Glioma grading using structural magnetic resonance imaging and molecular data

Syed M. S Reza; Manar D Samad; Zeina A Shboul; Karra A Jones; Khan M Iftekharuddin

doi:10.1117/1.JMI.6.2.024501

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Glioma grading using structural magnetic resonance imaging and molecular data

Journal article

Open access

Peer reviewed

Glioma grading using structural magnetic resonance imaging and molecular data

Syed M. S Reza, Manar D Samad, Zeina A Shboul, Karra A Jones and Khan M Iftekharuddin

Journal of medical imaging (Bellingham, Wash.), Vol.6(2), pp.024501-024501

04/2019

DOI: 10.1117/1.JMI.6.2.024501

PMCID: PMC6479231

PMID: 31037246

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https://doi.org/10.1117/1.JMI.6.2.024501View

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Abstract

A glioma grading method using conventional structural magnetic resonance image (MRI) and molecular data from patients is proposed. The noninvasive grading of glioma tumors is obtained using multiple radiomic texture features including dynamic texture analysis, multifractal detrended fluctuation analysis, and multiresolution fractal Brownian motion in structural MRI. The proposed method is evaluated using two multicenter MRI datasets: (1) the brain tumor segmentation (BRATS-2017) challenge for high-grade versus low-grade (LG) and (2) the cancer imaging archive (TCIA) repository for glioblastoma (GBM) versus LG glioma grading. The grading performance using MRI is compared with that of digital pathology (DP) images in the cancer genome atlas (TCGA) data repository. The results show that the mean area under the receiver operating characteristic curve (AUC) is 0.88 for the BRATS dataset. The classification of tumor grades using MRI and DP images in TCIA/TCGA yields mean AUC of 0.90 and 0.93, respectively. This work further proposes and compares tumor grading performance using molecular alterations ( IDH1/2 mutations) along with MRI and DP data, following the most recent World Health Organization grading criteria, respectively. The overall grading performance demonstrates the efficacy of the proposed noninvasive glioma grading approach using structural MRI.

2 mutant

Computer-Aided Diagnosis

dynamic texture

glioma grading

histopathology image

IDH1

magnetic resonance image

multiresolution fractal

Paper

Details

Title: Subtitle: Glioma grading using structural magnetic resonance imaging and molecular data
Creators: Syed M. S Reza - Old Dominion University
Manar D Samad - Tennessee State University
Zeina A Shboul - Old Dominion University
Karra A Jones - University of Iowa
Khan M Iftekharuddin - Old Dominion University
Resource Type: Journal article
Publication Details: Journal of medical imaging (Bellingham, Wash.), Vol.6(2), pp.024501-024501
DOI: 10.1117/1.JMI.6.2.024501
PMID: 31037246
PMCID: PMC6479231
NLM abbreviation: J Med Imaging (Bellingham)
ISSN: 2329-4302
eISSN: 2329-4310
Publisher: Society of Photo-Optical Instrumentation Engineers
Grant note: R01EB020683 / HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Language: English
Date published: 04/2019
Academic Unit: Pathology
Record Identifier: 9984186535602771

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