The Application of EUS-FNA in the Diagnosis of Pancreatic Cancer

The field of pancreatic cancer diagnosis is of paramount importance due to the disease's notoriously poor prognosis, with a five-year survival rate lingering below 10%. Accurate diagnosis of pancreatic lesions is critical for clinicians to make informed treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) has emerged as a pivotal diagnostic tool for pancreatic mass lesions, widely adopted in clinical practice for its role in early and precise detection, thereby significantly enhancing patient outcomes. Recent advancements in technology, particularly the integration of artificial intelligence (AI), have introduced new dimensions to the application of EUS-FNA/B in pancreatic cancer. AI promises to augment the accuracy, efficiency, and reliability of pathological assessments, potentially guiding more targeted interventions. Despite these advancements, the current body of evidence regarding the efficacy and safety of EUS-FNA/B in pancreatic cancer remains insufficiently detailed. Furthermore, the integration of AI and other advanced technologies faces technological, procedural, and regulatory hurdles that need to be addressed to fully harness their potential in clinical settings.

This research topic aims to explore the application of EUS-FNA in the diagnosis of pancreatic cancer. We seek to elucidate the true impact of EUS-FNA/FNB on various types of pancreatic tumors and to generate robust evidence for specific pancreatic diseases. Our objectives include developing a primary decision-making strategy for patients with pancreatic cancers and identifying which population groups would derive the most benefit from EUS-FNA/FNB, all supported by high-level clinical evidence. Additionally, we aim to investigate the potential benefits of combining EUS-FNA with AI and other advanced technologies.

To gather further insights into the application of EUS-FNA in pancreatic cancer diagnosis, we welcome articles addressing, but not limited to, the following themes:
- The efficacy of EUS-FNA/FNB in diagnosing different types of pancreatic tumors.
- The role of AI in enhancing the accuracy and efficiency of EUS-FNA/B.
- Technological advancements and challenges in integrating AI with EUS-FNA/B.
- Development of decision-making strategies for the use of EUS-FNA/FNB in clinical practice.
- Identification of patient populations that would benefit most from EUS-FNA/FNB.
- Evaluation of potential adverse events associated with EUS-FNA/B and AI integration.
- Regulatory and procedural considerations for the adoption of advanced technologies in clinical settings.

Keywords: EUS-FNA/FNB, Pancreatic Cancer, Diagnosis

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