Pancreatic cancer is the fourth leading cause of cancer death in the United States. A major reason for the lethal nature of this disease is the lack of effective strategies for early detection. As a result, the vast majority of cancers are detected at a very late stage. The delay in diagnosis and treatment of pancreatic cancer could be due to many reasons including, 1) lack of a clear quantitative or algorithm-based definition of a high-risk population who would benefit from active surveillance, 2) suboptimal use of image findings that could potentially foretell a growing tumor, 3) system or referral-related delay from time of abnormal finding to diagnosis and treatment. Methods to accelerate the detection of pancreatic cancer leading to increased proportion of early stage tumors at the time of diagnosis have the potential to have an immediate impact on survival. The objective of the proposed work is to establish a platform for development and implementation of a data-driven approach for detection of early stage pancreatic cancer within an integrated care setting. Specifically, the proposed work will focus on development of empiric algorithms for prediction of early stage pancreatic cancer as well as systematic pancreatic cancer-risk stratification of patients based on natural language processing-aided extraction of pancreatic features from existing pre-diagnostic imaging reports to enhance understanding of the natural history of disease progression. Finally, we will conduct a prospective cohort study to assess the accuracy of an algorithm-based approach for detection of early stage pancreatic cancer.