ArticleBioengineering (Basel, Switzerland)2022
A Novel System for Precise Grading of Glioma.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Leveraging deep learning for prognostic stratification of hepatocellular carcinoma using histopathological images.Frontiers in oncology · 2026Article
- Pilot research on predicting the sub-volume with high risk of tumor recurrence inside peritumoral edema using the ratio-maxiADC/meanADC from the advanced MRI.Discover oncology · 2025Article
- Review
- A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.Scientific reports · 2024Article
- Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques.Scientific reports · 2023Article
- Prediction of Wilms' Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System.Diagnostics (Basel, Switzerland) · 2023Article
- Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities.Frontiers in medicine · 2023Article
- MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation.Frontiers in medicine · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1-79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based 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), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and
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