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

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. PNETs are classified into Grade 1 (G1), Grade 2 (G2), and...

Full description

Saved in:
Bibliographic Details
Published in:Cancer management and research 2019-03, Vol.11, p.1933-1944
Main Authors: Guo, Chuan-Gen, Ren, Shuai, Chen, Xiao, Wang, Qi-Dong, Xiao, Wen-Bo, Zhang, Jing-Feng, Duan, Shao-Feng, Wang, Zhong-Qiu
Format: Article
Language:English
Subjects:
Cancer metastasis
Comparative analysis
Diagnostic imaging
Driving while intoxicated
Magnetic resonance imaging
Medical research
Neuroendocrine tumor
Neuroendocrine tumors
Original Research
Pancreas
Regression analysis
Texture analysis
Tumors
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. 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. 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 (
ISSN:1179-1322
1179-1322
DOI:10.2147/CMAR.S195376