FLAIR vessel hyperintensities predict functional outcomes in patients with acute ischemic stroke treated with medical therapy.

OBJECTIVES: The prognostic value of fluid-attenuated inversion recovery vessel hyperintensity (FVH) remains controversial in acute ischemic stroke (AIS). The objective was to investigate whether the presence of FVH could predict long-term functional outcomes in patients with AIS receiving medical therapy. METHODS: Consecutive AIS patients with anterior circulation large vessel stenosis (LVS) in multiple centers between January 2019 and December 2020 were studied. Presence of FVH was identified and evaluated as FVH (+). Quantification of FVH was performed using an FVH-Alberta Stroke Program Early CT Score (ASPECTS) system and divided into grades: FVH-ASPECTS of 0 = grade 0; 1-2 = grade 1; 3-7 = grade 2. Poor functional outcome was defined as modified Rankin scale > 2 at 3 months. RESULTS: Overall, 175 patients were analyzed (age, 64.31 ± 13.47 years; men, 65.1%), and 78.9% patients presented with FVH. Larger infarct volume (19.90 mL vs. 5.50 mL, p < 0.001), higher rates of FVH (+) (92.0% vs. 65.9%, p < 0.001), and higher FVH grades (grade 2, 34.5% vs. 10.2%, p < 0.001) were more prone to be observed in patients with poor functional outcomes. FVH (+) with infarct volume larger than 6.265 mL (adjusted odds ratio [aOR] 6.03, 95% confidence interval [CI] 1.82-19.98) and FVH grade (grade 1, aOR 3.07, 95% CI 1.12-8.43; grade 2, aOR 5.80, 95% CI 1.59-21.11) were independently associated with poor functional outcomes. CONCLUSION: FVH (+) combined with large infarct volume and high FVH grade can predict poor long-term functional outcomes in patients with LVS who receive medical therapy. KEY POINTS: • FVH is expected to be a contrast agent-independent alternative for assessing hemodynamic status in the acute stage of stroke. • FVH (+) and high FVH grade, quantified by FVH-ASPECTS rating system and grades, are associated with large infarct volume. • The combination of FVH and DWI-based infarct volume has independent predictive value for long-term functional outcomes in AIS patients with large artery stenosis treated with medical therapy.

MRI evaluation of lateral geniculate body in normal aging brain using quantitative susceptibility mapping.

OBJECTIVE: To investigate the changes of lateral geniculate body (LGB) in the normal aging brain using quantitative susceptibility mapping (QSM) technique. METHODS: Magnetic resonance (MR) phase and magnitude images were acquired from enhanced gradient echo T2 star weighted angiography sequence with 16 echoes on 3.0T MR system using the head coil with 32 channels. Morphology Enabled Dipole Inversion (MEDI) method was applied for QSM, and the susceptibility value of LGB was measured by region of interest (ROI) drawn manually on three orthogonal planes. RESULTS: LGB of the middle-aged group had a higher susceptibility value (0.16±0.05 ppm) than that of the youth group (0.12±0.05 ppm) and elderly group (0.13±0.03 ppm) (all P<0.05). Partial correlation analysis demonstrated that there was significantly positive correlation between susceptibility value and age in the youth group (r=0.71, P<0.05). CONCLUSION: LGB could clearly be identified on QSM in the brain in vivo.

Aberrant voxel-based degree centrality in Parkinson's disease patients with mild cognitive impairment.

The purpose was to explore the intrinsic dysconnectivity pattern of whole-brain functional networks in Parkinson's disease patients with mild cognitive impairment (PD-MCI) using a voxel-wise degree centrality (DC) analysis approach. The resting-state functional magnetic resonance imaging (rs-fMRI) scanning was performed in all subjects including PD-MCI, PD patients with no cognitive impairment (PD-NCI), and healthy controls (HCs). DC mapping was used to identify functional connectivity (FC) alterations among these groups. Correlation between abnormal DC and clinical features was performed. Secondary seed-based FC analyses and voxel-based morphometry (VBM) analyses were also conducted. Compared with HCs, PD-MCI and PD-NCI showed DC abnormalities mainly in the right temporal lobe, thalamus, left cuneus, middle frontal gyrus, and corpus callosum. Compared with PD-NCI, PD-MCI showed abnormal DC in the left fusiform gyrus (FFG) and left cerebellum lobule VI, left cuneus, right hippocampus, and bilateral precuneus. In PD-MCI patients, correlation analyses revealed that DC in the left FFG was positively correlated with the Montreal Cognitive Assessment (MoCA) scores, and DC in the left precuneus was negatively correlated with the MoCA scores. Secondary seed-based FC analysis further revealed FC changes mainly in the default mode network, right middle cingulum, right supramarginal gyrus, and right postcentral/precentral gyrus. However, no significant difference was found in the secondary VBM analysis. The findings suggest that dysfunction in extensive brain areas is involved in PD-MCI. Among these regions, the left precuneus, FFG, and cerebellum VI may be the key hubs in the pathogenesis of PD-MCI.

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study.

PURPOSE: To evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters. MATERIALS AND METHODS: One hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen's kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs. RESULTS: In the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively. CONCLUSION: Through the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.