Objective: Current standard-of-care technologies are unable to distinguish IPMN at high-risk of malignancy from low-risk lesions. The objective of this study was to create a single-platform assay to identify IPMN that are at high-risk for malignant progression. Methods: Building on the Verona consensus conference BD-IPMN biomarker study; specific protein, cytokine, mucin, and miRNA cyst fluid targets were identified for creation of a q-PCR based assay. A multi-institutional international IPMN cyst fluid collaborative contributed patient samples to validate this platform. Cyst fluid gene expression levels were processed to obtain RQ values that were normalized, z-transformed, and utilized in classification and regression analysis by a support vector machine (SVM) training algorithm. Results: From 59 cyst fluid samples, principal component analysis confirmed no institutional bias/clustering. Sixty percent of eligible samples were randomized to a training set, followed by SVM model optimization with 10-fold cross-validation, and then applied to a test set. The model was repeated 100 times and performance determined by ROC analysis. Machine learning methods classified samples into low-risk (low/moderate dysplasia) or high risk (high-grade dysplasia/invasive cancer). The assay accurately discriminated high from low-risk cysts with a c-statistic (AUC) of 0.83 (figure). Conclusion: We have identified a single-platform PCR-based assay using multiple targets to predict IPMN with high-malignant potential. The creation of this test may allow patients with low-risk IPMN to avoid pancreatic surgery, while identifying patients with high-risk lesions so that they may undergo surgery before the development of invasive disease.