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Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis

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...

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Autores principales: Kunimatsu, Akira, Kunimatsu, Natsuko, Kamiya, Kouhei, Watadani, Takeyuki, Mori, Harushi, Abe, Osamu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japanese Society for Magnetic Resonance in Medicine 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5760233/
https://www.ncbi.nlm.nih.gov/pubmed/28638001
http://dx.doi.org/10.2463/mrms.mp.2017-0044
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author Kunimatsu, Akira
Kunimatsu, Natsuko
Kamiya, Kouhei
Watadani, Takeyuki
Mori, Harushi
Abe, Osamu
author_facet Kunimatsu, Akira
Kunimatsu, Natsuko
Kamiya, Kouhei
Watadani, Takeyuki
Mori, Harushi
Abe, Osamu
author_sort Kunimatsu, Akira
collection PubMed
description 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 T(1)-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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spelling pubmed-57602332018-01-19 Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis Kunimatsu, Akira Kunimatsu, Natsuko Kamiya, Kouhei Watadani, Takeyuki Mori, Harushi Abe, Osamu Magn Reson Med Sci Major Paper 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 T(1)-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. Japanese Society for Magnetic Resonance in Medicine 2017-06-22 /pmc/articles/PMC5760233/ /pubmed/28638001 http://dx.doi.org/10.2463/mrms.mp.2017-0044 Text en © 2017 Japanese Society for Magnetic Resonance in Medicine This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Major Paper
Kunimatsu, Akira
Kunimatsu, Natsuko
Kamiya, Kouhei
Watadani, Takeyuki
Mori, Harushi
Abe, Osamu
Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
title Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
title_full Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
title_fullStr Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
title_full_unstemmed Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
title_short Comparison between Glioblastoma and Primary Central Nervous System Lymphoma Using MR Image-based Texture Analysis
title_sort comparison between glioblastoma and primary central nervous system lymphoma using mr image-based texture analysis
topic Major Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5760233/
https://www.ncbi.nlm.nih.gov/pubmed/28638001
http://dx.doi.org/10.2463/mrms.mp.2017-0044
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