Magnetic Resonance in Medical Sciences
Online ISSN : 1880-2206
Print ISSN : 1347-3182
ISSN-L : 1347-3182
Major Papers
Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
Akira KunimatsuNatsuko KunimatsuKouhei KamiyaTakeyuki WatadaniHarushi MoriOsamu Abe
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JOURNAL OPEN ACCESS

2018 Volume 17 Issue 1 Pages 50-57

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Abstract

Purpose: To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features.

Methods: This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T1-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL.

Results: Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM.

Conclusions: Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.

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© 2018 by Japanese Society for Magnetic Resonance in Medicine

This article is licensed under a Creative Commons [Attribution-NonCommercial-NoDerivatives 4.0 International] license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
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