Prediction of malignancy in cystic neoplasms of the pancreas: a population-based cohort study.

Abstract

Pancreatic cystic neoplasms (PCNs) are being detected with increased frequency. The aims of this study were to determine the incidence of malignancy and develop an imaging-based system for prediction of malignancy in PCN. We conducted a retrospective cohort study of patients ≥18 years of age with confirmed PCN from January 2005 to December 2010 in a community-based integrated care setting in Southern California. Patients with history of acute or chronic pancreatitis were excluded. Malignancy diagnosed within 3 months of cyst diagnosis was considered as pre-existing. Subsequent incidence of malignancy during surveillance was calculated based on person-time at risk. Age- and gender-adjusted standardized incidence ratio (SIR) was calculated with the non-cyst reference population. Recursive partitioning was used to develop a risk prediction model based on cyst imaging features. We identified 1,815 patients with confirmed PCN. A total of 53 (2.9%) of patients were diagnosed with cyst-related malignancy during the study period. The surveillance cohort consisted of 1,735 patients with median follow-up of 23.4 months. Incidence of malignancy was 0.4% per year during surveillance. The overall age- and gender-adjusted SIR for pancreatic malignancy was 35.0 (95% confidence level 26.6, 46.0). Using recursive partitioning, we stratified patients into low (<1%), intermediate (1-5%), and high (9-14%) risk of harboring malignant PCN based on four cross-sectional imaging features: size, pancreatic duct dilatation, septations with calcification as well as growth. Area under the receiver operator characteristic curve for the prediction model was 0.822 (training) and 0.808 (testing). Risk of pancreatic malignancy was lower than previous reports from surgical series but was still significantly higher than the reference population. A risk stratification system based on established imaging criteria may help guide future management decisions for patients with PCN.