Pancreatic cysts are increasingly recognized as incidental lesions due to the widespread use of cross-sectional imaging techniques such as CT and MRI. Twenty percent of all pancreatic cysts are non-inflammatory cystic neoplasms, many of which are mucinous, and therefore have a significant potential for malignant transformation. The primary challenge facing clinicians is the inability of current standard diagnostic tests, including imaging and standard laboratory data, to accurately and reliably discriminate neoplastic from non-neoplastic, and benign from malignant cysts, particularly in patients without symptoms. Therefore, some patients with benign cysts undergo unnecessary costly surveillance and possible avoidable surgical resections, which is associated with high cost, significant morbidity and mortality, while some may not be as aggressively followed. The International Association of Pancreatology (IAP) has published guidelines on the management of the two main types of mucinous cystic lesions: intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs). (1) Although these guidelines emphasize the frequency of surveillance and outline the indications for resection, these guidelines have not yet been validated, and these guidelines presume the diagnosis of a mucinous neoplastic cyst has been confirmed prior to surgery. It is unlikely that guidelines that only use clinical and imaging will be able to accurately differentiate benign cysts from those that are pre-malignant or currently harbor malignancy. Hence, there is a critical need for a diagnostic model that more accurately diagnoses neoplastic mucinous and malignant pancreatic cysts as this will improve the management of many patients with pancreatic cysts.
The aims of this study are to determine the natural history of pancreatic cysts and to propose and prospectively validate a diagnostic approach and model for prediction of mucinous versus non-mucinous, and malignant versus non-malignant, pancreatic cysts using a combination of clinical, radiologic, and biomarker characteristics.