About this Research Topic
Accurate diagnosis of pancreatic lesions is crucial for clinicians to ensure appropriate treatment decisions, particularly in the case of pancreatic cancer, which represents an extremely poor prognosis, with a five-year survival rate below 10%. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) has been regarded as a front-line diagnostic tool for pancreatic mass lesions over the years, which has been widely used in clinical practice and is essential for early and accurate detection, significantly improving patient prognosis. Furthermore, the emergence of advanced technology, such as artificial intelligence (AI), has brought new opportunities and challenges for the application of this technique in pancreatic cancer recently. AI integration in EUS-FNA/B holds significant promise for improving the diagnosis and management of solid pancreatic cancer by enhancing the accuracy, efficiency, and reliability of pathological assessments and guiding targeted interventions. However, current evidence for the true importance of EUS FNA/B in pancreatic cancer is not robust or detailed, and the potential advert events need to be investigated. Technological, procedural, and regulatory challenges must also be addressed to fully realize the potential of AI and other advanced technology in clinical practice.
In this Research Topic, we aim to bring together great insights into the application of EUS-FNA in the diagnosis of pancreatic cancer. We seek to determine the real impact of EUS-FNA/FNB on different kinds of pancreatic tumors and generate a high level of evidence for the specific pancreatic disease. Moreover, we want to develop a primary decision-making strategy for the population with pancreatic cancers and identify which population groups would most benefit from EUS-FNA/FNB, supported by high-level evidence for clinical practice. Additionally, research with a particular focus on the potential benefit of EUS-FNA combined with AI and other potential advanced technology is highly welcome.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
In this Research Topic, we aim to bring together great insights into the application of EUS-FNA in the diagnosis of pancreatic cancer. We seek to determine the real impact of EUS-FNA/FNB on different kinds of pancreatic tumors and generate a high level of evidence for the specific pancreatic disease. Moreover, we want to develop a primary decision-making strategy for the population with pancreatic cancers and identify which population groups would most benefit from EUS-FNA/FNB, supported by high-level evidence for clinical practice. Additionally, research with a particular focus on the potential benefit of EUS-FNA combined with AI and other potential advanced technology is highly welcome.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
Keywords: EUS-FNA/FNB, Pancreatic Cancer, Diagnosis
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