Yazıcı, Cemal

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.

Abstract Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM after AP and understanding the underlying mechanisms. The DREAM (Diabetes RElated to Acute Pancreatitis and its Mechanisms) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM after AP. In the following article, we summarize literature regarding the epidemiology of DM after AP and provide the rationale and an overview of the DREAM study.

AbstractBackground and study aims Endoscopic ultrasound (EUS)-guided tissue sampling is the standard of care for diagnosing solid pancreatic lesions. While many two-way comparisons between needle types have been made in randomized controlled trials (RCTs), it is unclear which size and type of needle offers the best probability of diagnosis. We therefore performed a network meta-analysis (NMA) to compare different sized and shaped needles to rank the diagnostic performance of each needle.Methods We searched MEDLINE, EMBASE and Cochrane Library databases through August, 2020 for RCTs that compared the diagnostic accuracy of EUS fine-needle aspiration (FNA) and biopsy (FNB) needles in solid pancreatic masses. Using a random-effects NMA under the frequentist framework, RCTs were analyzed to identify the best needle type and sampling technique. Performance scores (P-scores) were used to rank the different needles based on pooled diagnostic accuracy. The NMA model was used to calculate pairwise relative risk (RR) with 95 % confidence intervals.Results Review of 2577 studies yielded 29 RCTs for quantitative synthesis, comparing 13 different needle types. All 22G FNB needles had an RR > 1 compared to the reference 22G FNA (Cook) needle. The highest P-scores were seen with the 22G Medtronic FNB needle (0.9279), followed by the 22G Olympus FNB needle (0.8962) and the 22G Boston Scientific FNB needle (0.8739). Diagnostic accuracy was not significantly different between needles with or without suction.Conclusions In comparison to FNA needles, FNB needles offer the highest diagnostic performance in sampling pancreatic masses, particularly with 22G FNB needles.