Background and Objectives: A majority of pancreatic malignancies are unresectable at the time of presentation and require EUS–guided fine-needle aspiration or fine-needle biopsy (EUS-FNA/FNB) for diagnosis. With the advent of precision therapy, there is an increasing need to use EUS-FNA/FNB sample for genetic analysis. Next-generation sequencing (NGS) is a preferred technology to detect genetic mutations with high sensitivity in small specimens. We performed a meta-analysis to evaluate the adequacy of EUS-FNA/FNB for NGS in pancreatic malignancies. Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched from database inception to November 11, 2023. The primary outcome was the proportion of sufficient sample acquired by EUS-FNA/FNB in pancreatic malignancies for NGS. Secondary outcomes were the proportion of sufficient sample for NGS in pancreatic ductal adenocarcinoma (PDAC) and the detection rates of mutations in KRAS, TP53, CDKN2A, and SMAD4 and actionable mutations in PDAC. The pooled proportions were calculated using a random-effects model. Potential sources of heterogeneity were investigated with subgroup analyses and meta-regression. Results: Twenty studies with 881 samples were included. The pooled adequacy of EUS-FNA/FNB sample for NGS was 89.9% (95% CI, 80.8%–96.7%) in pancreatic malignancies and 92.0% (95% CI, 81.3%–98.8%) in PDAC. Screening sample suitability before NGS testing was associated with lower adequacy in subgroup analysis (79.7% vs. 98.4%, P = 0.001). The pooled prevalences of mutations in KRAS, TP53, CDKN2A, and SMAD4 in PDAC were 87.4% (95% CI, 83.2%–91.2%), 62.6% (95% CI, 53.2%–71.7%), 20.6% (95% CI, 11.9%–30.8%), and 19.4% (95% CI, 11.2%–29.1%), respectively. The pooled prevalence of potentially actionable mutations in PDAC was 14.5% (95% CI, 8.2%–22.0%). Conclusions: In the majority of cases, EUS-FNA/FNB can acquire adequate sample for NGS and identify tumor-specific mutations in patients with pancreatic malignancies. Strict pre-analysis screening criteria may negatively impact the sample adequacy and the success rate for NGS.

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

The diagnosis of early chronic pancreatitis (ECP) is challenging due to the lack of standardized diagnostic criteria. EUS has been considered a sensitive diagnostic modality for chronic pancreatitis (CP), with advancements in technique such as EUS-guided fine needle aspiration and biopsy (EUS-FNA/FNB) being developed. However, their role in the diagnosis of ECP remains unelucidated. This review thereby aimed to provide an overview of the clinical landscape of EUS in the field of ECP.