A Novel System for Precise Grading of Glioma
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 co...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/10/532 |
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| author | Ahmed Alksas Mohamed Shehata Hala Atef Fatma Sherif Norah Saleh Alghamdi Mohammed Ghazal Sherif Abdel Fattah Lamiaa Galal El-Serougy Ayman El-Baz |
| author_facet | Ahmed Alksas Mohamed Shehata Hala Atef Fatma Sherif Norah Saleh Alghamdi Mohammed Ghazal Sherif Abdel Fattah Lamiaa Galal El-Serougy Ayman El-Baz |
| author_sort | Ahmed Alksas |
| collection | DOAJ |
| description | 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 <i>k</i>-fold stratified (with <i>k</i> = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at <i>k</i> = 10 and 5. Alternative classifiers, including RFs and SVM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>l</mi><mi>i</mi><mi>n</mi></mrow></msub></semantics></math></inline-formula> produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma. |
| first_indexed | 2024-03-09T20:40:49Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T20:40:49Z |
| publishDate | 2022-10-01 |
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| series | Bioengineering |
| spelling | doaj.art-7089970eafc44f56a3e7823d45e95f962023-11-23T22:57:27ZengMDPI AGBioengineering2306-53542022-10-0191053210.3390/bioengineering9100532A Novel System for Precise Grading of GliomaAhmed Alksas0Mohamed Shehata1Hala Atef2Fatma Sherif3Norah Saleh Alghamdi4Mohammed Ghazal5Sherif Abdel Fattah6Lamiaa Galal El-Serougy7Ayman El-Baz8Bioengineering Department, University of Louisville, Louisville, KY 40292, USABioengineering Department, University of Louisville, Louisville, KY 40292, USADepartment of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, EgyptDepartment of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, EgyptDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaElectrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab EmiratesDepartment of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, EgyptDepartment of Radiology, Faculty of Medicine, Mansoura University, Mansoura 35516, EgyptBioengineering Department, University of Louisville, Louisville, KY 40292, USAGliomas 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 <i>k</i>-fold stratified (with <i>k</i> = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at <i>k</i> = 10 and 5. Alternative classifiers, including RFs and SVM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>l</mi><mi>i</mi><mi>n</mi></mrow></msub></semantics></math></inline-formula> produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.https://www.mdpi.com/2306-5354/9/10/532GG-CADMRIsHOGGLCMGLRLMADC |
| spellingShingle | Ahmed Alksas Mohamed Shehata Hala Atef Fatma Sherif Norah Saleh Alghamdi Mohammed Ghazal Sherif Abdel Fattah Lamiaa Galal El-Serougy Ayman El-Baz A Novel System for Precise Grading of Glioma Bioengineering GG-CAD MRIs HOG GLCM GLRLM ADC |
| title | A Novel System for Precise Grading of Glioma |
| title_full | A Novel System for Precise Grading of Glioma |
| title_fullStr | A Novel System for Precise Grading of Glioma |
| title_full_unstemmed | A Novel System for Precise Grading of Glioma |
| title_short | A Novel System for Precise Grading of Glioma |
| title_sort | novel system for precise grading of glioma |
| topic | GG-CAD MRIs HOG GLCM GLRLM ADC |
| url | https://www.mdpi.com/2306-5354/9/10/532 |
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