Journal
PANCREAS
Volume 51, Issue 6, Pages 586-592Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MPA.0000000000002080
Keywords
pancreas; MRI; CT; volume; perfusion; artificial intelligence; 3D; three-dimensional; ACL; Artificial Intelligence Core Lab; AI; artificial intelligence; AP; acute pancreatitis; CBD; common bile duct; CIAL; Core Image Analysis Lab; CT; computed tomography; DCE MRI; dynamic contrast-enhanced MRI; DM; diabetes mellitus; DWI; diffusion-weighted imaging; eGFR; estimated glomerular filtration rate; EPD; exocrine pancreas dysfunction; IVIM; intravoxel incoherent motion; DL; deep learning; DREAM; Diabetes RElated to Acute pancreatitis and its Mechanisms; IMMINENT; Imaging Morphology of Pancreas in Diabetic Patients Following Acute Pancreatitis; MRCP; MR cholangiopancreatography; MRI; magnetic resonance imaging; Pre-DM; prediabetes; SegCaps; deep capsule-based segmentation networks; SIR; signal intensity ratio; SFTP; secure file transfer protocol; T1DAPC; Type 1 Diabetes in Acute Pancreatitis Consortium; T1D; type 1 diabetes mellitus; T2D; type 2 diabetes mellitus
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Funding
- National Institute of Diabetes and Digestive and Kidney Diseases [U01DK127384, U01DK127367, U01DK127377, U01DK127392, U01DK127382, U01DK127403, U01DK127404, U01DK127388, U01DK127395, U01DK127378, U01DK127400]
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The core component of the DREAM study aims to use advanced MRI techniques to predict diabetes mellitus after acute pancreatitis. Longitudinal MRI observations will allow for a better understanding of the natural history of post-AP DM. The study will also correlate MRI parameters with metabolic, genetic, and immunological phenotypes to develop a quantitative composite risk score.
This core component of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study will examine the hypothesis that advanced magnetic resonance imaging (MRI) techniques can reflect underlying pathophysiologic changes and provide imaging biomarkers that predict diabetes mellitus (DM) after acute pancreatitis (AP). A subset of participants in the DREAM study will enroll and undergo serial MRI examinations using a specific research protocol. The aim of the study is to differentiate at-risk individuals from those who remain euglycemic by identifying parenchymal features after AP. Performing longitudinal MRI will enable us to observe and understand the natural history of post-AP DM. We will compare MRI parameters obtained by interrogating tissue properties in euglycemic, prediabetic, and incident diabetes subjects and correlate them with metabolic, genetic, and immunological phenotypes. Differentiating imaging parameters will be combined to develop a quantitative composite risk score. This composite risk score will potentially have the ability to monitor the risk of DM in clinical practice or trials. We will use artificial intelligence, specifically deep learning, algorithms to optimize the predictive ability of MRI. In addition to the research MRI, the DREAM study will also correlate clinical computed tomography and MRI scans with DM development.
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