Evaluating the impact of recombinant human epidermal growth factor on scar formation in oral and maxillofacial traumatic wound healing.

Scarring following oral and maxillofacial trauma can have significant aesthetic and functional repercussions. Recombinant human epidermal growth factor (rhEGF) has emerged as a potential therapeutic agent to enhance wound healing and minimise scar formation. This retrospective study analysed data from March 2020 to June 2023 at a single institution. A total of 105 patients were divided into a control group (n = 70) receiving standard treatment and an observation group (n = 35) receiving standard treatment plus rhEGF. The primary outcomes were the incidence of scar hyperplasia and infection rates, with the secondary outcome being scar aesthetics measured by the visual analogue scale (VAS). No significant differences were found in baseline characteristics between the two groups. The observation group showed a significant reduction in scar hyperplasia (14.3% vs. 57.1%, χ2 = 20.98, p < 0.01) and infection rates (5.7% vs. 21.4%, χ2 = 4.246, p < 0.05) compared to the control group. VAS scores indicated a superior aesthetic outcome in the observation group at all post-treatment timepoints (p < 0.01). rhEGF treatment in oral and maxillofacial trauma patients resulted in favourable healing outcomes and reduced scar formation, improving aesthetic results. These findings highlight the therapeutic potential of rhEGF and underscore the need for larger-scale trials to further investigate its benefits.

Antioxidant and anti-inflammatory effects of selenomethionine promote osteogenesis via Wnt/ß-Catenin pathway.

Background: Recently, the antioxidant properties of the natural compound, selenomethionine (Se-Met), have been recognized. However, its effect on the osteogenic mineralization of the Wnt/ß-Catenin pathway under conditions of oxidative stress and inflammation remain unclear. Methods: This study utilized tert-butyl hydroperoxide (TBHP) to simulate oxidative stress and inflammation. Se-Met was then subsequently used to inhibit these effects in vitro. Results: TBHP induces oxidative stress and inflammatory responses by increasing the expression of reactive oxygen species and NLRP3, whereas decreasing the expression of GPX4, thereby inhibiting the viability of MC3T3-E1 cells. TBHP further promotes lipid peroxidation and damages the ultrastructure of mitochondria. Furthermore, TBHP inhibits the expression levels of ß-Catenin, thereby reducing the activity of the Wnt pathway, which in turn suppresses the osteogenic differentiation and mineralization capacity. Importantly, Se-Met significantly alters the aforementioned responses to enhance expression levels of Wnt pathway-related proteins and improving the osteogenic differentiation and mineralization capacity of the cells. Conclusion: Se-Met enhances antioxidant and anti-inflammatory responses in MC3T3-E1 cells via the Wnt/ß-Catenin signaling pathway to promote osteogenesis. Thus, Se-Met plays a crucial role in the field of bone homeostasis, and presents an opportunity for the future development of novel drugs for treating osteoporosis and maintaining bone stability. However, further detailed preclinical animal studies are required to generate solid and reliable data to aid this development.

Differences in distribution and features of carotid and middle cerebral artery plaque in patients with pial infarction and perforating artery infarction: A 3D vessel wall imaging study.

PURPOSE: Atherosclerotic plaques of carotid artery (CA) and middle cerebral artery (MCA) are important causes of acute ischemic stroke (AIS). This study was designed to jointly assess the plaque distribution and features of CA and MCA in AIS patients with pial infarction (PI) and perforating artery infarction (PAI), and to investigate the associations between plaque characteristics and ischemic infarction patterns. METHODS: Imaging data of sixty-five patients from a cross-sectional study were reviewed. All the patients had acute infarction in the MCA territory on diffusion weighted imaging (DWI) and underwent CA and MCA vessel wall imaging (VWI). The CA and MCA plaque presence and high-risk features on the ipsilateral side of infarction were analyzed. The brain infarction lesions were divided into PI group vs. non-PI group, and PAI group vs. non-PAI group. Different plaque distribution types and plaque features were compared in each two groups, and their associations were investigated using binary logistic regression. RESULTS: Sixty-five patients (mean age, 54.6 ± 10.1 years; 61 men) were included. The CA high-risk plaque (OR: 5.683 [1.409-22.929], P = 0.015) and MCA plaque presence (OR: 3.949 [1.397-11.162], P = 0.010) were significantly associated with PI. MCA plaques that involved the orifice of the perforating arteries were significantly associated with PAI (OR: 15.167 [1.851-124.257], P = 0.011). CONCLUSION: CA and MCA plaques show distinct distribution and high-risk features in patients with PI and PAI. Combined intracranial and extracranial arteries imaging should be considered for the evaluation of the symptomatic ischemic patients.

Volume mismatch indicates tumors in paramedial bithalamic diseases: a retrospective study.

Objective: This study aimed to investigate the diagnostic performance of volume mismatch sign on discriminating paramedial bithalamic tumors from non-tumors. Methods: In this study, we recruited patients with tumors or non-tumors of the paramedial bithalamus. We confirmed the diagnosis by pathology, laboratory tests documented in medical records, medical imaging at the baseline, or through at least 1 year of follow-up. Cases with paramedial thalamic lesions on only one side or purely midbrain illnesses were excluded. Additionally, any case without involvement of the medial thalami (i.e., those with one or both-sided anterior, lateral, or posterior thalamic lesions) was excluded. Two neuroradiologists were trained independently to evaluate volume mismatch sign on magnetic resonance T2-weighted images or T2 fluid-attenuated inversion recovery images. A positive volume mismatch sign means that the ratio of the larger-sided lesion volume to the smaller-sided lesion volume is >150%. The volume of each lesion was calculated by multiplying the anteroposterior diameter by the left-right diameter and by the height of the lesion and then dividing by 2. The kappa value was calculated to show the consistency between the two observers. The chi-square test was used to evaluate differences in volume mismatch sign between the bilthalamic midline tumor and non-tumor groups. The positive (PPV) and negative (NPV) predictive values, sensitivity, and specificity were calculated to evaluate the ability of volume mismatch sign to differentiate paramedial bilateral thalamus tumors from non-tumors. A two-tailed P ≤ 0.05 was considered to be statistically significant. The analyses were performed using the statistical software SPSS version 26. Results: A total of 96 patients were enrolled in this study between March 2012 and October 2022. A high agreement between the two observers on the volume mismatch sign of bilateral paramedian thalamic diseases was found, and the Kappa value was 0.828. A statistically significant difference was observed for the volume mismatch sign between the paramedial bithalamic tumor and the non-tumorous groups (χ2 = 35.465, P < 0.001). The presence of volume mismatch sign in paramedial bithalamic illnesses predicted the presence of tumors with a sensitivity and specificity of 69.2% and 90.9%, respectively, and PPV and NPV were 90.0% and 71.4%. Conclusion: Volume mismatch sign may indicate tumors in paramedian bithalamic diseases.

Distribution and regional variation of wall shear stress in the curved middle cerebral artery using four-dimensional flow magnetic resonance imaging.

Background: To investigate the distribution and regional variation of wall shear stress (WSS) in the curved middle cerebral artery (MCA) in healthy individuals using four-dimensional (4D) flow magnetic resonance imaging (MRI). Methods: A total of 44 healthy participants (18 males; mean ages: 27.16±5.69 years) were included in this cross-sectional study. The WSS parameters of mean, minimum, and maximum values, the coefficient of variation of time-averaged WSS (TAWSSCV), and the maximum values of the oscillatory shear index (OSI) were calculated and compared in the curved proximal (M1) segments. Three cross-sectional planes were selected: the location perpendicular to the beginning of the long axis of the curved M1 segment of the MCA (proximal section), the most curved M1 location (curved M1 section), and the location before the insular (M2) segment bifurcation (distal section). The WSS and OSI parameters of the proximal, curved, and distal sections of the curved M1 segment were compared, including the inner and outer curvatures of the curved M1 section. Results: Of the curved M1 segments, the curved M1 section had significantly lower minimum TAWSS values than the proximal (P=0.031) and distal sections (P=0.002), and the curved M1 section had significantly higher maximum OSI values than the distal section (P=0.001). The TAWSSCV values at the curved M1 section were significantly higher than the proximal (P=0.001) and distal sections (P<0.001). At the curved M1 section, the inner curvature showed a significantly lower minimum TAWSS (P=0.013) and higher maximum OSI values (P=0.002) than the outer curvature. Conclusions: There are distribution variation of WSS and OSI parameters at the curved M1 section of the curved MCA, and the inner curvature of the curved M1 section has the lowest WSS and highest OSI distribution. The local hemodynamic features of the curved MCA may be related to the predilection for atherosclerotic plaque development.

Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.

Importance: Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. Objective: To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. Design, Setting, and Participants: This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. Main Outcomes and Measures: Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. Results: A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%-64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. Conclusions and Relevance: These findings suggest that deep learning system-based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.

Cerebral Microbleed Automatic Detection System Based on the "Deep Learning".

Objective: To validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs). Method: This is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI. The patients were divided into training and validation cohorts of 1,285 and 330 patients, respectively, and another 30 patients were used for internal testing. The model training and validation data were labeled layer by layer and rechecked by two neuroradiologists with 15 years of work experience. Afterward, a three-dimensional convolutional neural network (CNN) was applied to the MRI data from the training and validation cohorts to construct a deep learning system (DLS) that was tested with the 72 patients, independent of the aforementioned MRI cohort. The DLS tool was used as a segmentation program for these 72 patients. These results were evaluated and revised by five neuroradiologists and subjected to an output analysis divided into the missed label, incorrect label, and correct label. The interneuroradiologists DLS agreement rate, which was assessed using the interrater agreement kappas test, was used for the quality analysis. Results: In the detection and segmentation of the CMBs, the DLS achieved a Dice coefficient of 0.72. In the evaluation of the independent clinical data, the neuroradiologists reported that more than 90% of the lesions were directly detected and less than 10% of lesions were incorrectly labeled or the label was missed by our DLS. The kappa value for interneuroradiologist DLS agreement reached 0.79 on average. Conclusion: Based on the results, the automatic detection and segmentation of CMBs are feasible. The proposed well-trained DLS system might represent a trusted tool for the segmentation and detection of CMB lesions.

Changes of prominent vessel sign and susceptibility vessel sign in acute ischemic stroke patients with and without successful recanalization: a study based on susceptibility weighted images.

OBJECTIVES: To investigate the changes of prominent vessel sign (PVS) and susceptibility vessel sign (SVS) in acute ischemic stroke (AIS) patients with successful and non-successful vascular recanalization treatment, and to study the associations between the susceptibility-weighted imaging (SWI) findings and early clinical and imaging prognosis. METHODS: Thirty-five patients with the acute MCA territory infarction were retrospectively included and classified into Groupre (n = 25) and Groupnon (n = 10) according to the success of vascular recanalization. NIHSS was used for clinical assessment. PVS and DWI were analyzed using ASPECT scores. Baseline, post-treatment, the changes of PVS and SVS, and SVS length were recorded and compared between two groups. Correlation analysis was performed between SWI factors and early post-treatment clinical and imaging factors. RESULTS: The mean PVSpre value was 5.20 ± 1.76 and PVSpost was 8.20 ± 1.86. PVSpre and PVSpost showed statistical significance between Groupre and Groupnon (both p < 0.001). A significant difference wasfound in SVSpost(+) (p < 0.001), SVSpost-pre(+) (p = 0.001), SVSpost-pre length (p = 0.036) and SVSpost length (p = 0.001) between the two groups.  A positive correlation was found between PVSpre and DWIpost (p < 0.001, r = 0.564). There were positive correlations between PVS-DWI mismatch and NIHSSpost in Groupre (p = 0.042, r = 0.410) and in Groupnon (p = 0.006, r = 0.789). CONCLUSIONS: PVS and SVS changes are influenced by the success of vascular recanalization. However, the changes are unrelated to either early clinical or imaging outcomes in AIS patients. PVS-DWI mismatch can be taken as an imaging biomarker for early clinical outcomes, both for patients with or without successful vascular recanalization.

The Reliability of Measuring Early Brain Edema with Computed Tomography.

BACKGROUND: Postoperative brain edema is a common complication in patients with high-grade glioma after craniotomy. Both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are applied to diagnose brain edema. Usually, MRI is considered to be better than CT for identifying brain edema. However, MRI is not generally applied in diagnosing acute cerebral edema in the early postoperative stage. Whether CT is reliable in detecting postoperative brain edema in the early stage is unknown. OBJECTIVE: This study aimed at investigating the agreement and correlation between CT and MRI for measuring early postoperative brain edema. METHODS: Patients with high-grade glioma who underwent craniotomy in the Beijing Tiantan hospital from January 2017 to October 2018 were retrospectively analyzed. The region of interest and operative cavity were manually outlined, and the volume of postoperative brain edema was measured on CT and MRI. Pearson correlation testing and the Intraclass Correlation Coefficient (ICC) were used to evaluate the association and agreement between CT and MRI for detecting the volume of postoperative brain edema. RESULTS: Twenty patients were included in this study. The interrater agreement was perfect for detecting brain edema (CT: κ=1, ICC=0.977, P<0.001; MRI: κ=0.866, ICC=0.963, P<0.001). A significant positive correlation and excellent consistency between CT and MRI were found for measuring the volume of brain edema (rater 1: r=0.97, ICC=0.934, P<0.001; rater 2: r=0.97, ICC=0.957, P<0.001). CONCLUSION: Substantial comparability between CT and MRI is demonstrated for detecting postoperative brain edema. It is reliable to use CT for measuring brain edema volume in the early stage after surgery.

Utility of Dual-Layer Spectral Detector CTA to Characterize Carotid Atherosclerotic Plaque Components: An Imaging-Histopathology Comparison in Patients Undergoing Endarterectomy.

BACKGROUND. The composition of noncalcified portions of carotid atherosclerotic plaque is an important marker of plaque vulnerability and ischemia risk. OBJECTIVE. The purpose of this study was to assess the utility of dual-layer spectral detector CTA (DLCTA) parameters for characterization of carotid plaque components with histologic results from carotid endarterectomy as the reference. METHODS. Seven patients (five men, two women; mean age, 61.6 ± 8.5 [SD] years) with carotid plaque awaiting carotid endarterectomy were prospectively enrolled and underwent preoperative supraaortic DLCTA. A neuroradiologist and pathologist performed joint slice-by-slice review of histologic slices of resected plaques and CTA images. With the use of anatomic landmarks, ROIs were placed on noncalcified components (lipid-rich necrotic core [LRNC], intraplaque hemorrhage [IPH], fibrous tissue, loose matrix) on CTA images and compared with corresponding histologic slices. For each ROI, attenuation was recorded for conventional polyenergetic images (CTPI) and virtual monoenergetic images with energy ranging from 40 to 140 keV (CT40-140keV), attenuation spectrum curve slope was calculated, and Z-effective value (representing effective atomic number) was recorded. DLCTA parameters were compared among plaque components. RESULTS. Seven plaques with a total of 65 slices and 364 ROIs (159 fibrous tissue, 96 LRNC, 86 loose matrix, 23 IPH) were analyzed. All parameters (CTPI, CT40-140keV, slope from 40 to 140 keV, Z-effective value) had significant differences between LRNC and the other components (all p < .001). For example, mean CTPI was 37.1 ± 15.1 HU for LRNC, 58.4 ± 21.6 HU for IPH, 69.7 ± 20.5 HU for fibrous tissue, and 69.6 ± 19.6 HU for loose matrix. Mean CT40keV was 28.1 ± 36.7 HU for LRNC, 87.5 ± 48.9 HU for IPH, 106.3 ± 47.5 HU for fibrous tissue, and 102.6 ± 48.0 HU for loose matrix. AUC for differentiating LRNC from other components was highest (0.945) for CT40kev and decreased with higher energy; AUC for CTPI was 0.908. CT40kev also had highest accuracy (90.4%); at a cutoff of 55.7 HU, CT40kev had 88.5% sensitivity and 91.0% specificity. For differentiating IPH from fibrous tissue and loose matrix, AUC was highest at 0.652 for CTPI and 0.645 for CT40kev. CONCLUSION. DLCTA showed strong performance in differentiating LRNC from other noncalcified plaque components; CT40kev had highest accuracy, outperforming CTPI. CLINICAL IMPACT. DLCTA parameters may help characterize carotid plaque composition as a marker of vulnerable plaque and ischemia risk.

Segmentation of Cerebral Small Vessel Diseases-White Matter Hyperintensities Based on a Deep Learning System.

Objective: Reliable quantification of white matter hyperintensities (WHMs) resulting from cerebral small vessel diseases (CSVD) is essential for understanding their clinical impact. We aim to develop and clinically validate a deep learning system for automatic segmentation of CSVD-WMH from fluid-attenuated inversion recovery (FLAIR) imaging using large multicenter data. Method: A FLAIR imaging dataset of 1,156 patients diagnosed with CSVD associated WMH (median age, 54 years; 653 males) obtained between September 2018 and September 2019 from Beijing Tiantan Hospital was retrospectively analyzed in this study. Locations of CSVD-WMH on the FLAIR scans were manually marked by two experienced neurologists. Using the manually labeled data of 996 patients (development set), a U-shaped novel 2D convolutional neural network (CNN) architecture was trained for automatic segmentation of CSVD-WMH. The segmentation performance of the network was evaluated with per pixel and lesion level dice scores using an independent internal test set (n = 160) and a multi-center external test set (n = 90, three medical centers). The clinical suitability of the segmentation results, classified as acceptable, acceptable with minor revision, acceptable with major revision, and not acceptable, was analyzed by three independent neuroradiologists. The inter-neuroradiologists agreement rate was assessed by the Kendall-W test. Results: On the internal and external test sets, the proposed CNN architecture achieved per pixel and lesion level dice scores of 0.72 (external test set), and they were significantly better than the state-of-the-art deep learning architectures proposed for WMH segmentation. In the clinical evaluation, neuroradiologists observed the segmentation results for 95% of the patients were acceptable or acceptable with a minor revision. Conclusions: A deep learning system can be used for automated, objective, and clinically meaningful segmentation of CSVD-WMH with high accuracy.

Aberrant multimodal brain networks in patients with anti-NMDA receptor encephalitis.

AIMS: To explore large-scale brain network alterations and examine their clinical and neuropsychological relevance in patients with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. METHODS: Twenty-four patients with anti-NMDAR encephalitis and 26 matched healthy controls (HCs) were enrolled in our study. Based on the multimodal MRI dataset, individual morphological, structural, and functional brain networks were constructed and compared between the two groups at multiple levels. The associations with clinical/neuropsychological variables and the discriminant ability of significant alterations were further studied. RESULTS: Multimodal network analysis revealed that anti-NMDAR encephalitis mainly affected morphological and structural networks, but subtle alterations were observed in functional networks. Intriguingly, decreased network local efficiency was observed for both morphological and structural networks and increased nodal centrality in the lateral orbital gyrus was convergently observed among the three types of networks in the patients. Moreover, the alterations, particularly those from structural networks, accounted largely for cognitive deficits of the patients and could distinguish the diseased individuals from the HCs with excellent performance (area under the curve =0.933). CONCLUSIONS: The current study provides a comprehensive view of characteristic multimodal network dysfunction in anti-NMDAR encephalitis, which is crucial to establish new diagnostic biomarkers and promising therapeutic targets for the disease.

High-resolution vessel wall magnetic resonance imaging for depicting imaging features of unruptured intracranial vertebrobasilar dissecting aneurysms.

OBJECTIVE: To demonstrate the application value of high-resolution vessel wall magnetic resonance imaging (HR-VW-MRI) for depicting the imaging features of unruptured intracranial vertebrobasilar dissecting aneurysms (VBDAs). METHODS: HR-VW-MRI data of 49 patients with suspected unruptured VBDAs were retrospectively analyzed. The presence of intramural hematomas (IMH), double lumens, intimal flaps, and outer diameter enlargements were recorded. Specificity and sensitivity were calculated for both two-dimensional (2D) and three-dimensional (3D) sequences. Additionally, IMH volumes were measured and posterior inferior cerebellar artery (PICA) involvement was analyzed. RESULTS: Thirty-five VBDAs were confirmed in 34 patients. The overall sensitivity and specificity were 0.889 (95% confidence interval [CI]: 0.730-0.964) and 0.769 (95% CI: 0.460-0.938) for 2D sequences, and 0.917 (95% CI: 0.764-0.978) and 0.846 (95% CI: 0.537-0.973) for 3D sequences, respectively. Intimal flaps were detected in 57.1%, 87.5%, and 71.4% of all cases on 2D pre-contrast T1-weighted, contrast-enhanced T1-weighted, and 3D T1-weighted black-blood (BB) images, respectively. There was no significant difference in IMH volume between 3D T1-weighted BB and magnetization-prepared rapid gradient-echo sequences. PICA involvement was best visualized using 3D T1 sequences. CONCLUSION: 3D T1-weighted BB MRI provided good visualization of VBDA features, with large coverage, and was useful for detecting dissection flaps.

Are prominent medullary veins better than prominent cortical veins as predictors of early clinical outcome in patients with acute ischemic stroke?

PURPOSE: The prominent vessel sign (PVS) on susceptibility-weighted imaging (SWI) can be dichotomized into prominent cortical veins (PCV) and prominent medullary veins (PMV). This study was designed to compare the predictive value of PCV and PMV in the evaluation of the severity of acute ischemic stroke (AIS) in patients within the reperfusion window. METHODS: Forty-seven consecutive patients with AIS within the middle cerebral artery territory were recruited. Magnetic resonance imaging was performed within 8 hours of symptom onset and at 7 days after stroke onset. Infarct volume was measured, and the early clinical outcome at 7 days was assessed using the modified Rankin Scale. PVS was dichotomized into cases with both PCV and PMV and cases with only PCV according to location. RESULTS: Patients with both PCV and PMV (n=32) had higher admission National Institutes of Health Stroke Scale scores (p = 0.020), larger infarct volumes at baseline (p = 0.026) and 7 days (p = 0.007), and larger infarct growth at 7 days (p = 0.050) than those with PCV only. Multivariate regression analysis showed that both the time of onset at baseline (p = 0.013) and infarct growth at 7 days (p = 0.014) could independently predict poor early clinical outcome. CONCLUSION: PMV may predict poor early clinical outcome in AIS patients, and reperfusion therapy may, therefore, be required more urgently in patients with PMV.

Comparison of three FLAIR vascular hyperintensities methodologies in patients with acute ischemic stroke.

BACKGROUND: Multiple methods have been used to analyze fluid-attenuated inversion recovery (FLAIR) vascular hyperintensities (FVHs) which may represent collaterals in patients with acute ischemic stroke (AIS); however, there is no consensus between methods. PURPOSE: To compare three frequently used FVH methods for predicting early infarct volume and clinical outcome in patients with AIS. MATERIAL AND METHODS: Patients with AIS in middle cerebral artery territory were recruited. FVHs were evaluated using extensive FVHs, FVH-diffusion-weighted imaging (DWI) mismatch, and FVH-in/out-DWI. Infarct volume at baseline and day 7 were measured. Early neurological improvement (ENI) was assessed. Good outcomes were defined by modified Rankin Scale scores of 0-2 at 90 days. RESULTS: Fifty-one patients were included. ENI was 55.6% in patients with extensive FVHs and 23.3% in those without (P = 0.024). Patients with extensive FVHs had smaller infarct volume growth at seven days than those without (P = 0.041). ENI was 48.3% in patients with FVH-DWI mismatch and 15.8% in those without (P = 0.021). Patients with FVH-DWI mismatch had smaller infarct volumes at seven days than those without (P = 0.038). Patients with FVH-out-DWI had smaller baseline infarct volumes, smaller seven-day volumes, and smaller infarct growth than those with FVH-in-DWI (P<0.001, P<0.001, and P = 0.031, respectively). In multivariate logistic regression analysis, the infarct growth at seven days negatively independently predicted ENI (OR = 0.737, 95% CI 0.593-0.915, P = 0.006). However, none of the FVH classifications could predict a good 90-day outcome. CONCLUSION: Patients with extensive FVHs or FVH-DWI mismatch tend to have early favorable clinical outcome. FVH-out-DWI being associated with smaller infarct growth may also indicate early favorable clinical outcome.

Differences in atheroma between Caucasian and Asian subjects with anterior stroke: A vessel wall MRI study.

BACKGROUND AND PURPOSE: While extracranial carotid artery stenosis is more common among Caucasians and intracranial artery stenosis is more common among Asians, the differences in atherosclerotic plaque characteristics have not yet been extensively examined. We sought to investigate plaque location and characteristics within extracranial carotid and intracranial arteries in symptomatic Caucasians and Chinese using vessel wall MRI. METHODS: Subjects with recent anterior circulation ischaemic stroke were recruited and imaged at two sites in the USA and China using similar protocols. Both extracranial carotid and intracranial arteries were reviewed to determine plaque location and characteristics. RESULTS: The prevalence of extracranial carotid plaque in Caucasians and Chinese was 73.1% and 49.1%, respectively (p=0.055). Prevalence of intracranial plaque was 38.5% and 69.1% in Caucasians and Chinese, respectively (p=0.02). Furthermore, 42% of Caucasians and 16% of Chinese had high-risk plaque (HRP) features (intraplaque haemorrhage, luminal surface disruption) in the extracranial carotid artery (p=0.03). The prevalence of HRP features in intracranial arteries was not significantly different between the two cohorts (4% vs 11%; p=0.42). CONCLUSIONS: Differences in the location and characteristics of cerebrovascular atherosclerosis were identified by vessel wall MRI in US Caucasian and Chinese subjects with recent anterior circulation ischaemic stroke. Extracranial carotid plaques with HRP features were more common in Caucasians. Intracranial plaques were more common in Chinese subjects, but no significant difference between the two cohorts in intracranial HRP prevalence was found. Larger studies using vessel wall imaging to investigate racial differences in cerebrovascular disease may inform underlying mechanisms of HRP development and may ultimately help guide appropriate therapy.

Plaque burden assessment and attenuation measurement of carotid atherosclerotic plaque using virtual monoenergetic images in comparison to conventional polyenergetic images from dual-layer spectral detector CT.

PURPOSE: To compare virtual monoenergetic images (VMIs) with conventional polyenergetic images (PI) of Dual-layer spectral detector CT angiography (DLCTA) in plaque burden assessment and attenuation measurement of carotid atherosclerotic plaques. METHODS: Supra-aortic DLCTA imaging of thirty patients (8 female, mean ages 63.1 ±â€¯7.5 years) were respectively reviewed. Lumen area, wall area, and calcified area of plaques were outlined and recorded. Normalized wall index (NWI) was calculated for plaque burden and compared between PI and different VMIs. The attenuation of the non-calcified, calcified area of the plaques, sternocleidomastoid muscle (SCM), as well as Z effective values were measured and compared. RESULTS: Fifty carotid plaques (27 left, 23 right) of thirty patients were analyzed. The average values of lumen, wall, calcified areas and NWI on PI were 34.50 ±â€¯20.57mm2, 47.61 ±â€¯19.94 mm2, 5.25 mm2 (1.35- 51.86 mm2), and 0.59 ±â€¯0.16 respectively. No significant difference was found in the lumen area (p = 0.314), wall area (p = 0.600), and NWI (p = 0.980) between different VMIs and PI. A significant difference was found in the calcified area between VMIs and PI (p = 0.009). Attenuations of non-calcified and calcified components in carotid plaques were comparable to PI for 50-120 keV (all: p > 0.05) and 60-120 keVs (all p > 0.05), respectively. Z Effective values for non-calcified, calcified and SCM were 7.67 ±â€¯0.42, 11.70 ±â€¯1.22, and 7.45 ±â€¯0.12, respectively. CONCLUSIONS: Carotid plaque burden assessment was comparable between PI and VMIs at 40-120 keVs. Attenuations of non-calcified components in carotid plaques were comparable to PI for 50-120 keV VMIs of DLCTA. VMIs might provide more information on carotid plaque features.

Accuracy of imaging markers on noncontrast computed tomography in predicting intracerebral hemorrhage expansion.

Objectives Hematoma expansion (HE) is an important factor of unfavorable outcome in patients with intracerebral hemorrhage (ICH). Imaging markers on noncontrast computed tomography (NCCT) provide increasing value in the prediction of HE due to fast and easy-to-use advantages; however, the accuracy of NCCT-based prediction of intracerebral HE remains unclear. We aimed to investigate the predictive accuracy of NCCT markers for the evaluation of HE using a well-characterized ICH cohort. Methods We retrospectively analyzed 414 patients with spontaneous ICH, who underwent baseline CT within 6 h after symptom onset and follow-up CT within 24 h after ICH. Hematoma volumes were measured on baseline and follow-up CT images, and imaging features that predicted HE were analyzed. The test characteristics for the NCCT predictors were calculated. Results Of the 414 patients investigated, 63 presented blend sign, 45 showed black hole sign, 36 had island sign and 34 had swirl sign. In the 414 patients, 88 presented HE, the incidence was 21.26%. Of the 88 patients with HE, 22 presented blend sign, 11 showed black hole sign, 8 had swirl sign and 7 had island sign. The blend sign showed highest sensitivity (25.00%) and swirl sign showed the highest specificity (92.02%) among the four predictors. We noted excellent interobserver agreement for the identification of HE. Conclusion The four NCCT markers can predict HE with limited sensitivity, high specificity and good accuracy. This may be useful for prompt identification of patients at high risk of active bleeding, and prevention of over-treatment associated with HE. Abbreviations HE, hematoma expansion; ICH, intracerebral hemorrhage; NCCT, noncontrast computed tomography.

Association between ADAMTS7 polymorphism and carotid artery plaque vulnerability.

Recent genome-wide association studies (GWAS) indicated that polymorphisms in ADAMTS7 were associated with artery disease caused by atherosclerosis. However, the correlation between the ADAMTS7 polymorphism and plaque stability remains unclear. The objective of this study was to evaluate the association between 2 ADAMTS7 variants rs3825807 and rs7173743 and ischemic stroke or atherosclerotic plaque vulnerability.This research is an observational study. Patients with ischemic stroke and normal control individuals admitted to Beijing Tiantan Hospital from May 2014 to October 2017 were enrolled. High-resolution magnetic resonance imaging was used to distinguish vulnerable and stable carotid plaques. The ADAMTS7 SNPs were genotyped using TaqMan assays on real-time PCR system. The multivariate logistic regression analyses were used to adjust for multiple risk factors between groups.Three hundred twenty-six patients with ischemic stroke (189 patients with vulnerable plaque and 81 patients with stable plaque) and 432 normal controls were included. ADAMTS7 polymorphisms of both rs7173743 and rs3825807 were associated with carotid plaque vulnerability but not the prevalence of ischemic stroke. The T/T genotype of rs7173743 [odds ratio (OR) = 1.885, 95% confidence interval (CI) = 1.067-3.328, P = .028] and A/A genotype of rs3825807 (OR = 2.146, 95% CI = 1.163-3.961, P = .013) were considered as risk genotypes for vulnerable plaque susceptibility.In conclusion, ADAMTS7 variants rs3825807 and rs7173743 are associated with the risk for carotid plaque vulnerability.