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Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance

Authors Guo C, Ren S, Chen X, Wang Q, Xiao W, Zhang J, Duan S, Wang Z

Received 20 November 2018

Accepted for publication 28 January 2019

Published 4 March 2019 Volume 2019:11 Pages 1933—1944


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Ahmet Emre Eskazan

Chuan-gen Guo,1,* Shuai Ren,2,* Xiao Chen,2 Qi-dong Wang,1 Wen-bo Xiao,1 Jing-feng Zhang,1 Shao-feng Duan,3 Zhong-qiu Wang2

1Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University, Hangzhou 310003, China; 2Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China; 3GE Healthcare, Shanghai 201203, China

*These authors contributed equally to this work

Purpose: The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance.
Patients and methods: PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.
Results: There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989.
Conclusion: Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

Keywords: magnetic resonance imaging, neuroendocrine tumor, pancreas, texture analysis

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