who experienced endoscopic confirmed relapse, 28 (62%) resumed anti-TNF therapy, with 26 achieving both clinical and endoscopic remission. Additionally, 10 of 13 patients with mild relapses achieved endoscopic remission with optimized non-biologic therapy. There were no difference in serious adverse events between the groups.

Conclusion

After two years, 83% of patients in the anti-TNF withdrawal group and 96% in the maintenance group were in clinical and endoscopic remission, including those who experienced flares and achieved new remission after reintroduction of anti-TNF or optimized non-biologic therapy. Nearly half of the patients maintained remission without anti-TNF, and the majority of relapses were effectively managed. These findings suggest that anti-TNF withdrawal is a safe option for selected UC patients in confirmed remission, provided they are monitored closely.
Trial Registration: ClinicalTrials.gov Identifier: NCT03011268; EudraCT Number: 2016-001409-18. Trial completion date: 31 January 2025.

Disclosure

No disclosures

Introduction

Deep learning systems in gastrointestinal endoscopy are traditionally pretrained on large, diverse datasets comprising generic, non-medical images before being trained on scarce endoscopic data. Foundation models are models pretrained based on domain-specific datasets. For endoscopy, foundation models require large datasets of general endoscopic images. Yet datasets for developing such models remain limited. In this study, we present GastroNet-5M, a dataset comprising 4,820,653 endoscopic images of approximately 500,000 procedures.

Aims & Methods

GastroNet-5M is composed of anonymized endoscopic images collected from eight Dutch hospitals between 2012 and 2020. Using a self-supervised learning approach, this dataset enabled the development of a foundation model for various endoscopic AI applications. In this study, we compared GastroNet-5M-pretrained models against current state-of-the-art models with traditional pretraining approaches and all publicly available endoscopic foundation models.
We evaluated diagnostic performance on 9 classification and 9 segmentation tasks, including Barrett’s neoplasia detection, colorectal polyp characterization, and gastric cancer invasion depth prediction. All models were trained from scratch for each task. Data efficiency was assessed by repeating all classification tasks with progressively smaller training sets (i.e. 100%, 50%, 25%, 10% and 5%). Robustness was evaluated across 12 datasets reflecting data heterogeneity such as variation in endoscope manufacturer, imaging modality (e.g., virtual chromoendoscopy), and image quality.

Results

GastroNet-5M-pretrained models outperformed all benchmarks in 8 of 9 classification tasks, with an average AUC improvement of 6.8% (Table 1). For segmentation, they achieved higher Dice scores compared to all other models across all 9 tasks, with an average gain of 15.4%. In data efficiency experiments, GastroNet-5M models achieved the highest AUC in 5 of 9 tasks when training data was limited, while no other model led in more than one. In robustness testing, they outperformed other models in 8 of 12 tasks evaluating heterogeneous data conditions.

Table 1. Area-Under-the-Curve scores of the GastroNet-5M-pretrained model and benchmark models for all classification tasks.

Conclusion

GastroNet-5M, a multicenter dataset of over 5 million unlabeled endoscopic images, offers a valuable resource for pretraining deep learning models in endoscopy. The use of GastroNet-5M enhances diagnostic accuracy, reduces the need for scarce application-specific endoscopic imagery, and improves robustness of endoscopic AI systems to the inevitable data heterogeneity in clinical practice. GastroNet-5M is publicly accessible for further research and development.

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