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Item Design and Rationale for the Use of Magnetic Resonance Imaging Biomarkers to Predict Diabetes After Acute Pancreatitis in the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium(Wolters Kluwer, 2022) Tirkes, Temel; Chinchilli, Vernon M.; Bagci, Ulas; Parker, Jason G.; Zhao, Xuandong; Dasyam, Anil K.; Feranec, Nicholas; Grajo, Joseph R.; Shah, Zarine K.; Poullos, Peter D.; Spilseth, Benjamin; Zaheer, Atif; Xie, Karen L.; Wachsman, Ashley M.; Campbell-Thompson, Martha; Conwell, Darwin L.; Fogel, Evan L.; Forsmark, Christopher E.; Hart, Phil A.; Pandol, Stephen J.; Park, Walter G.; Pratley, Richard E.; Yazici, Cemal; Laughlin, Maren R.; Andersen, Dana K.; Serrano, Jose; Bellin, Melena D.; Yadav, Dhiraj; Type 1 Diabetes in Acute Pancreatitis Consortium (T1DAPC); Radiology and Imaging Sciences, School of MedicineThis 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) following acute pancreatitis (AP). A subset of participants in the DREAM study will enroll and undergo serial MRI examinations using a specific research protocol. We aim to differentiate at-risk individuals from those who remain euglycemic by identifying parenchymal features following 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 computerized tomography and MRI scans with DM development.Item Diagnosis of chronic pancreatitis using semi-quantitative MRI features of the pancreatic parenchyma: results from the multi-institutional MINIMAP study Temel Tirkes1,18, Dhiraj Yadav2(Springer, 2023) Tirkes, Temel; Yadav, Dhiraj; Conwell, Darwin L.; Territo, Paul R.; Zhao, Xuandong; Persohn, Scott A.; Dasyam, Anil K.; Shah, Zarine K.; Venkatesh, Sudhakar K.; Takahashi, Naoki; Wachsman, Ashley; Li, Liang; Li, Yan; Pandol, Stephen J.; Park, Walter G.; Swaroop Vege, Santhi; Hart, Phil A.; Topazian, Mark; Andersen, Dana K.; Fogel, Evan L.; Consortium for the Study of Chronic Pancreatitis, Diabetes, Pancreatic Cancer (CPDPC); Radiology and Imaging Sciences, School of MedicinePurpose: To determine the diagnostic performance of parenchymal MRI features differentiating CP from controls. Methods: This prospective study performed abdominal MRI scans at seven institutions, using 1.5 T Siemens and GE scanners, in 50 control and 51 definite CP participants, from February 2019 to May 2021. MRI parameters included the T1-weighted signal intensity ratio of the pancreas (T1 score), arterial-to-venous enhancement ratio (AVR) during venous and delayed phases, pancreas volume, and diameter. We evaluated the diagnostic performance of these parameters individually and two semi-quantitative MRI scores derived using logistic regression: SQ-MRI Model A (T1 score, AVR venous, and tail diameter) and Model B (T1 score, AVR venous, and volume). Results: When compared to controls, CP participants showed a significantly lower mean T1 score (1.11 vs. 1.29), AVR venous (0.86 vs. 1.45), AVR delayed (1.07 vs. 1.57), volume (54.97 vs. 80.00 ml), and diameter of the head (2.05 vs. 2.39 cm), body (2.25 vs. 2.58 cm), and tail (1.98 vs. 2.51 cm) (p < 0.05 for all). AUCs for these individual MR parameters ranged from 0.66 to 0.79, while AUCs for the SQ-MRI scores were 0.82 and 0.81 for Model A (T1 score, AVR venous, and tail diameter) and Model B (T1 score, AVR venous, and volume), respectively. After propensity-matching adjustments for covariates, AUCs for Models A and B of the SQ-MRI scores increased to 0.92 and 0.93, respectively. Conclusion: Semi-quantitative parameters of the pancreatic parenchyma, including T1 score, enhancement ratio, pancreas volume, diameter and multi-parametric models combining these parameters are helpful in diagnosis of CP. Longitudinal analyses including more extensive population are warranted to develop new diagnostic criteria for CP.