Evaluation of cystic neoplasms using EUS, MRI, and CT scan and treatment strategy

Background: Cystic neoplasms, and pancreatic cystic lesions (PCLs) in general, are increasingly a practice dilemma in diagnosis with their increasing incidental discovery and indeterminate risk of malignancy. Characterization is needed to maximize patient outcomes, balancing the risks of overtreatment against the risks of missed malignancy. In this review, endoscopic ultrasonography (EUS), magnetic resonance imaging (MRI), and computed tomography (CT) diagnostic performance, prognostic information, and clinical utility have been extensively reviewed in the imaging and treatment of cystic neoplasms, and most importantly pancreatic neoplasms. Methods We performed narrative review synthesis and comparative analysis of MRI, EUS, and CT technology, including technical principles, diagnostic accuracy, safety profiles, and therapeutic decision-making roles. Focus was given to guideline-directed management approaches, risk stratification factors like mural nodules, cyst size, and main pancreatic duct dilatation, and advancing technologies such as AI-enhanced imaging and biopsy. Results: EUS was better for mural nodule and small cystic lesion detection, particularly in combination with fine-needle aspiration (FNA). MRI offered better non-invasive morphologic description of cyst shape and communication with the duct, and CT was an acceptable first detection device, but less specific to identify mucinous vs. non-mucinous lesions. Multimodal imaging approaches, i.e., combination of EUS and MRI/MRCP, optimized diagnostic accuracy and informed individualized treatment plans. Guideline-based analysis identified variability among large societies (AGA, IAP, ESGE) in surgical thresholds and surveillance guidelines and underscored the need to use individualized, evidence-based decision-making. Conclusion: There is each of a unique niche in the diagnosis and management of cystic neoplasm for MRI, EUS, and CT. A multimodal imaging plan customized to lesion and patient considerations provides maximum diagnostic and treatment planning. The use of new technologies such as AI-assisted image analysis and advanced biopsy could potentially enhance risk stratification and decrease nonessential treatment. Prospective validation of the new technologies as well as standardization of imaging-guided management algorithms will be needed to better optimize outcomes.