Document Type

Article

Publication Date

2019

DOI

10.1117/1.JMI.6.2.024501

Publication Title

Journal of Medical Imaging

Volume

6

Issue

2

Pages

024501 (1-10)

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.

Comments

Copyright 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

Publisher's version available at: http://dx.doi.org/10.1117/1.JMI.6.2.024501

Original Publication Citation

Reza, S. M. S., Samad, M. D., Shboul, Z. A., Jones, K. A., & Iftekharuddin, K. M. (2019). Glioma grading using structural magnetic resonance imaging and molecular data. Journal of Medical Imaging, 6(2), 024501. doi:10.1117/1.JMI.6.2.024501

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