Introduction

Severe submucosal fibrosis (SSF) in early gastric cancer (EGC) is known to cause technical difficulty for endoscopic resection.^{1, 2} Various methods have been reported to tackle this problem. However, there is no established consensus on the management of this situation. Endoscopic intermuscular dissection (EID) has been successfully used in the rectum to overcome SSF.³ The procedure involves the dissection between the circular and longitudinal layers of the muscularis propriae. Recently, the procedure has been used in the stomach for en bloc resection of a neuroendocrine tumor.⁴ We report a case endoscopic resection of early gastric cancer with severe submucosal fibrosis where EID was used to attain a negative vertical margin.

Aims & Methods

We aim to demonstrate the use of EID in the stomach to overcome the challenge of SSF in endoscopic resection of early cancer. An 81 year male underwent an upper endoscopy for heaviness and discomfort in the epigastrium for few months. It revealed an approximately 25mm lesion around a healed ulcer scar at the gastric angle on the lesser curvature side with irregular surface and vascular pattern and a clear demarcation line. Biopsy showed high grade dysplasia and endoscopic resection was planned. Since the lesion was over a healed ulcer scar, and endoscopy showed convergence of gastric folds towards the lesion, SSF was expected. The resection was started by conventional endoscopic submucosal dissection in retroflexion and a tunnel was created under the distal side of the tumor. However, after the incision and initial submucosal dissection, we encountered SSF. We decided to carefully dissect in the intermuscular plane underneath the fibrotic area by identifying and separating the circular and longitudinal muscle layer. Similar method was used on the oral side where EID was performed after initial ESD and tunnel was completed. Resection was accomplished and the circular muscle defect in the middle of the ulcer was closed with multiple endoclips.

Results

Total procedure time was 60 minutes. En bloc specimen measured 56mm x 40mm. There were no immediate or early complications. Patient was started on soft diet on post procedure day 1 and discharged on day 5. Pathology was tubular adenoma with high grade dysplasia. Horizontal and vertical margins were negative and specimen contained the muscle layer.

Conclusion

This case demonstrates the feasibility of EID in the stomach without any special accessories or equipment. EID can be used to overcome severe submucosal fibrosis in early gastric cancer. Further studies on wider applicability of gastric EID in clinical practice would be worthwhile.

References

1. Jeong JY, Oh YH, Yu YH et al. Does submucosal fibrosis affect the results of endoscopic submucosal dissection of early gastric tumors?. Gastrointestinal endoscopy. 2012 jul 1;76(1):59-66.
2. Higashimaya M, Oka S, Tanaka S et al. Outcome of endoscopic submucosal dissection for gastric neoplasm in relationship to endoscopic classification of submucosal fibrosis. Gastric Cancer. 2013 Jul;16:404-10.
3. Toyonaga T, Ohara Y, Baba S et al. Peranal endoscopic myectomy (PAEM) for rectal lesions with severe fibrosis and exhibiting the muscle-retracting sign. Endoscopy. 2018 aug;50(08):813-7.
4. Despott EJ, Lucaciu LA, Murino A et al. First report of gastric endoscopic intermuscular dissection. Endoscopy. 2024 Dec;56(S 01):E132-3.

Introduction

EUS-guided Gastroenterostomy (EUS-GE) represents a recent breakthrough in therapeutic endosonography, with most existing data derived from retrospective studies focused on short-term technical outcomes. Limited information is available on long-term outcomes, the need for reinterventions and modifications of Lumen Apposing Metal Stents (LAMS) over time.

Aims & Methods

All consecutive EUS-GE performed in a single institution with the Wireless Simplified Technique (WEST) between 2020 and 2024 were enrolled in a prospective registry (PROTECT, ClinicalTrials.gov NCT04813055), with efficacy and safety outcomes assessed every 60 days. The primary outcomes were symptom recurrence after initial clinical success (rate, proportion) and dysfunction-free survival (DyFS) estimated using Kaplan Meier curves. Endoscopic LAMS reassessments were used to calculate the time to first modification of the stent and surrounding tissues, whilst Radiologic imaging was used to estimate the change in stent calibre (linear regression).

Results

164 EUS-GEs (median age 66 [60-74], male 52.4%, malignant indication 99.4% [pancreatic cancer 73.2%, metastatic 58.3%]) were included. A 20 mm LAMS was used in 98.8%, dilated up to 18 mm in 98.8% of cases. Technical and Clinical success rates were 98.8% and 96.3%, respectively. Adverse events (AEs) were registered in 15.2% (procedure-related 6.1%, severe/fatal 3.7%).
Over a median follow-up of 112 [40-190] days, symptom recurrence occurred in 9.1%, but was related to stent dysfunctions in 10/154 (6.5%) cases. Stent dysfunctions happened after a median of 231 [67-321] days from the procedure, all of which (100%) were successfully managed endoscopically. Estimated DyFS at 6 and 12 months was 97.5% and 82.6%, respectively.
Among 38 patients undergoing endoscopic LAMS re-evaluation for different reasons, tissue reactions on the gastric or jejunal side were observed in 34.2% after a median of 190 [33-351] days, and LAMS ingrowth was seen in 13.2% after a median of 421 [250-560] days. A total of 132 CT scans from 75 patients were analysed, with regression analysis revealing a reduction in LAMS diameter of ≈ 0.25 mm + 0.03 every 2 months (F-test p<.0001).

Conclusion

In the natural history of EUS-GE, subclinical LAMS changes and tissue reactions can be frequently detected during follow-up along with a progressive narrowing of LAMS diameter. However, most of these modifications are asymptomatic, with a 6.5% rate of endoscopic reinterventions, all performed successfully. Analysis of these curves may be helpful to plan scheduled reinterventions, especially in cases with benign indication or in long-term survivors.

Introduction

Accurate diagnosis of pancreatic lesions poses a considerable challenge due to the involvement of multiple examinations in the diagnostic process. Endoscopic ultrasound (EUS) stands out as a valuable tool enabling characterization and tissue sampling of both cystic and solid lesions, thereby influencing treatment decisions and follow-up strategies significantly. Pancreatic cystic neoplasms (PCNs) encompass mucinous (M-PCN) and non-mucinous lesions (NM-PCN), while pancreatic adenocarcinoma (P-ADC) remains the most prevalent cause of pancreatic solid lesions (PSL), trailed by pancreatic neuroendocrine tumors (P-NET). Despite the potential benefits of artificial intelligence, particularly deep learning algorithms, in EUS, research on the role of convolutional neural networks (CNNs) in pancreatic lesions is scarce.

Aims & Methods

This multicentric study aims to develop a CNN tailored for the detection and differentiation of PCNs (specifically M-PCN and NM-PCN) as well as PSLs (particularly P-ADC and P-NET). Our group developed a retrospective multicenter study based on patients submitted to EUS for pancreatic lesion characterization. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-ADC from P-NET. The total dataset was divided into a training and testing dataset (used for model’s evaluation) in a 90/10% ratio. The model performance was evaluated through its sensitivity, specificity, positive and negative predictive values and accuracy. The Sci-kit learn v0.22.2 was used for statistical analysis of the model.

Results

A total of 378 videos of EUS performed for evaluation of pancreatic lesions during November 2017 and December 2023 in four reference centers (Hospital Universitario Puerta de Hierro Majadahonda, n=234; Centro Hospitalar Universitário São João, n=33; New York University Langone and Manhattan Hospital, n=60; Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, n=51) were used for development of the model. A total of 126.000 frames were extracted for development of CNN: 64.286 of P-ADC, 29.153 of P-NET, 4.858 of normal pancreas, 19.528 of M-PCN and 8.175 of NM-PCN.
Pancreatic Cystic Neoplasms.
The trinary CNN was developed in order to distinguish between normal pancreas images and cystic lesions, differentiating M-PCN from NM-PCN. The deep learning model identified normal pancreas images with 98.6% sensitivity, 99.2% specificity and 99.1% accuracy. M-PCN were detected with 98.9% sensitivity, 99.1% specificity, a PPV of 99.4% and NPV of 98.3%, with a global accuracy of 99%. Finally, the testing dataset of our CNN revealed 99.3% sensitivity, 99.9% specificity, 99.9% PPV, 99.8% NPV and 99.8% accuracy for the diagnosis of NM-PCN.
Pancreatic Solid Lesions
The binary CNN was developed to differentiate between the two commoner PSL, namely P-ADC and P-NET. The testing dataset revealed 94.0% accuracy for the distinction between the lesions, with 98.7% and 83.7% sensitivities for P-ADC and PNET, respectively.

Conclusion

Our group developed a deep learning model capable of differentiating the main types of pancreatic solid lesions, as well as pancreatic cystic lesions, with high performance values. To our knowledge this is the first multicentric approach for the development of an AI algorithm designed to simultaneously characterize cystic and solid pancreatic lesions.. The development of deep learning models may help differentiate pancreatic lesions.Larger multicentric and prospective studies are needed for the validation and future clinical implementation of this technology.

References

Kuwahara T, Hara K, Mizuno N, et al. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN Open. 2024;4(1):e267.