The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34-68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25-64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.

Recently renamed, metabolic dysfunction-associated steatotic liver disease remains a leading cause of chronic liver disease worldwide. Regular physical activity is recommended as a treatment for all with this condition because it is highly efficacious, especially when exercise training is undertaken with a specific goal in mind. Despite decades of research demonstrating exercises efficacy, key questions remain about the mechanism of benefit and most efficacious dose, as well as the independent impact on liver histology. To answer these questions, we present the design of a 16-week randomized controlled clinical trial of 45 adults aged 18-69 years with metabolic dysfunction-associated steatohepatitis. The primary aim of this study is to better understand the dose required and mechanisms to explain how exercise impacts multiple clinical end points in metabolic dysfunction-associated steatohepatitis. The primary outcome is MRI-measured liver fat. Secondary outcomes include other biomarkers of liver fibroinflammation, liver histology, and mechanistic pathways, as well as cardiometabolic risk and quality of life. This is the first study to compare different doses of exercise training to determine if there is a differential impact on imaging and serum biomarkers as well as liver histology.