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Artificial Intelligence-Assisted Endoscopic Ultrasound-Guided Ablation of Pancreatic Neuroendocrine Tumors: Toward
Ahmed Salman1, Ahmed Elewa2, Ahmed Safina3
1Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, EGY.
Abstract:
Pancreatic neuroendocrine tumors (pNETs) are increasingly detected at an early stage because of the wider use of cross-sectional imaging and endoscopic ultrasound. Their management remains challenging, particularly for small functioning tumors and selected non-functioning lesions, where the risks of pancreatic surgery must be balanced against tumor biology, symptoms, progression risk, and patient preference. Endoscopic ultrasound (EUS)-guided ablation, particularly radiofrequency ablation, has emerged as a minimally invasive, organ-preserving option for carefully selected patients with small pNETs, especially insulinomas and low-risk non-functioning lesions. However, current evidence is limited by small cohorts, heterogeneous techniques, variable follow-up protocols, and uncertainty regarding long-term oncological outcomes. Artificial intelligence (AI) may enhance this evolving field by supporting EUS-based lesion detection, characterization, grading prediction, risk stratification, patient selection, procedural planning, and post-ablation surveillance. AI-assisted models using EUS images, radiomics, pathology, and multimodal clinical data may help identify patients most likely to benefit from ablation while avoiding inappropriate local therapy in biologically aggressive disease. This review summarizes the current role of EUS-guided ablation for pNETs and explores the emerging potential of AI to support precision diagnosis, individualized risk assessment, and personalized minimally invasive therapy.
Insights
Endoscopic ultrasound (EUS)-guided ablation offers a minimally invasive option for early-stage pancreatic neuroendocrine tumors (pNETs). Artificial intelligence (AI) shows promise in improving patient selection and outcomes for this pNETs treatment.
Area of Science:
- Gastroenterology and Oncology
- Medical Imaging and Artificial Intelligence
Background:
- Pancreatic neuroendocrine tumors (pNETs) are increasingly detected early due to advanced imaging.
- Management of small pNETs requires balancing surgical risks with tumor characteristics and patient factors.
- Endoscopic ultrasound (EUS)-guided ablation is an emerging organ-preserving treatment for select pNETs.
Purpose of the Study:
- To review the current role of EUS-guided ablation for pNETs.
- To explore the potential of artificial intelligence (AI) in enhancing pNETs management.
- To highlight AI's role in precision diagnosis, risk assessment, and personalized therapy for pNETs.
Main Methods:
- Review of current literature on EUS-guided ablation for pNETs.
- Exploration of AI applications in EUS-based lesion analysis and patient stratification.
- Discussion of AI's potential in procedural planning and post-ablation surveillance.
Main Results:
- EUS-guided ablation is a viable option for carefully selected small pNETs, but evidence is limited.
- AI can potentially improve EUS-based detection, characterization, and grading prediction of pNETs.
- AI may aid in selecting patients who will benefit most from ablation and avoiding overtreatment.
Conclusions:
- EUS-guided ablation is a promising minimally invasive approach for select pNETs.
- AI integration holds significant potential to personalize pNETs management, from diagnosis to surveillance.
- Further research is needed to validate AI models and long-term oncological outcomes of EUS-guided ablation for pNETs.
