Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas

Purpose: To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas. Material and methods: A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas. Results: There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%. Conclusion: TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas

The value of the apparent diffusion coefficient value in the Liver Imaging Reporting and Data System (LI-RADS) version 2018

Purpose: To assess role of the apparent diffusion coefficient (ADC) in the Liver Imaging Reporting and Data System (LI-RADS) version 2018 for the prediction of hepatocellular carcinoma (HCC). Material and methods: Retrospective analysis of 137 hepatic focal lesions in 108 patients at risk of HCC, who underwent magnetic resonance imaging of the liver. Hepatic focal lesions were classified according to LI-RADS-v2018, and ADC of hepatic lesions was calculated by 2 independent blinded reviewers. Results: The mean ADC of LR-1 and LR-2 were 2.11 ± 0.47 and 2.08 ± 0.47 × 10-3 mm2/s, LR-3 were 1.28 ± 0.12 and 1.36 ± 0.16 × 10-3 mm2/s, LR-4, LR-5 and LR-TIV were 1.07 ± 0.08 and 1.08 ± 0.12 × 10-3 mm2/s and LR-M were 1.02 ± 0.09 and 1.00 ± 0.09 × 10-3 mm2/s by both observers, respectively. There was excellent agreement of both readings for LR-1 and LR-2 (r = 0.988), LR-3 (r = 0.965), LR-4, LR-5 and LR-TIV (r = 0.889) and LR-M (r = 0.883). There was excellent correlation between ADC and LI-RADS-v2018 (r = –0.849 and –0.846). The cut-off ADC used to differentiate LR-3 from LR-4, LR-5, and LR-TIV were ≤ 1.21 and ≤ 1.23 × 10-3 mm2/s with AUC of 0.948 and 0.926. Conclusions: Inclusion of ADC to LI-RADS-v2018 improves differentiation variable LI-RADS categories and can helps in the prediction of HCC

Differentiation between high-grade gliomas and metastatic brain tumors using Diffusion Tensor Imaging metrics

Introduction: The aim of this work was to differentiate between high-grade gliomas and metastatic brain tumors using diffusion tensor derived metrics in the enhancing tumor and peri-tumoral regions. Patients & methods: Prospective study was done on 36 patients with provisional MRI diagnosis of high grade gliomas WHO grade III & IV versus metastatic brain tumors, examination was done on 1.5 tesla scanner, patients were divided into two groups based on pathology results, the fraction anisotropy (FA), mean diffusivity (MD), linear coefficient (CL), planer coefficient (CP) and spherical coefficient (CS) were measured in the enhancing tumor parts and immediate peri-tumoral edema and results were compared between the two groups. Results: Values of FA, CL and CP measured in the peri-tumoral edema were significantly high in the metastatic than primary high malignant glial tumors with high specificity (100%) of the CP and high sensitivity of the CL (76.5%) among the three significant values, and no significant differences in the values of MD and CS. The values of the five metrics measured in the enhancing tumor parts showed no significant differences between the two groups. Conclusion: Brain metastasis and high-grade gliomas can be differentiated using DTI derived FA, CL and CP measured in the peri-tumoral region

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 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