Interobserver reliability of computed tomography angiography in the assessment of ruptured intracranial aneurysm and impact on patient management.

BACKGROUND: Aneurysmal subarachnoid hemorrhage is an emergency that can lead to a high mortality rate and many severe complications. It is critical to make a rapid radiological evaluation of ruptured intracranial aneurysms (RIAs) to determine the appropriate surgical treatment. AIM: To assess the reliability of computed tomography angiography (CTA) in assessing different features of ruptured intracranial aneurysm and its impact on patient management. METHODS: The final cohort of this study consisted of 146 patients with RIAs (75 male and 71 female) who underwent cerebral CTA. Their age ranged from 25 to 80, and the mean age ± SD was 57 ± 8.95 years. Two readers were asked to assess different features related to the aneurysm and perianeurysmal environment. Inter-observer agreement was measured using kappa statistics. Imaging data extracted from non-contrast computed tomography and CTA were considered to categorize the study population into two groups according to the recommended therapeutic approach. RESULTS: The inter-observer agreement of both reviewers was excellent for the detection of aneurysms (K = 0.95, P = 0.001), aneurysm location (K = 0.98, P = 0.001), and (K = 0.98, P = 0.001), morphology (K = 0.92, P = 0.001) and margins (K = 0.95, P = 0.001). There was an excellent interobserver agreement for the measurement of aneurysm size (K = 0.89, P = 0.001), neck (K = 0.85, P = 0.001), and dome-to-neck ratio (K = 0.98, P = 0.001). There was an excellent inter-observer agreement for the detection of other aneurysm-related features such as thrombosis (K = 0.82, P = 0.001), calcification (K = 1.0, P = 0.001), bony landmark (K = 0.89, P = 0.001) and branch incorporation (K = 0.91, P = 0.001) as well as perianeurysmal findings including vasospasm (K = 0.91, P = 0.001), perianeurysmal cyst (K = 1.0, P = 0.001) and associated vascular lesions (K = 0.83, P = 0.001). Based on imaging features, 87 patients were recommended to have endovascular treatment, while surgery was recommended in 59 patients. 71.2% of the study population underwent the recommended therapy. CONCLUSION: CTA is a reproducible promising diagnostic imaging modality for detecting and characterizing cerebral aneurysms.

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.

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.

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.

Liver Imaging Reporting and Data System Version 2018: What Radiologists Need to Know.

In this article, we aim to review Liver Imaging Reporting and Data System version 18 (LI-RADS v2018). Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy. Liver Imaging Reporting and Data System developed for standardizing interpreting, reporting, and data collection of HCC describes 5 major features for accurate HCC diagnosis and several ancillary features, some favoring HCC in particular or malignancy in general and others favoring benignity. Untreated hepatic lesions LI-RADS affords 8 unique categories based on imaging appearance on computed tomography and magnetic resonance imaging, which indicate the possibility of HCC or malignancy with or without tumor in vein. Furthermore, LI-RADS defines 4 treatment response categories for treated HCCs after different locoregional therapy. These continuous recent updates on LI-RADS improve the communication between the radiologists and the clinicians for better management and patient outcome.

Interobserver Agreement of Magnetic Resonance Imaging of Liver Imaging Reporting and Data System Version 2018.

AIM: This study aimed to assess the interobserver agreement of magnetic resonance (MR) imaging of Liver Imaging Reporting and Data System version 2018 (LI-RADS v2018). SUBJECTS AND METHODS: Retrospective analysis was done for 119 consecutive patients (77 male and 42 female) at risk of hepatocellular carcinoma who underwent dynamic contrast MR imaging. Image analysis was done by 2 independent and blinded readers for arterial phase hyperenhancement, washout appearance, enhancing capsule appearance, and size. Hepatic lesions were classified into 7 groups according to LI-RADS v2018. RESULTS: There was excellent interobserver agreement of both reviewers for LR version 4 (κ = 0.887, P = 0.001) with 90.76% agreement. There was excellent interobserver agreement for nonrim arterial phase hyperenhancement (κ = 0.948; 95% confidence interval [CI], 0.89-0.99; P = 0.001), washout appearance (κ = 0.949; 95% CI, 0.89-1.0; P = 0.001); and enhancing capsule (κ = 0.848; 95% CI, 0.73-0.97; P = 0.001) and excellent reliability of size (interclass correlation, 0.99; P = 0.001). There was excellent interobserver agreement for LR-1 (κ = 1.00, P = 0.001), LR-2 (κ = 0.94, P = 0.001), LR-5 (κ = 0.839, P = 0.001), LR-M (κ = 1.00, P = 0.001), and LR-TIV (κ = 1.00; 95% CI, 1.0-1.0; P = 0.001), and good agreement for LR-3 (κ = 0.61, P = 0.001) and LR-4 (κ = 0.61, P = 0.001). CONCLUSION: MR imaging of LI-RADS v2018 is a reliable imaging modality and reporting system that may be used for standard interpretation of hepatic focal lesions.