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.

Introduction: Changes in the number and functional capacity of T lymphocytes have been reported in chronic lymphocytic leukemia (CLL) patients. The aim of this study was to examine the prognostic significance of T-lymphocyte subgroups in CLL patients. Methods: Eighty-three previously untreated patients were retrospectively enrolled and flow cytometry results at diagnosis were examined. No difference was found in T-lymphocyte parameters according to age, gender, and disease stage. Results: The CD4 and CD7 percentages, CD4/MBC (malignant B cell), CD8/MBC, and CD7/MBC values at diagnosis were significantly lower in patients with a progressive disease. T-lymphocyte percentages were significantly lower in deceased patients. In the univariate regression model, T-lymphocyte percentages, T-lymphocyte/MBC ratios, HLA-DR+ percentage, Rai stage (intermediate + high risk), Binet stage (B+C), and beta-2 microglobulin level had significant effects on both progression-free survival (PFS) and overall survival (OS); treatment status (yes) had a significant effect only on PFS, while age at diagnosis (≥65 years) had a significant effect only on OS. In the multivariate regression model, Rai stage, CD7/MBC ratio, and treatment status (yes) had a significant effect on PFS; Rai stage and CD8/MBC ratio had a significant effect on OS. Conclusion: Lower T-lymphocyte/MBC ratios at diagnosis could be a marker for higher risk of CLL progression.