A Comprehensive Non-invasive System for Early Grading of Gliomas

Abstract

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Glioma grading is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive noninvasive multimodal magnetic resonance (MR)-based computeraided diagnostic (CAD) system that has the ability to differentiate between high grade gliomas (HGG) and low grade gliomas (LGG). The proposed glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), gray-level co-occurrence matrix (GLCM), and (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and wash-in slope. These features are then integrated together and processed using a multi-layer perceptron artificial neural networks (MLP-ANN) classification model towards getting the final diagnosis of a glioma as HGG or LGG. The GG-CAD system was evaluated on a total of 82 gliomas (HGG = 42 and LGG = 40) using a k-fold cross-validation approach (k = 82, 10, and 5). The GG-CAD achieved 98.8%±1.0% accuracy, 99.2%±1.1% sensitivity, 98.3%±1.2% specificity, and 0.99%±0.01% F1score at k = 82 and an outstanding diagnostic performance at k = 10 and 5. The obtained diagnostic results hold promise of the developed GG-CAD system as a non-invasive diagnostic tool. © 2022 IEEE. keywords ADC, CAD, GLCM,GLRLM, HOG, MRIs