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Advanced Endoscopic Ultrasound (EUS) Techniques: Current and Future Directions
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Gastroenterology Department, Hôpital Privé Jean Mermoz, Ramsay Générale de Santé, Lyon, France
Gastroenterology Unit, Hospital of Imola, University of Bologna, 40026 Bologna, Italy
Department of Gastroenterology, Faculty of Medicine in the Galilee, Bar-Ilan University, Nahariya 2210001, Israel
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Advanced Endoscopic Ultrasound (EUS) Techniques: Current and Future Directions

Abstract submission deadline
31 July 2026
Manuscript submission deadline
31 October 2026
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843

Topic Information

Dear Colleagues,

In recent years, we have made significant progress in the field of interventional endoscopy, specifically in therapeutic endoscopic ultrasound (EUS), which drastically extended th therapeutic armamentarium in several gastrointestinal diseases and finally contributed the improvement of patients’ treatment and outcome. Among the therapeutic implications including EUS-guided drainage using lumen apposing metal stents (LAMSs), EUS has been increasingly used for creating gastrointestinal anastomosis, including gastro-jejunostomy and EUS-directed trans-gastric intervention (EDGI), and used in antegrade procedures such as hepatico-gastrostomy and antegrade maneuvers to reach the biliary tree in altered anatomy and inaccessible retrograde biliary approaches. Additionally, some advancements in diagnostic techniques have been made regarding ultrasensitive Doppler, shear wave elastography and 3D EUS as well as molecular analyses of EUS-FNA/FNB specimen in pancreaticobiliary tumors.

Given the rapidly evolving shift in EUS, as part of the following Topic, entitled “Advanced Endoscopic Ultrasound (EUS) Techniques: Current and Future Directions”, we aim to publish outstanding manuscripts focused on advanced EUS techniques. Priority will be given to high-quality original articles, but well-designed, systematic reviews (with or without meta-analysis) and narrative reviews addressing the latest updated insights on a specific topic will be welcomed.

Dr. Bertrand V. Napoleón
Dr. Andrea Lisotti
Dr. Tawfik Khoury
Topic Editors

Keywords

  • diagnostic EUS
  • EUS-guided sampling
  • therapeutic EUS
  • LAMS
  • pancreas
  • cyst biopsy
  • biliary disease
  • portal hypertension
  • radiofrequency ablation
  • elastography

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Cancers
cancers 		4.4 8.8 2009 19.1 Days CHF 2900 Submit
Current Oncology
curroncol 		3.4 4.9 1994 22.8 Days CHF 2200 Submit
Diagnostics
diagnostics 		3.3 5.9 2011 21.6 Days CHF 2600 Submit
Gastroenterology Insights
gastroent 		0.7 2.7 2009 23.8 Days CHF 1800 Submit

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Published Papers (1 paper)

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Review
Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review
by Jacobo Echeverri-Hoyos, Jaime A. Echeverri-Franco, Nicole Bonilla, Gustavo Monsalve-Morales and Eduardo Tuta-Quintero
Curr. Oncol. 2026, 33(5), 287; https://doi.org/10.3390/curroncol33050287 - 13 May 2026
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Abstract
Introduction: Lung cancer remains highly lethal. Endobronchial ultrasound (EBUS) enables minimally invasive diagnosis and staging. Artificial intelligence (AI) improves image analysis and diagnostic accuracy, though current evidence is limited by retrospective, small, single center studies. Methods: A scoping review following Arksey–O’Malley, [...] Read more.
Introduction: Lung cancer remains highly lethal. Endobronchial ultrasound (EBUS) enables minimally invasive diagnosis and staging. Artificial intelligence (AI) improves image analysis and diagnostic accuracy, though current evidence is limited by retrospective, small, single center studies. Methods: A scoping review following Arksey–O’Malley, Levac, and JBI frameworks, was reported as per PRISMA-ScR. Databases were searched for studies (2015–2026) on AI in EBUS. Two reviewers screened, extracted standardized data, and performed narrative synthesis grouped by algorithm type, application, and performance metrics. Results: A total of 26 studies were included. Of these, 73.1% (19/26) employed deep learning-based models, while 26.9% (7/26) used traditional or hybrid machine learning approaches. The most frequent clinical objective was diagnostic classification of malignancy (14/26; 53.8%), followed by segmentation or cytological analysis (5/26; 19.2%), anatomical navigation or lymph node station classification (3/26; 11.5%), and multimodal predictive or staging support models (4/26; 15.4%). Most studies were based on EBUS-derived images or videos (18/26; 69.2%), including both convex-probe and radial-probe applications. Studies were distributed among Convex Probe-EBUS for mediastinal staging, Radial Probe-EBUS for peripheral lesion assessment, and rapid on-site evaluation-based cytology analysis, reflecting diverse clinical contexts. Most models were developed using static images. Conclusions: AI applications in EBUS are predominantly based on deep learning and mainly focused on diagnostic classification, with growing but still limited exploration of segmentation, navigation, and multimodal approaches. The evidence reflects diverse clinical contexts and data sources, particularly image-based inputs, but remains unevenly distributed across applications. Full article
(This article belongs to the Topic Advanced Endoscopic Ultrasound (EUS) Techniques: Current and Future Directions)
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