Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium

Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC)

doi:10.1097/MPA.0000000000002080

Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium

Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC)

Radiology

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	586-592
Number of pages	7
Journal	Pancreas
Volume	51
Issue number	6
DOIs	https://doi.org/10.1097/MPA.0000000000002080
State	Published - Jul 1 2022

Funding

Besides the funding support from NIH listed about, C.E.F. receives consultant fee or honorarium from Nestle HealthCare Nutrition, Inc, Parexel International Corp, and Medialis, Ltd. M.D.B. is an advisory board member of Insulet and receives research support from Dexcom and Viacyte. S.J.P. owns stock options of Avenzoar Pharmaceuticals, Phyteau, and Lucid Sciences. W.G.P. is a consultant for AbbVie. B.S. is a consultant for Francis Medical and Botimage. T.T. receives royalties from Springer Nature. J.R.G. receives royalties from Elsevier, Inc. D.K.A. receives royalties from McGraw-Hill. R.E.P. is consultant for Bayer AG, Corcept Therapeutics Incorporated, Dexcom, Gasherbrum Bio, Inc, Hanmi Pharmaceutical Co, Hengrui (USA) Ltd, Merck, Novo Nordisk, Pfizer, Rivus Pharmaceuticals, Inc, Sanofi, Scohia Pharma Inc, and Sun Pharmaceutical Industries and receives speaker fees from Novo Nordisk. The other authors declare no conflict of interest. The Type 1 Diabetes in Acute Pancreatitis Consortium is funded by the National Institute of Diabetes and Digestive and Kidney Diseases: grants U01DK127384, U01DK127367, U01DK127377, U01DK127392, U01DK127382, U01DK127403, U01DK127404, U01DK127388, U01DK127395, U01DK127378, and U01DK127400. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (NIH).

Keywords

3D - three-dimensional
ACL - Artificial Intelligence Core Lab
AI - artificial intelligence
AP - acute pancreatitis
CBD - common bile duct
CIAL - Core Image Analysis Lab
CT
CT - computed tomography
DCE MRI - dynamic contrast-enhanced MRI
DL - deep learning
DM - diabetes mellitus
DREAM - Diabetes RElated to Acute pancreatitis and its Mechanisms
DWI - diffusion-weighted imaging
EPD - exocrine pancreas dysfunction
IMMINENT - Imaging Morphology of Pancreas in Diabetic Patients Following Acute Pancreatitis
IVIM - intravoxel incoherent motion
MRCP - MR cholangiopancreatography
MRI
MRI - magnetic resonance imaging
Pre-DM - prediabetes
SFTP - secure file transfer protocol
SIR - signal intensity ratio
SegCaps - deep capsule-based segmentation networks
T1D - type 1 diabetes mellitus
T1DAPC - Type 1 Diabetes in Acute Pancreatitis Consortium
T2D - type 2 diabetes mellitus
artificial intelligence
eGFR - estimated glomerular filtration rate
pancreas
perfusion
volume

ASJC Scopus subject areas

Internal Medicine
Endocrinology, Diabetes and Metabolism
Hepatology
Endocrinology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC) (2022). Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium. Pancreas, 51(6), 586-592. https://doi.org/10.1097/MPA.0000000000002080

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title = "Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium",

abstract = "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.",

keywords = "3D - three-dimensional, ACL - Artificial Intelligence Core Lab, AI - artificial intelligence, AP - acute pancreatitis, CBD - common bile duct, CIAL - Core Image Analysis Lab, CT, CT - computed tomography, DCE MRI - dynamic contrast-enhanced MRI, DL - deep learning, DM - diabetes mellitus, DREAM - Diabetes RElated to Acute pancreatitis and its Mechanisms, DWI - diffusion-weighted imaging, EPD - exocrine pancreas dysfunction, IMMINENT - Imaging Morphology of Pancreas in Diabetic Patients Following Acute Pancreatitis, IVIM - intravoxel incoherent motion, MRCP - MR cholangiopancreatography, MRI, MRI - magnetic resonance imaging, Pre-DM - prediabetes, SFTP - secure file transfer protocol, SIR - signal intensity ratio, SegCaps - deep capsule-based segmentation networks, T1D - type 1 diabetes mellitus, T1DAPC - Type 1 Diabetes in Acute Pancreatitis Consortium, T2D - type 2 diabetes mellitus, artificial intelligence, eGFR - estimated glomerular filtration rate, pancreas, perfusion, volume",

author = "{Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC)} and Temel Tirkes and Chinchilli, {Vernon M.} and Ulas Bagci and Parker, {Jason G.} and Xuandong Zhao and Dasyam, {Anil K.} and Nicholas Feranec and Grajo, {Joseph R.} and Shah, {Zarine K.} and Poullos, {Peter D.} and Benjamin Spilseth and Atif Zaheer and Xie, {Karen L.} and Wachsman, {Ashley M.} and Martha Campbell-Thompson and Conwell, {Darwin L.} and Fogel, {Evan L.} and Forsmark, {Christopher E.} and Hart, {Phil A.} and Pandol, {Stephen J.} and Park, {Walter G.} and Pratley, {Richard E.} and Cemal Yazici and Laughlin, {Maren R.} and Andersen, {Dana K.} and Jose Serrano and Bellin, {Melena D.} and Dhiraj Yadav",

year = "2022",

month = jul,

day = "1",

doi = "10.1097/MPA.0000000000002080",

language = "English (US)",

volume = "51",

pages = "586--592",

journal = "Pancreas",

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publisher = "Lippincott Williams and Wilkins",

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Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC) 2022, 'Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium', Pancreas, vol. 51, no. 6, pp. 586-592. https://doi.org/10.1097/MPA.0000000000002080

Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium. / Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC).
In: Pancreas, Vol. 51, No. 6, 01.07.2022, p. 586-592.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study

T2 - From the Type 1 Diabetes in Acute Pancreatitis Consortium

AU - Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC)

AU - Tirkes, Temel

AU - Chinchilli, Vernon M.

AU - Bagci, Ulas

AU - Parker, Jason G.

AU - Zhao, Xuandong

AU - Dasyam, Anil K.

AU - Feranec, Nicholas

AU - Grajo, Joseph R.

AU - Shah, Zarine K.

AU - Poullos, Peter D.

AU - Spilseth, Benjamin

AU - Zaheer, Atif

AU - Xie, Karen L.

AU - Wachsman, Ashley M.

AU - Campbell-Thompson, Martha

AU - Conwell, Darwin L.

AU - Fogel, Evan L.

AU - Forsmark, Christopher E.

AU - Hart, Phil A.

AU - Pandol, Stephen J.

AU - Park, Walter G.

AU - Pratley, Richard E.

AU - Yazici, Cemal

AU - Laughlin, Maren R.

AU - Andersen, Dana K.

AU - Serrano, Jose

AU - Bellin, Melena D.

AU - Yadav, Dhiraj

PY - 2022/7/1

Y1 - 2022/7/1

N2 - 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.

AB - 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.

KW - 3D - three-dimensional

KW - ACL - Artificial Intelligence Core Lab

KW - AI - artificial intelligence

KW - AP - acute pancreatitis

KW - CBD - common bile duct

KW - CIAL - Core Image Analysis Lab

KW - CT

KW - CT - computed tomography

KW - DCE MRI - dynamic contrast-enhanced MRI

KW - DL - deep learning

KW - DM - diabetes mellitus

KW - DREAM - Diabetes RElated to Acute pancreatitis and its Mechanisms

KW - DWI - diffusion-weighted imaging

KW - EPD - exocrine pancreas dysfunction

KW - IMMINENT - Imaging Morphology of Pancreas in Diabetic Patients Following Acute Pancreatitis

KW - IVIM - intravoxel incoherent motion

KW - MRCP - MR cholangiopancreatography

KW - MRI

KW - MRI - magnetic resonance imaging

KW - Pre-DM - prediabetes

KW - SFTP - secure file transfer protocol

KW - SIR - signal intensity ratio

KW - SegCaps - deep capsule-based segmentation networks

KW - T1D - type 1 diabetes mellitus

KW - T1DAPC - Type 1 Diabetes in Acute Pancreatitis Consortium

KW - T2D - type 2 diabetes mellitus

KW - artificial intelligence

KW - eGFR - estimated glomerular filtration rate

KW - pancreas

KW - perfusion

KW - volume

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UR - http://www.scopus.com/inward/citedby.url?scp=85139571746&partnerID=8YFLogxK

U2 - 10.1097/MPA.0000000000002080

DO - 10.1097/MPA.0000000000002080

M3 - Article

C2 - 36206463

AN - SCOPUS:85139571746

SN - 0885-3177

VL - 51

SP - 586

EP - 592

JO - Pancreas

JF - Pancreas

IS - 6

ER -

Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC). Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium. Pancreas. 2022 Jul 1;51(6):586-592. doi: 10.1097/MPA.0000000000002080

Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes after Acute Pancreatitis in the D iabetes RE lated to A cute Pancreatitis and Its M echanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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