Endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) enables easy and accurate pathological assessment. Here, we compared and assessed the area of samples on glass slides for three needle types: a 19-gauge Franseen needle (Acquire, Boston Scientific, Natick, MA, USA), a 22-gauge Franseen needle, and a 19-gauge fine-needle aspiration (FNA) needle (EZ Shot 3 Plus; Olympus, Tokyo, Japan). Among patients with suspected pancreatic cancer, with a ≥20 mm tumor located in the pancreatic body and tail, and who underwent EUS-FNA or FNB between June 2018 and March 2020, 10 were randomly selected to test each needle. The areas of histological tissue and blood clot samples were measured using the BZ-X800 imaging software (Keyence Corporation, Osaka, Japan). Baseline patient characteristics and pathological sample data showed no significant differences among the needles. The 19-gauge Franseen needle obtained significantly more histological tissue samples than the 19-gauge conventional needle (p = 0.010) and 22-gauge Franseen needle (p = 0.008). Conversely, there was no significant difference between the 19-gauge conventional needle and 22-gauge Franseen needle (p = 0.838) in this regard. The 19-gauge Franseen needle could collect more samples than the other needles, contributing to giving a more precise pathological diagnosis and more information, including genomic profiling.

Gallbladder (GB) disease is classified into two broad categories: GB wall-thickening and protuberant lesions, which include various lesions, such as adenomyomatosis, cholecystitis, GB polyps, and GB carcinoma. This review summarizes recent advances in the differential diagnosis of GB lesions, focusing primarily on endoscopic ultrasound (EUS) and related technologies. Fundamental B-mode EUS and contrast-enhanced harmonic EUS (CH-EUS) have been reported to be useful for the diagnosis of GB diseases because they can evaluate the thickening of the GB wall and protuberant lesions in detail. We also outline the current status of EUS-guided fine-needle aspiration (EUS-FNA) for GB lesions, as there have been scattered reports on EUS-FNA in recent years. Furthermore, artificial intelligence (AI) technologies, ranging from machine learning to deep learning, have become popular in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification. In this review, we outline the current status of AI in the diagnosis of GB.