Role of Multivoxel Intermediate TE 2D CSI MR Spectroscopy and 2D Echoplanar Diffusion Imaging in Grading of Primary Glial Brain Tumours

Aggarwal, Abhishek; Das, Pankaj; Arvind, Shukla; Parashar, Sagar; Choudhary, Mohini; Kumar, Arpit; Kumar, Narendra; Dutta, Shyamoli

doi:10.7860/jcdr/2017/24982.9984

Cited by 6 publications

(4 citation statements)

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“…It has been confirmed that the alteration in metabolite concentrations such as Cho, Cr, NAA, and also their relative values, including Cho/NAA and Cho/Cr are strongly correlated with the pathology of the tumor (10). In addition to MRS-guided tumor grading (11),…”

Section: Introductionmentioning

confidence: 82%

Assessment of Residual Tumor After Resection of Glioma: A Magnetic Resonance Spectroscopic Study

et al. 2019

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Background: Detection of actual residual tumor extent after resection of gliomas is important for further treatment implications. Conventional MRI features such as T1 weighted contrast enhancement or T2 weighted hyperintensity are not strong indicators of the tumor. Therefore, it is needed to use advanced metabolic imaging such as magnetic resonance spectroscopy (MRS). Objectives: This work reports the contrast between MRS defining metabolic alteration and imaging features of residual tumor after glioma resection. Methods: Eighteen patients with glioma after tumor resection were included in the study. Routine MRI sequences and multi-voxel MRS were obtained. Metabolic regions of interest (ROI) were defined for Cho/NAA and Cho/Cr in different thresholds. Imaging ROI for residual tumor (ROI-t) was defined on conventional MR images. Area of each ROI, the distance between ROI centers, and dice coefficient for the evaluation of similarity between imaging and metabolic ROIs were calculated. Results: Maximum similarity and minimum distance of ROI centers were determined between ROI of Cho/NAA > 1.7 and ROI-t. For Cho/Cr, the maximum similarity was determined in > 1.5. Conclusions: Findings of the present study propose that MRS could be a proper detector for residual tumor after surgical treatment of glioma.

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Section: Introductionmentioning

confidence: 82%

Assessment of Residual Tumor After Resection of Glioma: A Magnetic Resonance Spectroscopic Study

et al. 2019

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“…Previous studies demonstrated that DWI-based glioma classification into the two groups of high-grade (HGG) and low-grade glioma (LGG) was possible with a sensitivity of over 90% [6,29]. Unfortunately, most of these reports have a relatively small patient cohort [30]. In addition, the molecular tumor stratification, used in the present article, correlates better with the clinical outcome than the outdated 2007 CNS WHO classification in HGG and LGG, used by most other research groups, and subsequently enables support of the clinical decision-making process [6,7,29].…”

Section: Discussionmentioning

confidence: 99%

ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging

Nuessle

Behling

Tabatabai

et al. 2021

JCM

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Purpose: To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades. Materials and methods: Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC). Results: Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively). Conclusions: High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.

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“…In glial brain tumor grading; N-Acetyl Aspartate (NAA) is a neuronal integrity marker, choline (Cho) a cell membrane breakdown and turnover marker while creatinine (Cr) is a metabolism marker. Increased glioma grade is related to increased aggression of tumor cells that is associated with rising in Cho and reduction in both NAA and Cr levels as well as increased Cho/NAA and Cho/Cr ratios [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel System for Precise Grading of Glioma

et al. 2022

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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 k-fold stratified (with k = 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 k = 10 and 5. Alternative classifiers, including RFs and SVMlin 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.

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Role of Multivoxel Intermediate TE 2D CSI MR Spectroscopy and 2D Echoplanar Diffusion Imaging in Grading of Primary Glial Brain Tumours

Cited by 6 publications

References 10 publications

Assessment of Residual Tumor After Resection of Glioma: A Magnetic Resonance Spectroscopic Study

Assessment of Residual Tumor After Resection of Glioma: A Magnetic Resonance Spectroscopic Study

ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging

A Novel System for Precise Grading of Glioma

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