Neuroradiology最新文献

Background: Tuberculomas are prevalent in developing countries and demonstrate variable signals on MRI resulting in the overlap of the conventional imaging phenotype with other entities including glioma and brain metastasis. An accurate MRI diagnosis is important for the early institution of anti-tubercular therapy, decreased patient morbidity, mortality, and prevents unnecessary neurosurgical excision. This study aims to assess the potential of radiomics features of regular contrast images including T1W, T2W, T2W FLAIR, T1W post contrast images, and ADC maps, to differentiate between tuberculomas, high-grade-gliomas and metastasis, the commonest intra parenchymal mass lesions encountered in the clinical practice.

Methods: This retrospective study includes 185 subjects. Images were resampled, co-registered, skull-stripped, and zscore-normalized. Automated lesion segmentation was performed followed by radiomics feature extraction, train-test split, and features reduction. All machine learning algorithms that natively support multiclass classification were trained and assessed on features extracted from individual modalities as well as combined modalities. Model explainability of the best performing model was calculated using the summary plot obtained by SHAP values.

Results: Extra tree classifier trained on the features from ADC maps was the best classifier for the discrimination of tuberculoma from high-grade-glioma and metastasis with AUC-score of 0.96, accuracy-score of 0.923, Brier-score of 0.23.

Conclusion: This study demonstrates that radiomics features are effective in discriminating between tuberculoma, metastasis, and high-grade-glioma with notable accuracy and AUC scores. Features extracted from the ADC maps surfaced as the most robust predictors of the target variable.

Purpose: Identification of the Adamkiewicz artery before aortic surgery is important for preventing postoperative complications due to spinal cord ischemia. The Adamkiewicz artery is difficult to identify due to its small diameter. Nitroglycerin has a vasodilatory effect and is used clinically to improve visualization of blood vessels on coronary computed tomography (CT) angiography. We investigated whether the vasodilatory effect of nitroglycerin could improve the ability to visualize the Adamkiewicz artery.

Methods: We extracted 33 cases wherein contrast-enhanced CT images were taken before and after aortic aneurysm surgery. Nitroglycerin was administered for coronary artery evaluation on the preoperative CT. However, no nitroglycerin was administered before the postoperative CT. Aortic contrast-to-noise ratio, CT value, image noise, and diameter of the Adamkiewicz artery and anterior spinal artery were measured. The depiction of the Adamkiewicz artery was graded into four grades and evaluated. These measurements were performed by two independent reviewers.

Results: In nitroglycerin-administered cases, the contrast-to-noise ratio and CT values were significantly higher (P < 0.001, P < 0.001, respectively); the Adamkiewicz artery and anterior spinal artery diameters were dilated (P = 0.005, P = 0.001, respectively). The Adamkiewicz artery score also improved significantly (P < 0.001). No significant difference was found in image noise.

Conclusion: Nitroglycerin contributed to improving the Adamkiewicz artery's visualization.

Purpose: In brain development, Myelination is the characteristic feature of white matter maturation, which plays an important role in efficient information transmitting. The white matter abnormality has been reported to be associated with self-limited epilepsy with centrotemporal spikes (SeLECTS). This study aimed to detect the altered white matter region in the SeLECTS patients by the combination of diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM) technique.

Methods: 27 children with SeLECTS and 23 age- and gender-matched healthy children were enrolled. All participants were scanned with 3.0-T MRI to acquire the structure, diffusion and susceptibility-weighted data. The susceptibility and diffusion weighted data were processed to obtain quantitative susceptibility map and fraction anisotropy (FA) map. Then voxel-wise tract-based spatial statistics (TBSS) were used to analyze quantitative susceptibility and FA data.

Results: Both DTI and QSM revealed extensive white matter alterations in the frontal, parietal, and temporal lobes in SeLECTS patients. The overlapped region of DTI and QSM analyses was located in the fiber tracts of the corona radiata. The FA values in this overlapped region were negatively correlated with the magnetic susceptibility values.

Conclusion: Our results suggest that TBSS-based QSM can be employed as a novel approach for characterizing alterations in white matter in SeLECTS. And the combination of QSM and DTI can provide a more comprehensive evaluation of white matter integrity by utilizing different biophysical features.

Background and purpose: Neuronal intranuclear inclusion disease (NIID) is a rare complex neurodegenerative disorder presents with various radiological features. The study aimed to investigate the structural abnormalities in NIID using multi-shell diffusion MR.

Materials and methods: Twenty-eight patients with adult-onset NIID and 32 healthy controls were included. Volumetric and diffusion MRI measures, including volume, fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF) of six brain structures, including cortex, subcortical GM, cerebral WM, cerebellar GM and WM, and brainstem, were obtained and compared between NIID and healthy controls. Associations between MRI measures and clinical variables were investigated.

Results: Brain lesions of NIID included corticomedullary junction lesions on DWI, confluent leukoencephalopathy, lesions on callosum, cerebellar middle peduncle, cerebellar paravermal area and brainstem, and brain atrophy. Compared to healthy controls, NIID showed extensive volume loss of all the six brain regions (all p < 0.001); lower FA in cerebral WM (p < 0.001); higher MD in all WM regions; lower ODI in cortex (p < 0.001); higher ODI in subcortical GM (p < 0.001) and brainstem (p = 0.016); lower ICVF in brainstem (p = 0.001), and cerebral WM (p < 0.001); higher ISOVF in all the brain regions (p < 0.001). Higher MD of cerebellar WM was associated with worse cognitive level as evaluated by MoCA scores (p = 0.011).

Conclusions: NIID patients demonstrated widespread brain atrophy but heterogeneous diffusion alterations. Cerebellar WM integrity impairment was correlated with the cognitive decline. The findings of the current study offer a sophisticated picture of brain structural alterations in NIID.

Purpose: Meningioma is the most common intracranial tumor, graded on pathology using WHO criteria to predict tumor course and treatment. However, pathological grading via biopsy may not be possible in cases with poor surgical access due to tumor location. Therefore, our systematic review aims to evaluate whether diagnostic imaging features can differentiate high grade (HG) from low grade (LG) meningiomas as an alternative to pathological grading.

Methods: Three databases were searched for primary studies that either use routine magnetic resonance imaging (MRI) or computed tomography (CT) to assess pathologically WHO-graded meningiomas. Two investigators independently screened and extracted data from included studies.

Results: 24 studies met our inclusion criteria with 12 significant (p < 0.05) CT and MRI features identified for differentiating HG from LG meningiomas. Cystic changes in the tumor had the highest specificity (93.4%) and irregular tumor-brain interface had the highest positive predictive value (65.0%). Mass effect had the highest sensitivity (81.0%) and negative predictive value (90.7%) of all imaging features. Imaging feature with the highest accuracy for identifying HG disease was irregular tumor-brain interface (79.7%). Irregular tumor-brain interface and heterogenous tumor enhancement had the highest AUC values of 0.788 and 0.703, respectively.

Conclusion: Our systematic review highlight imaging features that can help differentiate HG from LG meningiomas.

We compared different LLMs, notably chatGPT, GPT4, and Google Bard and we tested whether their performance differs in subspeciality domains, in executing examinations from four different courses of the European Society of Neuroradiology (ESNR) notably anatomy/embryology, neuro-oncology, head and neck and pediatrics. Written exams of ESNR were used as input data, related to anatomy/embryology (30 questions), neuro-oncology (50 questions), head and neck (50 questions), and pediatrics (50 questions). All exams together, and each exam separately were introduced to the three LLMs: chatGPT 3.5, GPT4, and Google Bard. Statistical analyses included a group-wise Friedman test followed by a pair-wise Wilcoxon test with multiple comparison corrections. Overall, there was a significant difference between the 3 LLMs (p < 0.0001), with GPT4 having the highest accuracy (70%), followed by chatGPT 3.5 (54%) and Google Bard (36%). The pair-wise comparison showed significant differences between chatGPT vs GPT 4 (p < 0.0001), chatGPT vs Bard (p < 0. 0023), and GPT4 vs Bard (p < 0.0001). Analyses per subspecialty showed the highest difference between the best LLM (GPT4, 70%) versus the worst LLM (Google Bard, 24%) in the head and neck exam, while the difference was least pronounced in neuro-oncology (GPT4, 62% vs Google Bard, 48%). We observed significant differences in the performance of the three different LLMs in the running of official exams organized by ESNR. Overall GPT 4 performed best, and Google Bard performed worst. This difference varied depending on subspeciality and was most pronounced in head and neck subspeciality.

Purpose: Alagille syndrome (ALGS) is a multisystem autosomal dominant disorder with highly variable expression. Intracranial arterial and venous anomalies have a reported prevalence of 30-40% and can increase the risk of stroke by 16%. Few reports document the frequency and evolution of cerebrovascular abnormalities (CVAs) in children with ALGS. We aimed to define the spectrum, frequency, and evolution of CVAs in a series of children with ALGS using magnetic resonance angiography (MRA).

Methods: We conducted a single-center, retrospective study in a large tertiary pediatric hospital. CVAs were grouped into 4 categories: 1) Stenosis or narrowing; 2) Aneurysms and ectasias; 3) Tortuosity; and 4) Vascular anomalies and anatomical variants.

Results: Thirty-two children met the inclusion criteria. The median age at initial diagnosis was 6 (3.8-10.3) years. Thirteen (40%) had follow-up MRI at a mean of 55 (31.5-66) months. Eighteen (56%) had CVAs; the most frequent fell into group 1 (n = 12, 37.5%). CVAs were stable over time, except for one patient with Moyamoya arteriopathy (MMA). One patient developed a transient ischemic attack secondary to an embolic event. Three (9.3%) had microhemorrhages at the initial diagnosis secondary to Tetralogy of Fallot. Another patient had recurrent subdural hematomas of unknown cause.

Conclusion: CVAs were stable except in the presence of MMA. Vascular strokes, which are reported in older patients with ALGS, were not a common feature in children under 16 years of age, either at presentation or over the 31.5-66 month follow-up period.

Introduction: We conducted a multilayer network analysis in patients with juvenile myoclonic epilepsy (JME) and healthy controls, to investigate the gray matter layer using a morphometric similarity network and analyze the white matter layer using structural connectivity.

Methods: We enrolled 42 patients with newly diagnosed JME and 53 healthy controls. Brain magnetic resonance imaging (MRI) using a three-tesla MRI scanner, including T1-weighted imaging and diffusion tensor imaging (DTI) were performed. We created a gray matter layer matrix with a morphometric similarity network using T1-weighted imaging, and a white matter layer matrix with structural connectivity using the DTI. Subsequently, we performed a multilayer network analysis by applying graph theory.

Results: There were significant differences in network at the global level in the multilayer network analysis between the groups. The average multiplex participation of patients with JME was lower than that of healthy controls (0.858 vs. 0.878, p = 0.007). In addition, several regions showed significant differences in multiplex participation at the nodal level in the multilayer network analysis. Multiplex participation in the right entorhinal cortex was lower, whereas multiplex participation in the right supramarginal gyrus was higher at the nodal level in the multilayer network analysis of patients with JME compared to healthy controls.

Conclusion: We demonstrated differences in network at the global and nodal levels in the multilayer network analysis between patients with JME and healthy controls. These features may be associated with the pathophysiology of JME and could help us understand the complex brain network in patients with JME.

Purpose: The presurgical discrimination of IDH-mutant astrocytoma grade 4 from IDH-wildtype glioblastoma is crucial for patient management, especially in younger adults, aiding in prognostic assessment, guiding molecular diagnostics and surgical planning, and identifying candidates for IDH-targeted trials. Despite its potential, the full capabilities of DSC-PWI remain underexplored. This research evaluates the differentiation ability of relative-cerebral-blood-volume (rCBV) percentile values for the enhancing and non-enhancing tumor regions compared to the more commonly used mean or maximum preselected rCBV values.

Methods: This retrospective study, spanning 2016-2023, included patients under 55 years (age threshold based on World Health Organization recommendations) with grade 4 astrocytic tumors and known IDH status, who underwent presurgical MR with DSC-PWI. Enhancing and non-enhancing regions were 3D-segmented to calculate voxel-level rCBV, deriving mean, maximum, and percentile values. Statistical analyses were conducted using the Mann-Whitney U test and AUC-ROC.

Results: The cohort consisted of 59 patients (mean age 46; 34 male): 11 astrocytoma-4 and 48 glioblastoma. While glioblastoma showed higher rCBV in enhancing regions, the differences were not significant. However, non-enhancing astrocytoma-4 regions displayed notably higher rCBV, particularly in lower percentiles. The 30th rCBV percentile for non-enhancing regions was 0.705 in astrocytoma-4, compared to 0.458 in glioblastoma (p = 0.001, AUC-ROC = 0.811), outperforming standard mean and maximum values.

Conclusion: Employing an automated percentile-based approach for rCBV selection enhances differentiation capabilities, with non-enhancing regions providing more insightful data. Elevated rCBV in lower percentiles of non-enhancing astrocytoma-4 is the most distinguishable characteristic and may indicate lowly vascularized infiltrated edema, contrasting with glioblastoma's pure edema.