Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) remains the most common EUS-guided tissue acquisition technique. This study aimed to evaluate the performance of a new Franseen tip fine needle biopsy (FNB) device for EUSguided sampling of solid lesions and compare it with the historical FNA technique. Acquire® 22 G FNB needle (Boston Scientific Co., Natick, MA, USA) was used for solid tumor sampling (Study group). Tissue was collected for rapid on-site evaluation, and touch and crush preparations were made. Historical EUS-FNA samples obtained using Expect® 22 G FNA needle (Boston Scientific Co.) were used as controls (Control group). All specimens were independently evaluated by two cytopathologists blinded to the formal cytopathological diagnosis. Mean cell block histology scores were significantly higher (p=0.046) in the FNB group (51 samples) despite a significantly lower (p<0.001) mean number of passes compared to the FNA group (50 specimens). The overall diagnostic yields for the FNB vs. FNA groups were 96% vs. 88%. The degree of tumor differentiation was adequately assessed in all cell block qualifying lesions in the FNB group. Two patients developed post-FNB abdominal pain. The new Franseen tip FNB device provides histologically superior and cytologically comparable specimens to those obtained by FNA, but with fewer passes.

Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012–2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012–2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.

Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide. Pancreatic lesions consist of both neoplastic and non-neoplastic lesions and often pose a diagnostic and therapeutic challenge due to similar clinical and radiological features. In recent years, pancreatic lesions have been discovered more frequently as incidental findings due to the increased utilization and widespread availability of abdominal cross-sectional imaging. Therefore, it becomes imperative to establish an early and appropriate diagnosis with meticulous differentiation in an attempt to balance unnecessary treatment of benign pancreatic lesions and missing the opportunity for early intervention in malignant lesions. Endoscopic ultrasound (EUS) has become an important diagnostic modality for the identification and risk stratification of pancreatic lesions due to its ability to provide detailed imaging and acquisition of tissue samples for analysis with the help of fine-needle aspiration/biopsy. The recent development of EUS-based technology, including contrast-enhanced endoscopic ultrasound, real-time elastography–endoscopic ultrasound, miniature probe ultrasound, confocal laser endomicroscopy, and the application of artificial intelligence has significantly augmented the diagnostic accuracy of EUS as it enables better evaluation of the number, location, dimension, wall thickness, and contents of these lesions. This article provides a comprehensive overview of the role of the different types of EUS available for the diagnosis and differentiation of pancreatic cancer from other pancreatic lesions while discussing their key strengths and important limitations.