Berzin, Tyler M

INTRODUCTION: Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is frequently used to risk-stratify pancreatic cystic lesions (PCLs). Rising PCL incidence and developments in tissue acquisition and specimen analysis necessitate updated appraisal of EUS-FNA safety, particularly the risk of postprocedure pancreatitis, the most common EUS-FNA-related adverse event. Our systematic review aims to accurately quantify the risk of EUS-FNA-related pancreatitis to best inform decisions regarding EUS-FNA's optimal role in PCL workup. METHODS: We performed systematic searches in 4 databases from inception to April 2024 for original English-language studies investigating EUS-FNA-related pancreatitis. We extracted data on demographics and EUS-FNA-related pancreatitis risk, severity, and risk factors. These were meta-analyzed through the DerSimonian Laird Method using a random-effects model. Meta-regression of pancreatitis risk was performed to delineate associations with clinical and procedural characteristics. RESULTS: Sixty-four studies comprised 8,086 patients and reported 110 EUS-FNA-related pancreatitis events. Pooled risk of EUS-FNA-related pancreatitis was 1.4% (95% confidence intervals, −0.8% to 3.5%; I 2 = 0.00), which was predominantly of mild severity (67%) and uniformly nonfatal. Pancreatitis risk lacked significant association with sample size, age, sex, cyst size, needle caliber, or passes, although we noted trends toward higher risk in studies published after 2015, those using higher gauge needles (19 G vs 22 G/25 G), and those performing EUS-guided through-the-needle biopsy. DISCUSSION: We note with high certainty that pancreatitis after EUS-FNA of PCLs is infrequent and mild in severity with no mortality in the included cohort. EUS-guided through-the-needle biopsy may serve as a significant risk factor for EUS-FNA-related pancreatitis risk; however, further studies are needed to delineate other predisposing characteristics.

The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost‐effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer‐aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health‐care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe‐related cancer prevention; Statement 1.4: Health‐care delivery systems and authorities should evaluate the cost‐effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer‐aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health‐care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health‐care delivery systems and authorities should evaluate the cost‐effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high‐quality cost‐effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health‐care systems.