AUTHOR=Wang Lixia , Gaddam Srinivas , Wang Nan , Xie Yibin , Deng Zixin , Zhou Zhengwei , Fan Zhaoyang , Jiang Tao , Christodoulou Anthony G. , Han Fei , Lo Simon K. , Wachsman Ashley M. , Hendifar Andrew Eugene , Pandol Stephen J. , Li Debiao 

TITLE=Multiparametric Mapping Magnetic Resonance Imaging of Pancreatic Disease

JOURNAL=Frontiers in Physiology

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2020.00008

DOI=10.3389/fphys.2020.00008

ISSN=1664-042X

ABSTRACT=Background: Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MR imaging in characterizing various tissue types in pancreatic diseases such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. 
Purpose: To evaluate the feasibility of multiparametric mapping (T1, T2, and ADC) in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. 
Materials and Methods: Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prep gradient-echo, and multi-slice single-shot echo-planar diffusion-weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of the pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way ANOVA was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed and the areas under the curve (AUCs) were calculated using single parameter and combined parameters, respectively. Results: T1, T2 and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p<0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI:0.92-0.96), which yielded higher accuracy than any single parameter only (p<0.001). Conclusion: Multi-parametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides higher accuracy than any single parameter alone in tissue characterization of the pancreas.