Edge-Type Hyperintense Intracranial Artery Plaque: A Potential MRI Biomarker of Stroke Recurrence.

BACKGROUND: Identifying patients at high risk of stroke recurrence is important for stroke prevention and treatment. PURPOSE: To explore the characteristics of T1 hyperintense plaques (HIP) and their relationship with stroke recurrence in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-seven patients with moderate-to-severe (≥50%) nonocclusive sICAS and MRI studies (42 females and 115 males, mean age 58.69 ± 10.68 years). FIELD STRENGTH/SEQUENCE: 3D higher-resolution black-blood T1-weighted fast-spin-echo sequence at 3.0 T. ASSESSMENT: HIP (signal intensity [SI] of plaque-to-adjacent gray matter >1.0 on non-contrast T1-weighted images) and non-HIP plaques were identified. HIP plaques were categorized as edge type (high SI adjacent to lumen) and non-edge type (high SI within plaque). Clinical and imaging features of different plaque types were compared. Stroke recurrence was assessed through telephone or medical records at 3 and 6 months, and then once a year post-MRI. The relationship between edge type and non-edge types HIP with stroke recurrence was analyzed. STATISTICAL TESTS: Student's t test, Mann-Whitney U-test, chi square test and Fisher's exact test to compare features between plaque types. Kaplan-Meier curves (with log-rank tests) and Cox proportional hazards regression to assess relationship between stroke recurrence and different plaque types. A two-tailed P-value of <0.05 was considered statistically significant. RESULTS: Of 157 culprit lesions, 87 (55%) were HIPs (43 edge type, 44 non-edge type) and 70 (45%) were non-HIPs. Plaque thickness, area, and volume were significantly higher for HIPs than for non-HIPs. Among patients with HIPs, edge type was significantly more likely in the posterior circulation (53.5% vs. 27.3%), and had significantly higher plaque thickness, length, area, volume, plaque burden, and remodeling index than non-edge type. Edge-type HIP was significantly more common than non-edge HIP in patients with diabetes mellitus (51.2% vs. 29.5%) and dyslipidemia (79.1% vs. 54.5%). During median follow-up of 27 months, 33 patients experienced stroke recurrence. Recurrence was associated with edge-type HIP (adjusted hazard ratio = 2.83; 95% confidence interval: 1.40-5.69), both in the overall cohort (34.9% vs. 15.8%) and in patients with HIP (34.9% vs. 9.0%). Age ≥60 years and edge-type HIP had a significant interaction. DATA CONCLUSIONS: Hyperintense plaque may be categorized as edge type or non-edge type. Edge-type HIP may be a potential MRI biomarker of stroke recurrence. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Angiography-based hemodynamic features predict recurrent ischemic events after angioplasty and stenting of intracranial vertebrobasilar atherosclerotic stenosis.

OBJECTIVES: To assess the predictive value of hemodynamic features for stroke relapse in patients with intracranial vertebrobasilar atherosclerotic stenosis treated with percutaneous transluminal angioplasty and stenting (PTAS) using quantitative digital subtraction angiography (q-DSA). METHODS: In this retrospective longitudinal study, patients with intracranial vertebrobasilar atherosclerotic stenosis and who underwent PTAS treatment between January 2012 and May 2020 were enrolled. The q-DSA assessment was performed before and after PTAS. ROIs 1-4 were placed along the vertebral artery, proximal and distal basilar artery, and posterior cerebral artery; ROIs 5-8 were in 5 mm and 10 mm proximal and distal to the lesion, respectively. Relative time to peak (rTTP) was defined as the difference in TTP between ROIs. Cox regression analyses were performed to determine risk factors for recurrent stroke. RESULTS: A total of 137 patients (mean age, 62 years ± 10 [standard deviation], 83.2% males) were included, and 26 (19.0%) patients had stroke relapse during follow-up (median time of 42.6 months [interquartile range, 19.7-60.7]). Preprocedural rTTP4-1 (adjusted hazard ratio (HR) = 2.270; 95% CI 1.371-3.758; p = 0.001) and preprocedural rTTP8-5 (adjusted HR = 0.240; 95% CI 0.088-0.658; p = 0.006) were independently associated with the recurrent stroke. These hemodynamic parameters provided an incremental prognostic value for stroke relapse (AUC, 0.817 [0.704-0.931]; the net reclassification index, 0.431 [0.057-0.625]; and the integrated discrimination index, 0.140 [0.035-0.292]). CONCLUSIONS: In patients with intracranial vertebrobasilar atherosclerosis treated with PTAS, preprocedural prolonged TTP of the target vessel and shortened trans-stenotic TTP difference were associated with stroke relapse. Q-DSA-defined hemodynamic parameters provided incremental predictive value over conventional parameters for stroke recurrence. CLINICAL RELEVANCE STATEMENT: Quantitative DSA analysis enables intuitive observation and semi-quantitative evaluation of peri-therapeutic cerebral blood flow. More importantly, quantitative DSA-defined hemodynamic parameters have the potential for risk stratification of patients with intracranial atherosclerotic stenosis. KEY POINTS: Semi-quantitative angiography-based parameters can reflect pre- and postprocedural subtle changes in blood flow in patients with intracranial atherosclerotic stenosis. Although angioplasty procedures can significantly improve blood flow status, patients with more restricted baseline blood flow still show a higher risk of stroke recurrence. Angiography-based hemodynamic features possess prognostic value and can serve as clinical markers to assess stroke risk of patients with intracranial atherosclerotic stenosis.

Distinguishing Intracranial Diabetes-Related Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging-Based Radiomics Study.

INTRODUCTION: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. MATERIALS AND METHODS: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features, and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. RESULTS: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07-1.28) and area (OR: 1.13; 95% CI: 1.02-1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). CONCLUSIONS: The use of radiomics features of intracranial plaques on high-resolution magnetic resonance imaging can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

ASPECTS-Based Attenuation Changes on CT Angiography as an Imaging Biomarker to Predict Hemorrhagic Transformation in Acute Ischemic Stroke.

INTRODUCTION: Imaging-based early warning indicators and feasible stratification of acute ischemic stroke (AIS) patients with hemorrhagic transformation (HT), especially high-risk patients with parenchymal hematoma (PH), are crucial in determining subsequent treatment strategies. This study combined automated ASPECTS software with noncontrast CT (NCCT) and CTA source image (CTASI) attenuation changes using Hounsfield unit (HU) values to predict HT and PH in patients with AIS. MATERIALS AND METHODS: We retrospectively enrolled 172 consecutive patients with anterior circulation large-vessel occlusion between 2016 and 2020. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to investigate the relationship between NCCT and CTASI-ASPECTS-HU, as well as other clinical and radiological parameters of HT and PH. Univariate and multivariate logistic regression analyses were performed to explore risk factors for HT or PH, and an ROC curve was used to evaluate their diagnostic values. RESULTS: A multivariate analysis showed that CTASI-ASPECTS-HU and NIHSS score were independent predictors of HT (CTASI-ASPECTS-HU: odds ratio (OR), 2.22; 95% CI, 1.01-4.92; NIHSS: OR, 1.07; 95% CI, 1.02-1.13) and PH (CTASI-ASPECTS-HU: OR, 6.51; 95% CI, 2.29-18.50; NIHSS: OR, 1.07; 95% CI, 1.01-1.13). According to ROC analysis, CTASI-ASPECTS-HU >0.09 identified HT (area under the curve, 0.70; sensitivity, 70.15%; specificity, 61.90%), and CTASI-ASPECTS-HU >0.10 identified PH (area under the curve, 0.79; sensitivity, 76.19%; specificity, 73.33%). The area under the curve for predicting HT or PH increased when CTASI-ASPECTS-HU was combined with NIHSS score (HT: area under the curve, 0.74; sensitivity, 73.13%; specificity, 70.48%; PH: area under the curve, 0.81; sensitivity, 85.71%; specificity, 72.38%). CONCLUSION: CTASI-ASPECTS-HU is a reliable radiological predictor of HT and PH in patients with AIS. Its predictive efficacy is moderately improved when combined with NIHSS score.

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion.

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.

Reflection and transmission of Gaussian beam by a uniaxial anisotropic slab.

The reflection and transmission of an incident Gaussian beam by a uniaxial anisotropic slab are investigated, by expanding the incident Gaussian beam, reflected beam, internal beam as well as transmitted beam in terms of cylindrical vector wave functions. The unknown expansion coefficients are determined by virtue of the boundary conditions. For a localized beam model, numerical results are provided for the normalized field intensity distributions, and the propagation characteristics are discussed concisely.

Overview of CircRNAs Roles and Mechanisms in Liver Fibrosis.

Liver fibrosis represents the reversible pathological process with the feature of the over-accumulation of extracellular matrix (ECM) proteins within the liver, which results in the deposition of fibrotic tissues and liver dysfunction. Circular noncoding RNAs (CircRNAs) have the characteristic closed loop structures, which show high resistance to exonuclease RNase, making them far more stable and recalcitrant against degradation. CircRNAs increase target gene levels by playing the role of a microRNA (miRNA) sponge. Further, they combine with proteins or play the role of RNA scaffolds or translate proteins to modulate different biological processes. Recent studies have indicated that CircRNAs play an important role in the occurrence and progression of liver fibrosis and may be the potential diagnostic and prognostic markers for liver fibrosis. This review summarizes the CircRNAs roles and explores their underlying mechanisms, with a special focus on some of the latest research into key CircRNAs related to regulating liver fibrosis. Results in this work may inspire fruitful research directions and applications of CircRNAs in the management of liver fibrosis. Additionally, our findings lay a critical theoretical foundation for applying CircRNAs in diagnosing and treating liver fibrosis.

Incremental value of enhanced plaque length for identifying intracranial atherosclerotic culprit plaques: a high-resolution magnetic resonance imaging study.

OBJECTIVES: Besides plaque enhancement grade, the incremental value of enhancement-related high-resolution MRI features in defining culprit plaques needs further evaluation. This study was focused on assessing whether plaque enhancement features contribute to culprit plaque identification and further risk stratification. METHODS: We retrospectively studied patients who experienced an acute ischaemic stroke and transient ischaemic attack due to intracranial atherosclerosis from 2016 to 2022. The enhancement features included enhancement grade, enhanced length, and enhancement quadrant. Associations between plaque enhancement features and culprit plaques, as well as diagnostic value, were investigated using logistic regression and receiver operating characteristic analyses. RESULTS: Overall, 287 plaques were identified, of which 231 (80.5%) and 56 (19.5%) were classified as culprit and non-culprit plaques, respectively. Comparison of the pre- and post-enhancement images revealed enhanced length longer than the plaque length in 46.32% of the culprit plaques. Multivariate logistic regression showed that enhanced length longer than plaque length (OR 6.77; 95% CI 2.47-18.51) and grade II enhancement (OR 7.00; 95% CI 1.69-28.93) were independently associated with culprit plaques. The area under the curve value for the combination of stenosis and plaque enhancement grade for the diagnosis of culprit plaques was 0.787, which increased significantly to 0.825 on the addition of enhanced length longer than the plaque length (p = 0.026 for DeLong's test). CONCLUSIONS: Enhanced length longer than the plaque length and grade II enhancement were independently associated with culprit plaques. The combination of the enhanced plaque features resulted in better culprit plaque identification.

Gaussian beam scattering by a rotationally uniaxial anisotropic sphere.

Within the generalized Lorenz-Mie theory framework, an analytic solution to Gaussian beam scattering by a rotationally uniaxial anisotropic sphere is presented. The scattered fields as well as the fields within the anisotropic sphere are expanded in terms of infinite series with spherical vector wave functions by using an appropriate expansion of the incident Gaussian beam. The unknown expansion coefficients are determined from a system of linear equations derived from the boundary conditions. Numerical results of the normalized differential scattering cross section are shown, and the scattering characteristics are discussed concisely.

Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning.

BACKGROUND: Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke. OBJECTIVE: To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning. METHODS: 136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models. RESULTS: Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606). CONCLUSION: The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.

A Nomogram to Predict Symptomatic Intracranial Hemorrhage After Intravenous Thrombolysis in Chinese Patients.

BACKGROUND AND AIMS: A reliable predictive score system to identify the risk of symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis (IVT) in acute ischemic stroke patients is of great essence. We aimed to develop a nomogram for predicting the risk of sICH after IVT in Chinese patients. METHODS: We recruited acute ischemic stroke patients who were treated with IVT from five advanced stroke centers in China from April 2014 to November 2020. sICH was diagnosed according to the European Cooperative Acute Stroke Study II (ECASS-II) definition. Multivariable logistic regression was performed to construct the best-fit nomogram. The discrimination and calibration of the nomogram were evaluated by the area under the receiver operating characteristic curve (AUC-ROC) and calibration plot. RESULTS: A total of 1200 patients were enrolled, of whom 66 (5.5%) developed sICH. In the multivariate logistic regression model, atrial fibrillation (odds ratio [OR] 3.25; 95% confidence interval [CI], 1.89-5.60; P < 0.001), baseline glucose level (OR, 1.13; 95% CI, 1.07-1.20; P < 0.001), neutrophil to lymphocyte ratio (OR, 1.05; 95% CI, 1.01-1.09; P = 0.024) and baseline National Institute of Health Stroke Scale (NIHSS) (OR, 1.07; 95% CI, 1.04-1.10; P < 0.001) were independent predictors for sICH and were used to generate the nomogram. The nomogram demonstrated good discrimination as the AUC-ROC value was 0.788 (95% CI, 0.737-0.840). The calibration plot revealed good calibration. CONCLUSION: The nomogram consisted of atrial fibrillation, baseline glucose level, neutrophil to lymphocyte ratio, and NIHSS score may predict the risk of sICH in Chinese acute ischemic stroke patients treated with IVT.

ASPECTS-based net water uptake as an imaging biomarker for lesion age in acute ischemic stroke.

OBJECTIVE: We assessed the value of computed tomography (CT) and automated Alberta Stroke Program Early CT Score (ASPECTS) with net water uptake (NWU) to predict stroke onset time. METHODS: Two-hundred forty stroke patients with anterior circulation large-vessel occlusion were included. CT-ASPECTS-NWU values were calculated by comparing the mean Hounsfield units of affected ASPECTS regions with unaffected contralateral regions. The correlation between ASPECTS-NWU and stroke onset to CT time was assessed. ASPECTS-NWU predictive values were calculated to identify a stroke onset to CT time of within 4.5/6 h. RESULTS: A correlation existed between stroke onset to CT time and ASPECTS-NWU (r = 0.65, p < 0.001), which was affected by collateral status and infarct location. The area under the receiver operating characteristic (ROC) curve (AUC) for distinguishing a stroke onset to CT time of within 4.5 h was 0.837 (95% confidence interval [CI] 0.784-0.881; optimal cutoff 7%; sensitivity 87.10%; specificity 62.36%). The multi-index AUC was 0.884 (95% CI 0.837-0.922). The AUC for distinguishing a stroke onset to CT time of within 6 h was 0.836 (95% CI 0.783-0.880; optimal cutoff 9%; sensitivity 72.73%; specificity 81.16%). The multi-index AUC was 0.881 (95% CI 0.834-0.920). CONCLUSIONS: ASPECTS-NWU may be used to determine stroke onset time in patients with unwitnessed or wake-up stroke.

Automated ASPECTS for multi-modality CT predict infarct extent and outcome in large-vessel occlusion stroke.

PURPOSE: This study aimed to use the automated Alberta Stroke Program Early CT Score (ASPECTS) software to assess the value of different CT modalities (non-contrast CT, CT angiography [CTA]-arterial, CTA-venous, and arterial- and venous-phase mismatch-ASPECTS) in predicting the final infarct extent and clinical outcome in large-vessel occlusion stroke. METHODS: This retrospective study included patients with large-vessel occlusion stroke who underwent reperfusion therapy during 2015 to 2019. Correlations between different CT-ASPECTS modalities and follow-up CT-ASPECTS and outcome were determined using Spearman rank correlation coefficient. Receiver operating characteristic curve analysis was used to assess the ability of different CT-ASPECTS modalities to identify patients with good outcomes. RESULTS: One hundred and thirty-five patients were included. We found almost-perfect correlation between CTA-venous-ASPECTS and follow-up CT-ASPECTS (r = 0.92; 95% CI: 0.89-0.95), better than that in other CT modalities. The 90-day modified Rankin scale (mRS) score substantially correlated with CTA-venous-ASPECTS (r = -0.64; 95% CI: -0.73 to -0.52). The ROC curve analysis showed CTA-venous-ASPECTS had the highest area under the curve (AUC: 0.82; 95% CI: 0.75-0.89; P < 0.001), followed by mismatch-ASPECTS (AUC: 0.75; 95% CI: 0.65-0.85; P < 0.001). When emphasizing the sensitivity for identifying patients with good outcomes, the best cut-off point of mismatch-ASPECTS was -3 with the highest sensitivity (91.30%). CONCLUSIONS: CTA-venous-ASPECTS is a reliable tool to predict the infarct extent and outcome. Furthermore, mismatch-ASPECTS may represent images in different angiographic phases and was sensitive for prognosis prediction.