Principal Investigator

Awardee Organization

Kaiser Foundation Research Institute
United States

Fiscal Year
Activity Code
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date

PRedictiOn Algorithms for the DeTECTion of Early Stage Pancreatic Cancer (PRO-TECT)

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.


  • Wu BU. Diabetes and pancreatic cancer: recent insights with implications for early diagnosis, treatment and prevention. Current opinion in gastroenterology. 2021 Sep 1;37(5):539-543. PMID: 34387256
  • Chen W, Zhou Y, Asadpour V, Parker RA, Puttock EJ, Lustigova E, Wu BU. Quantitative Radiomic Features From Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis. Clinical and translational gastroenterology. 2023 Jan 1;14(1):e00548. PMID: 36434803
  • Chen W, Zhou B, Jeon CY, Xie F, Lin YC, Butler RK, Zhou Y, Luong TQ, Lustigova E, Pisegna JR, Wu BU. Machine learning versus regression for prediction of sporadic pancreatic cancer. Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]. 2023 Jun;23(4):396-402. Epub 2023 Apr 27. PMID: 37130760
  • Chen W, Zhou Y, Xie F, Butler RK, Jeon CY, Luong TQ, Zhou B, Lin YC, Lustigova E, Pisegna JR, Kim S, Wu BU. Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer. The American journal of gastroenterology. 2023 Jan 1;118(1):157-167. Epub 2022 Oct 13. PMID: 36227806

Clinical Trials

Study Name Clinical Trial ID
PRedictiOn Algorithms for the DeTECTion of Early Stage Pancreatic Cancer NCT04883450