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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.

Background Preventing acute complication of renal angiomyolipoma (AML), preserving renal parenchyma, and improving long-term renal function are the treatment targets of renal angiomyolipoma. Treatment should be considered for symptomatic lesions or those who are at risk of complications, especially bleeding symptoms, which are linked to tumor size, angiogenic component grade, and presence of tuberous sclerosis complex (TSC). Selective arterial embolization (SAE) has become the new norm for preventive or emergency treatment of renal AMLs with minimally invasive selective targeting of small arterial feeders, we aimed to assess the efficacy and safety of selective renal arterial embolization (SAE) in the management of complicated renal angiomyolipoma and to detect the predictors of prophylactic SAE in cases of non-complicated AML. Results Bleeding symptoms were significantly more frequent in patients with TSC-associated renal AMLs (C = 0.333 and p = 0.036) and patients with intra-lesional aneurysm > 3 mm (C = 0.387 and p = 0.013). Overall success rate: thirty-three (91.7%) renal AMLs were successfully embolized with no recurrence. While three (8.3%) renal AMLs were not; one (2.8%) renal AML was not embolized due to technical failure and two (5.5%) renal AMLs showed recurrence. Primary (technical) success rate: thirty-three (86.9%) successful embolization, five (13.1%) arteriographies were done with failed embolization. The maximum diameter and volume of the lesions after SAE showed statistically significant reduction (z = 4.25 and p < 0.001). Conclusions SAE is an effective and safe technique to manage renal AMLs preoperatively or in an emergency. TSC-associated lesions, and intra-lesional aneurysms (aneurysms > 3 mm in diameter) were significantly more associated with bleeding symptoms, considering them significant predictors for prophylactic SAE in non-complicated AML.