Identification of high-risk plaque features in intracranial atherosclerosis: initial experience using a radiomic approach

Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment

Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered by the included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.

Radiomics Signatures of Carotid Plaque on Computed Tomography Angiography : An Approach to Identify Symptomatic Plaques

PURPOSE

To develop and validate a combined model incorporating conventional clinical and imaging characteristics and radiomics signatures based on head and neck computed tomography angiography (CTA) to assess plaque vulnerability.

METHODS

We retrospectively analyzed 167 patients with carotid atherosclerosis who underwent head and neck CTA and brain magnetic resonance imaging (MRI) within 1 month. Clinical risk factors and conventional plaque characteristics were evaluated, and radiomic features were extracted from the carotid plaques. The conventional, radiomics and combined models were developed using fivefold cross-validation. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses.

RESULTS

Patients were divided into symptomatic (n = 70) and asymptomatic (n = 97) groups based on MRI results. Homocysteine (odds ratio, OR 1.057; 95% confidence interval, CI 1.001-1.116), plaque ulceration (OR 6.106; 95% CI 1.933-19.287), and carotid rim sign (OR 3.285; 95% CI 1.203-8.969) were independently associated with symptomatic status and were used to construct the conventional model and s radiomic features were retained to establish the radiomics model. Radiomics scores incorporated with conventional characteristics were used to establish the combined model. The area under the ROC curve (AUC) of the combined model was 0.832, which outperformed the conventional (AUC = 0.767) and radiomics (AUC = 0.797) models. Calibration and decision curves analysis showed that the combined model was clinically useful.

CONCLUSION

Radiomics signatures of carotid plaque on CTA can well predict plaque vulnerability, which may provide additional value to identify high-risk patients and improve outcomes.

Identification of vulnerable carotid plaque with CT-based radiomics nomogram

AIM

To develop and validate a radiomics nomogram for identifying high-risk carotid plaques on computed tomography (CT) angiography (CTA).

MATERIALS AND METHODS

A total of 280 patients with symptomatic (n=131) and asymptomatic (n=139) carotid plaques were divided into a training set (n=135), validation set (n=58), and external test set (n=87). Radiomic features were extracted from CTA images. A radiomics model was constructed based on selected features and a radiomics score (rad-score) was calculated. A clinical factor model was constructed by demographics and CT findings. A radiomics nomogram combining independent clinical factors and the rad-score was constructed. The diagnostic performance of three models was evaluated and validated by region of characteristic curves.

RESULTS

Calcification and maximum plaque thickness were the independent clinical factors. Twenty-four features were used to build the radiomics signature. In the validation set, the nomogram (area under the curve [AUC], 0.977; 95% CI, 0.899-0.999) performed better (p=0.017 and p=0.031) than the clinical factor model (AUC, 0.862; 95% CI, 0.746-0.938) and radiomics signature (AUC, 0.944; 95% CI, 0.850-0.987). In external test set, the nomogram (AUC, 0.952; 95% CI, 0.884-0.987) and radiomics signature (AUC, 0.932; 95% CI, 0.857-0.975) showed better discrimination capability (p=0.002 and p=0.037) than clinical factor model (AUC, 0.818; 95% CI, 0.721-0.892).

CONCLUSION

The CT-based nomogram showed satisfactory performance in identification of high-risk plaques in carotid arteries, and it may serve as a potential non-invasive tool to identify carotid plaque vulnerability and risk stratification.

Characteristics and evaluation of atherosclerotic plaques: an overview of state-of-the-art techniques

Atherosclerosis is an important cause of cerebrovascular and cardiovascular disease (CVD). Lipid infiltration, inflammation, and altered vascular stress are the critical mechanisms that cause atherosclerotic plaque formation. The hallmarks of the progression of atherosclerosis include plaque ulceration, rupture, neovascularization, and intraplaque hemorrhage, all of which are closely associated with the occurrence of CVD. Assessing the severity of atherosclerosis and plaque vulnerability is crucial for the prevention and treatment of CVD. Integrating imaging techniques for evaluating the characteristics of atherosclerotic plaques with computer simulations yields insights into plaque inflammation levels, spatial morphology, and intravascular stress distribution, resulting in a more realistic and accurate estimation of plaque state. Here, we review the characteristics and advancing techniques used to analyze intracranial and extracranial atherosclerotic plaques to provide a comprehensive understanding of atheroma.

A Non-Linear Role of Hyperlipidemia on Progression of Intracranial Atherosclerotic Plaques and Acute Downstream Ischemic Events

AIM

Intracranial atherosclerotic stenosis (ICAS) is the leading cause of ischemic stroke worldwide. Hyperlipidemia is a major contributor to atherosclerosis. However, the effect of hyperlipidemia on the evolution of intracranial atherosclerotic plaques and downstream ischemic episodes remains unclear. In this study, we aimed to assess the radiological features of ICAS plaques and to explore the relationship between hyperlipidemia and plaque progression.

METHODS

We included people with ICAS (≥50% stenosis) undergoing high-resolution magnetic resonance imaging. The culprit plaque was defined as the sole, or in case of multiple stenosis, the narrowest plaque on the intracranial artery responsible for acute ischemic stroke. Demographic, clinical data, plaque features on MRI, and lipid parameters were compared between culprit and non-culprit plaques. Plaque enhancement was graded as Grade 0, 1 and 2 by comparing to the adjacent normal vessel wall and pituitary funnel after contrast enhancement on T1-weighted sequences.

RESULTS

162 patients were included (mean age 57.7±12.1 years, male 61.6%), 110 of whom were identified as culprit plaque with an ipsilateral acute stroke. High-grade enhancement was the most prominent MRI feature of the culpable plaque (Grade-2: OR 6.539, 95%CI 1.706-23.707, p=0.006). LDL cholesterol was significantly associated with overall acute ischemic stroke caused by culprit plaque. After stratification by enhancement grading LDL was independently associated with ischemic events in Grade-1 enhancement plaques (OR 6.778, 95%CI 2.122-21.649, p=0.001). In patients with Grade-2 enhancement plaques, however, LDL was not associated with ischemic event; in contrast, Neutrophil/Lymphocyte ratio was independently associated with ischemic events caused by Grade-2 enhancement plaques (OR 2.188, 95%CI 1.209-3.961, p=0.010).

CONCLUSIONS

LDL was related with ischemia events in intermediate stage of intracranial atherosclerotic plaque progression, an excellent period for intensive lipid-lowering treatment. In advanced stage, inflammatory agents maybe the main contributor to ischemic events.

Quality assessment of stroke radiomics studies: Promoting clinical application

PURPOSE

To evaluate the quality of radiomics studies on stroke using a radiomics quality score (RQS), Minimum Information for Medial AI reporting (MINIMAR) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to promote clinical application.

METHODS

PubMed MEDLINE and Embase were searched to identify radiomics studies on stroke. Of 464 articles, 52 relevant original research articles were included. The RQS, MINIMAR and TRIPOD were scored to evaluate the quality of the studies by neuroradiologists.

RESULTS

Only four studies (7.7 %) performed external validation. The mean RQS was 3.2 of 36 (8.9 %), and the basic adherence rate was 24.9 %. The adherence rate was low for conducting phantom study (1.9 %), stating comparison to 'gold standard' (1.9 %), offering potential clinical utility (13.5 %) and performing cost-effectiveness analysis (1.9 %). None of the studies performed a test-retest, stated biologic correlation, conducted prospective studies, or opened codes and data to the public, resulting in low RQS. The total MINIMAR adherence rate was 47.4 %. The overall adherence rate for TRIPOD was 54.6 %, with low scores for reporting the title (2.0 %), key elements of the study setting (6.1 %), and explaining the sample size (2.0 %).

CONCLUSIONS

The overall radiomics reporting quality and reporting of published radiomics studies on stoke was suboptimal. More thorough validation and open data are needed to increase clinical applicability of radiomics studies.

Association of pre-diabetes and type 2 diabetes mellitus with intracranial plaque characteristics in patients with acute ischemic stroke

OBJECTIVES

To investigate the association of pre-diabetes(i.e., the early stages of glucometabolic disturbance) and Type 2 diabetes mellitus (T2DM) with intracranial plaque characteristics in patients with acute ischemic stroke using three-dimensional high-resolution MR imaging.

METHODS

One hundred and forty-three symptomatic patients with acute ischemic stroke attribute to intracranial atherosclerotic plaque were prospectively enrolled. All participants were further divided into three groups: normal glucose metabolism(non-diabetes) group(n = 41), pre-diabetes group(n = 45), and T2DM group(n = 57) according to glucometabolic status. Culprit plaque characteristics (such as plaque burden, normalized wall index and enhancement ratio), total plaque number, and global plaque enhancement score were analyzed and compared among the three glucometabolic groups. The association between pre-diabetes and T2DM with intracranial plaque characteristics was assessed by logistic regression and multivariate linear regression.

RESULTS

Plaque number was higher in patients with pre-diabetes and T2DM compared with those with non-diabetes(3.71 ± 1.83 and 3.75 ± 1.71 vs 2.24 ± 1.46, p = 0.006). Multivariate logistic regression showed a significant association of multiple intracranial plaques with pre-diabetes(OR 3.524, 95% CI 1.082 ~ 11.479, p = 0.037), T2DM(OR 3.760, 95% CI 1.098 ~ 12.872, p = 0.035) and luminal stenotic rate. Both pre-diabetes and T2DM were significantly associated with culprit plaque enhancement ratio(β = 0.527 and β = 0.536; respectively; p < 0.001) and global plaque enhancement score(β = 0.264 and β = 0.373; respectively; p < 0.05).

CONCLUSIONS

Patients with pre-diabetes and T2DM had similar intracranial atherosclerotic plaque vulnerability, as demonstrated by multiple plaques, increased culprit plaque enhancement ratio and global plaque enhancement score.

ADVANCES IN KNOWLEDGE

Pre-diabetes might be a risk factor for intracranial plaque vulnerability. It is necessary to monitor a slight increase in blood glucose in non-diabetes patients with acute ischemic stroke.

Impact of lesion size on reproducibility of quantitative measurement and radiomic features in vessel wall MRI

OBJECTIVES

To investigate reproducibility of quantitative measurement and radiomic features in vessel wall MRI (VW-MRI), evaluate the impact of lesion size, and identify reproducible radiomic features.

METHODS

This retrospective, single-center study included 251 patients (mean age, 53 ± 12 years; 128 women) with atherosclerosis, dissection, aneurysm, moyamoya disease, and vasculitis of the intracranial arteries who underwent three-dimensional turbo spin echo T1-weighted image. Lesion thickness, volume, and signal intensity were measured, and 157 radiomic features were extracted. Intra-observer reproducibility of quantitative measurement and radiomic features was evaluated by calculating the concordance correlation coefficient (CCC) and proportion of radiomic features above the predefined CCC. The reproducibility of quantitative measurement and radiomic features according to lesion size (binary comparison and stratification into 5 and 18 groups) was evaluated.

RESULTS

There was an overall serial increase in CCC for thickness measurement when stratified by lesion thickness and volume. There was an overall serial increase in the median CCC for radiomic features and proportion of radiomic features with CCC > 0.85 when stratified by lesion thickness and volume. Reproducibility of radiomic features was higher in the lesions with thickness ≥ 2.5 mm (median CCC, 0.97 vs. 0.89, p < .001; proportion with CCC > 0.85, 88.5% vs. 59.6%, p < .001) and volume ≥ 50 mm³ (median CCC, 0.97 vs. 0.88, p < .001; proportion with CCC > 0.85, 90.4% vs. 59.0%, p < .001). Intensity-based statistical features remained most reproducible in the thinnest and smallest lesions.

CONCLUSIONS

Intra-observer reproducibility of thickness measurement and radiomic features was affected by lesion size in VW-MRI although intensity-based statistical features remained most reproducible.

KEY POINTS

• There was an overall serial increase in CCC for thickness measurement when stratified by lesion size. • There was an overall serial increase in the median CCC for radiomic features and proportion of radiomic features with CCC > 0.85 when stratified by lesion size. • Intensity-based statistical features remained most reproducible in the thinnest and smallest lesions.

Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm

Introduction

The primary factor for cardiovascular disease and upcoming cardiovascular events is atherosclerosis. Recently, carotid plaque texture, as observed on ultrasonography, is varied and difficult to classify with the human eye due to substantial inter-observer variability. High-resolution magnetic resonance (MR) plaque imaging offers naturally superior soft tissue contrasts to computed tomography (CT) and ultrasonography, and combining different contrast weightings may provide more useful information. Radiation freeness and operator independence are two additional benefits of M RI. However, other than preliminary research on MR texture analysis of basilar artery plaque, there is currently no information addressing MR radiomics on the carotid plaque.

Methods

For the automatic segmentation of MRI scans to detect carotid plaque for stroke risk assessment, there is a need for a computer-aided autonomous framework to classify MRI scans automatically. We used to detect carotid plaque from MRI scans for stroke risk assessment pre-trained models, fine-tuned them, and adjusted hyperparameters according to our problem.

Results

Our trained YOLO V3 model achieved 94.81% accuracy, RCNN achieved 92.53% accuracy, and MobileNet achieved 90.23% in identifying carotid plaque from MRI scans for stroke risk assessment. Our approach will prevent incorrect diagnoses brought on by poor image quality and personal experience.

Conclusion

The evaluations in this work have demonstrated that this methodology produces acceptable results for classifying magnetic resonance imaging (MRI) data.

Imaging and Hemodynamic Characteristics of Vulnerable Carotid Plaques and Artificial Intelligence Applications in Plaque Classification and Segmentation

Stroke is a massive public health problem. The rupture of vulnerable carotid atherosclerotic plaques is the most common cause of acute ischemic stroke (AIS) across the world. Currently, vessel wall high-resolution magnetic resonance imaging (VW-HRMRI) is the most appropriate and cost-effective imaging technique to characterize carotid plaque vulnerability and plays an important role in promoting early diagnosis and guiding aggressive clinical therapy to reduce the risk of plaque rupture and AIS. In recent years, great progress has been made in imaging research on vulnerable carotid plaques. This review summarizes developments in the imaging and hemodynamic characteristics of vulnerable carotid plaques on the basis of VW-HRMRI and four-dimensional (4D) flow MRI, and it discusses the relationship between these characteristics and ischemic stroke. In addition, the applications of artificial intelligence in plaque classification and segmentation are reviewed.

Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients

BACKGROUND

Intraplaque hemorrhage (IPH), one of the key features of vulnerable plaques, has been shown to be associated with increased risk of stroke. The aim is to develop and validate a CT-based radiomics nomogram incorporating clinical factors and radiomics signature for the detection of IPH in carotid arteries.

METHODS

This retrospective study analyzed the patients with carotid plaques on CTA from January 2013 to January 2021 at two different institutions. Radiomics features were extracted from CTA images. Demographics and CT characteristics were evaluated to build a clinical factor model. A radiomics signature was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. The area under curves of three models were calculated by receiver operating characteristic analysis.

RESULTS

A total of 46 patients (mean age, 60.7 years ± 10.4 [standard deviation]; 36 men) with 106 carotid plaques were in the training set, and 18 patients (mean age, 61.4 years ± 10.1; 13 men) with 38 carotid plaques were in the external test sets. Stenosis was the independent clinical factor. Eight features were used to build the radiomics signature. The area under the curve (AUC) of the radiomics nomogram was significantly higher than that of the clinical factor model in both the training (p = 0.032) and external test (p = 0.039) sets.

CONCLUSIONS

A CT-based radiomics nomogram showed satisfactory performance in distinguishing carotid plaques with and without intraplaque hemorrhage.

Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis

OBJECTIVES

Carotid artery stenting (CAS) is an established treatment for local stenosis. The most common complication is new ipsilateral ischemic lesions (NIILs). This study aimed to develop models considering lesion morphological and compositional features, and radiomics to predict NIILs.

MATERIALS AND METHODS

One hundred and forty-six patients who underwent brain MRI and high-resolution vessel wall MR imaging (hrVWI) before and after CAS were retrospectively recruited. Lumen and outer wall boundaries were segmented on hrVWI as well as atherosclerotic components. A traditional model was constructed with patient clinical information, and lesion morphological and compositional features. Least absolute shrinkage and selection operator algorithm was performed to determine key radiomics features for reconstructing a radiomics model. The model in predicting NIILs was trained and its performance was tested.

RESULTS

Sixty-one patients were NIIL-positive and eighty-five negative. Volume percentage of intraplaque hemorrhage (IPH) and patients' clinical presentation (symptomatic/asymptomatic) were risk factors of NIILs. The traditional model considering these two features achieved an area under the curve (AUC) of 0.778 and 0.777 in the training and test cohorts, respectively. Twenty-two key radiomics features were identified and the model based on these features achieved an AUC of 0.885 and 0.801 in the two cohorts. The AUCs of the combined model considering IPH volume percentage, clinical presentation, and radiomics features were 0.893 and 0.842 in the training and test cohort respectively.

CONCLUSIONS

Compared with traditional features (clinical and compositional features), the combination of traditional and radiomics features improved the power in predicting NIILs after CAS.

KEY POINTS

• Volume percentage of IPH and symptomatic events were independent risk factors of new ipsilateral ischemic lesions (NIILs). • Radiomics features derived from carotid artery high-resolution vessel wall imaging had great potential in predicting NIILs after CAS. • The combination model with radiomics and traditional features further improved the diagnostic performance than traditional features alone.

Wall characteristics of atherosclerotic middle cerebral arteries in patients with single or multiple infarcts: A high-resolution MRI Study

Background and purpose

Understanding the stroke mechanism of middle cerebral artery (MCA) atherosclerosis may inform secondary prevention. The aim of this study was to explore the relationship between vascular wall characteristics and infarction patterns using high-resolution magnetic resonance imaging (HRMRI) and diffusion-weighted imaging (DWI).

Methods

From November 2018 to March 2021, patients with acute ischemic stroke due to MCA atherosclerotic disease were retrospectively analyzed. The wall characteristics of atherosclerotic MCA, including conventional characteristics and histogram-defined characteristics, were evaluated using HRMRI. Patients were divided into single-infarction and multiple-infarction groups based on DWI, and wall characteristics were compared between the two groups.

Results

Of 92 patients with MCA plaques, 59 patients (64.1%) had multiple infarcts, and 33 (35.9%) had single infarcts. The histogram-defined characteristics showed no differences between the single-infarction and multiple-infarction groups (P&gt;0.05). Plaque burden, degree of stenosis, and prevalence of intraplaque hemorrhage (IPH) were significantly greater in the multiple-infarction group than in the single-infarction group (plaque burden: P = 0.001; degree of stenosis: P = 0.010; IPH: P = 0.019). Multivariate analysis showed that plaque burden (odds ratio: 1.136; 95% confidence interval: 1.054–1.224, P = 0.001) and IPH (odds ratio: 5.248; 95% confidence interval: 1.573–17.512, P = 0.007) were independent predictors for multiple infarction.

Conclusion

IPH and plaque burden are independently associated with multiple infarcts. HRMRI may provide new insight into the mechanisms underlying the different MCA infarction patterns.

High-risk features of basilar artery atherosclerotic plaque

INTRODUCTION

High-resolution magnetic resonance imaging (HR-MRI) is used to characterize atherosclerotic plaque. The present study aimed to determine the high-risk features of the basilar artery (BA) atherosclerotic plaque.

METHODS

Patients with advanced BA stenosis were screened. The features including the ruptured fibrous cap (RFC), lipid core, intraplaque hemorrhage (IPH), plaque enhancement, and calcification were assessed by using high-resolution MRI. The relationship between the features and acute infarction was analyzed.

RESULTS

From 1 June 2014 to 31 December 2018, a total of 143 patients with 76 new strokes were included. RFC was identified in 25% of symptomatic and 10.4% of asymptomatic patients. IPH was identified in 48.7% of symptomatic and 25.4% of asymptomatic patients. RFC (3.157, 95% CI 1.062 to 9.382, p = 0.039) and IPH (2.78, 95% CI 1.127 to 6.505, p = 0.026) were independent risk factors for acute infarction.

CONCLUSION

Our study showed that RFC and IPH of BA plaque were independent risk factors for acute infarction.

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 Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography

Aim

To develop and validate a radiomics nomogram on non-contrast-enhanced computed tomography (NECT) for classifying hematoma entities in patients with acute spontaneous intracerebral hemorrhage (ICH).

Materials and Methods

One hundred and thirty-five patients with acute intraparenchymal hematomas and baseline NECT scans were retrospectively analyzed, i.e., 52 patients with vascular malformation-related hemorrhage (VMH) and 83 patients with primary intracerebral hemorrhage (PICH). The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed. After extracting the radiomics features of hematomas from baseline NECT, the least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct the radiomics signature. Multivariate logistic regression analysis was used to determine the independent clinical-radiological risk factors, and a clinical model was constructed. A predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors. Nomogram performance was assessed in the training cohort and tested in the validation cohort. The capability of models was compared by calibration, discrimination, and clinical benefit.

Results

Six features were selected to establish radiomics signature via LASSO regression. The clinical model was constructed with the combination of age [odds ratio (OR): 6.731; 95% confidence interval (CI): 2.209–20.508] and hemorrhage location (OR: 0.089; 95% CI: 0.028–0.281). Radiomics nomogram [area under the curve (AUC), 0.912 and 0.919] that incorporated age, location, and radiomics signature outperformed the clinical model (AUC, 0.816 and 0.779) and signature (AUC, 0.857 and 0.810) in the training cohort and validation cohorts, respectively. Good calibration and clinical benefit of nomogram were achieved in the training and validation cohorts.

Conclusion

Non-contrast-enhanced computed tomography-based radiomics nomogram can predict the individualized risk of VMH in patients with acute ICH.

Radiomics Nomogram for Predicting Stroke Recurrence in Symptomatic Intracranial Atherosclerotic Stenosis

Objective

To develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS).

Methods

The data of 156 patients with SICAS were obtained from the hospital database. Those with and without stroke recurrence were identified. The 156 patients were separated into a training cohort (n = 110) and a validation cohort (n = 46). Baseline clinical data were collected from our medical records, and plaque radiological features were extracted from vascular wall high-resolution imaging (VW-HRMRI). The imaging sequences included 3D-T1WI-VISTA, T2WI, and 3D-T1WI-VISTA-enhanced imaging. Least absolute shrinkage and selection operator (LASSO) analysis were used to select the radiomics features associated with stroke recurrence. Then, multiple logistic regression analysis of clinical risk factors, radiological features, and radiomics signatures were performed, and a predictive nomogram was constructed to predict the probability of stroke recurrence in SICAS. The performance of the nomogram was evaluated.

Results

Diabetes mellitus, plaque burden, and enhancement ratio were independent risk factors for stroke recurrence [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.04–3.79, p = 0.018; OR = 1.76, per 10% increase, 95% CI, 1.28–2.41, p < 0.001; and OR = 1.94, 95% CI: 1.27–3.09, p < 0.001]. Five features of 3D-T1WI-VISTA, six features of T2WI, and nine features of 3D-T1WI-VISTA-enhanced images were associated with stroke recurrence. The radiomics signature in 3D-T1WI-VISTA-enhanced images was superior to the radiomics signature of the other two sequences for predicting stroke recurrence in both the training cohort [area under the curve (AUC), 0.790, 95% CI: 0.669–0.894] and the validation cohort (AUC, 0.779, 95% CI: 0.620–0.853). The combination of clinical risk factors, radiological features, and radiomics signature had the best predictive value (AUC, 0.899, 95% CI: 0.844–0.936 in the training cohort; AUC, 0.803, 95% CI: 0.761–0.897 in the validation cohort). The C-index of the nomogram was 0.880 (95% CI: 0.805–0.934) and 0.817 (95% CI: 0.795–0.948), respectively, in the training and validation cohorts. The decision curve analysis further confirmed that the radiomics nomogram had good clinical applicability with a net benefit of 0.458.

Conclusion

The radiomics features were helpful to predict stroke recurrence in patients with SICAS. The nomogram constructed by combining clinical high-risk factors, plaque radiological features, and radiomics features is a reliable tool for the individualized risk assessment of predicting the recurrence of SICAS stroke.

Vertebrobasilar Junction Angle Over 90°: A Potential Imaging Marker Associated With Vertebrobasilar Atherosclerosis

Objective: Whether the cerebral vascular variations play an important role in the progression of intracranial atherosclerosis is yet largely unclear. We aimed to investigate the relationship between the magnitude of the vertebrobasilar junction (VBJ) angle and the imaging features of vertebrobasilar artery atherosclerosis.

Methods: Adult patients with acute ischemic stroke or transient ischemic attack undergoing a 3.0-tesla vessel wall magnetic resonance imaging (VW-MRI) scanning were consecutively included. Imaging features of vertebrobasilar artery atherosclerosis were assessed on the reconstructed short axis of VW-MRI at the most stenotic site. The VBJ angle degree was measured on magnetic resonance angiography and classified into the angle ≥90° or <90°.

Results: Among 68 patients (mean age = 63.5 ± 9.4 years old; 63.2% were male) with vertebrobasilar atherosclerosis, 33 had a VBJ angle ≥90° and 35 had a VBJ angle <90°. Compared to the vertebrobasilar plaques with VBJ angle <90°, those with VBJ angle ≥90° had a heavier plaque burden (84.35 vs. 70.58%, p < 0.001) and higher prevalence of intraplaque hemorrhage (17.1 vs. 3.3%, p = 0.01). In the regression analyses, the VBJ angle ≥90° was also robustly associated with plaque burden (odds ratio, 1.11; 95% confidential interval, 1.043–1.18; p = 0.001) and intraplaque hemorrhage (odds ratio, 5.776; 95% confidential interval, 1.095–30.46; p = 0.039) of vertebrobasilar atherosclerosis.

Conclusion: The VBJ angle over 90° might aggravate the vessel wall condition of the atherosclerotic vertebrobasilar arteries, which might serve as a potential risk factor for vertebrobasilar atherosclerosis.

Comparison of symptomatic vertebrobasilar plaques between patients with and without Diabetes Mellitus using computed tomographic angiography and vessel wall magnetic resonance imaging

OBJECTIVES

Diabetes mellitus is significantly associated with posterior circulation ischemic stroke. We aimed to compare the characteristics of vertebrobasilar plaques in symptomatic patients with and without diabetes using high-resolution vessel wall magnetic resonance imaging and computed tomographic angiography.

METHODS

From April 2017 to May 2021, cases from patients with transient ischemic attack or stroke in the posterior circulation territory who underwent high-resolution vessel wall magnetic resonance imaging and computed tomographic angiography were reviewed. Characteristics of culprit vertebrobasilar plaques were compared between patients with and without diabetes. Multivariate regression analysis was performed to assess the correlation between culprit plaque characteristics and diabetes.

RESULTS

A total of 148 patients were included and 75 patients were diagnosed with diabetes mellitus. Patients with diabetes had more intraplaque hemorrhage, calcification, spotty calcification presence, and higher calcification volume (all p < 0.05) compared with those without diabetes. Multivariate analysis demonstrated differences in the presence of intraplaque hemorrhage (p = 0.045) and number of spotty calcifications (p = 0.047) were statistically significant after adjusting for baseline characteristics.

CONCLUSIONS

Symptomatic patients with diabetes have a higher incidence of intraplaque hemorrhage and larger calcification burden than those without diabetes, indicating the association of diabetes with more advanced plaque features in the posterior circulation.

The Longitudinal Distribution and Stability of Curved Basilar Artery Plaque: A Study Based on HR-MRI

AIMS

This study aims to evaluate the differences in the characteristics of atherosclerotic plaques in the proximal, curved, and distal segments of the curved basilar artery (BA) through high-resolution magnetic resonance imaging(HR-MRI).

METHODS

The imaging and clinical data of 146 patients were retrospectively analyzed. On the basis of three-dimensional (3D) time -of -flight magnetic resonance angiography (3D-TOF-MRA), 51 patients with BA curvature were selected for the study. The BA plaque is divided into three groups: proximal, curved, and distal. Plaques were identified and analyzed according to spin echo acquisition imaging via T1-weighted 3D volumetric isotropic Tse acquisition (T1W-3D -VISTA), and compare the differences in clinical related factors and plaque characteristics between groups. Diffusion-weighted imaging (DWI) and/or T2WI identified brainstem infarction. The patients were divided into symptomatic and asymptomatic groups. The correlation between plaque location and symptoms was identified and analyzed.

RESULTS

Among 51 patients, a total of 376 plaques were detected. Plaques in the proximal and curved segments are more common than those in the distal segments. Proximal plaques are more likely to have intraplaque hemorrhage ( P=0.002 ＜0.05). There was no significant difference in the distribution of criminal plaques and non-criminal plaques between each group (P=0.36 ＞0.05).

CONCLUSION

Plaques in the proximal and curved segments of the BA are more common than those in the distal segments. The proximal plaque is more prone to intraplaque hemorrhage.

Computed Tomography Texture Analysis of Carotid Plaque as Predictor of Unfavorable Outcome after Carotid Artery Stenting: A Preliminary Study

Novel biomarkers are advocated to manage carotid plaques. Therefore, we aimed to test the association between textural features of carotid plaque at computed tomography angiography (CTA) and unfavorable outcome after carotid artery stenting (CAS). Between January 2010 and January 2021, were selected 172 patients (median age, 77 years; 112/172, 65% men) who underwent CAS with CTA of the supra-aortic vessels performed within prior 6 months. Standard descriptors of the density histogram were derived by open-source software automated analysis obtained by CTA plaque segmentation. Multiple logistic regression analysis, receiver operating characteristic (ROC) curve analysis and the area under the ROC (AUC) were used to identify potential prognostic variables and to assess the model performance for predicting unfavorable outcome (periprocedural death or myocardial infarction and any ipsilateral acute neurological event). Unfavorable outcome occurred in 17/172 (10%) patients (median age, 79 years; 12/17, 70% men). Kurtosis was an independent predictor of unfavorable outcome (odds ratio, 0.79; confidence interval, 0.65-0.97; p = 0.029). The predictive model for unfavorable outcome including CTA textural features outperformed the model without textural features (AUC 0.789 vs. 0.695, p = 0.004). In patients with stenotic carotid plaque, kurtosis derived by CTA density histogram analysis is an independent predictor of unfavorable outcome after CAS.

Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion

BACKGROUND

Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO).

METHODS

A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment.

RESULTS

The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665-0.875) and 0.792 (95% CI: 0.633-0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701-0.916) and 0.833 (95% CI: 0.702-0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614-0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712-0.928).

CONCLUSIONS

The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.

The Use of Pointwise Encoding Time Reduction With Radial Acquisition MRA to Assess Middle Cerebral Artery Stenosis Pre- and Post-stent Angioplasty: Comparison With 3D Time-of-Flight MRA and DSA

Background and Purpose: 3D pointwise encoding time reduction magnetic resonance angiography (PETRA-MRA) is a promising non-contrast magnetic resonance angiography (MRA) technique for intracranial stenosis assessment but it has not been adequately validated against digital subtraction angiography (DSA) relative to 3D-time-of-flight (3D-TOF) MRA. The aim of this study was to compare PETRA-MRA and 3D-TOF-MRA using DSA as the reference standard for intracranial stenosis assessment before and after angioplasty and stenting in patients with middle cerebral artery (MCA) stenosis. Materials and Methods: Sixty-two patients with MCA stenosis (age 53 ± 12 years, 43 males) underwent MRA and DSA within a week for pre-intervention evaluation and 32 of them had intracranial angioplasty and stenting performed. The MRAs' image quality, flow visualization within the stents, and susceptibility artifact were graded on a 1-4 scale (1 = poor, 4 = excellent) independently by three radiologists. The degree of stenosis was measured by two radiologists independently on DSA and MRAs. Results: There was an excellent inter-observer agreement for stenosis assessment on PETRA-MRA, 3D-TOF-MRA, and DSA (ICCs > 0.90). For pre-intervention evaluation, PETRA-MRA had better image quality than 3D-TOF-MRA (3.87 ± 0.34 vs. 3.38 ± 0.65, P < 0.001), and PETRA-MRA had better agreement with DSA for stenosis measurements compared to 3D-TOF-MRA (r = 0.96 vs. r = 0.85). For post-intervention evaluation, PETRA-MRA had better image quality than 3D-TOF-MRA for in-stent flow visualization and susceptibility artifacts (3.34 ± 0.60 vs. 1.50 ± 0.76, P < 0.001; 3.31 ± 0.64 vs. 1.41 ± 0.61, P < 0.001, respectively), and better agreement with DSA for stenosis measurements than 3D-TOF-MRA (r = 0.90 vs. r = 0.26). 3D-TOF-MRA significantly overestimated the stenosis post-stenting compared to DSA (84.9 ± 19.7 vs. 39.3 ± 13.6%, p < 0.001) while PETRA-MRA didn't (40.6 ± 13.7 vs. 39.3 ± 13.6%, p = 0.18). Conclusions: PETRA-MRA is accurate and reproducible for quantifying MCA stenosis both pre- and post-stenting compared with DSA and performs better than 3D-TOF-MRA.

Current Clinical Applications of Intracranial Vessel Wall MR Imaging

Intracranial vessel wall MR imaging (VWI) is increasingly being used as a valuable adjunct to conventional angiographic imaging techniques. This article will provide an updated review on intracranial VWI protocols and image interpretation. We review VWI technical considerations, describe common VWI imaging features of different intracranial vasculopathies and show illustrative cases. We review the role of VWI for differentiating among steno-occlusive vasculopathies, such as intracranial atherosclerotic plaque, dissections and Moyamoya disease. We also highlight how VWI may be used for the diagnostic work-up and surveillance of patients with vasculitis of the central nervous system and cerebral aneurysms.

Differences in Wall Shear Stress Between High-Risk and Low-Risk Plaques in Patients With Moderate Carotid Artery Stenosis: A 4D Flow MRI Study

This study aimed to evaluate the difference in wall shear stress (WSS) (axial, circumferential, and 3D) between high-risk and low-risk plaques in patients with moderate carotid artery stenosis and to identify which time points and directions play the dominant roles in determining the risk associated with plaques. Forty carotid arteries in 30 patients were examined in this study. All patients underwent high-resolution vessel wall (HRVW) imaging, diffusion-weighted imaging (DWI), and 4D flow MRI; HRVW imaging and DWI were used to separate low- and high-risk plaque. Twenty-four high-risk plaques and 16 low-risk plaques were enrolled. An independent-sample t-test was used to compare WSS between low- and high-risk plaques in the whole cardiac cycle and at 20 different time points in the cardiac cycle. The study found that patients with high-risk plaques had higher WSS than those with low-risk plaques throughout the entire cardiac cycle (p < 0.05), but the changes varied at the 20 different time points. The number of non-significant differences (p > 0.05) was less in diastole than in systole across different time points. The axial WSS values were higher than the circumferential WSS values; the difference in axial WSS values between high- and low-risk plaques was more significant than the difference in circumferential WSS, whereas 3D WSS values best reflected the difference between high-risk and low-risk plaques because they showed significant differences at every time point. In conclusion, increased WSS, especially during the diastolic period and in the axial direction, may be a signal of a high-risk plaque and may cause cerebrovascular events in patients with moderate carotid artery stenosis. Additionally, WSS can provide hemodynamic information and help clinicians make more appropriate decisions for patients with plaques.

Intracranial Atherosclerotic Plaque Characteristics and Burden Associated With Recurrent Acute Stroke: A 3D Quantitative Vessel Wall MRI Study

Background: Intracranial atherosclerotic disease (ICAD) tends to affect multiple arterial segments, and previous studies rarely performed a comprehensive plaque analysis of the entire circle of Willis for the evaluation of recurrent stroke risk. We aimed to investigate the features of circle of Willis ICAD on 3D magnetic resonance vessel wall imaging (MR-VWI) and their relationships with recurrent acute stroke. Methods: Patients with either acute ischemic stroke (within 4 weeks after stroke) or chronic ischemic stroke (after 3 months of stroke) due to intracranial atherosclerotic plaque underwent 3D contrast-enhanced MR-VWI covering major cerebral arteries. Participants were divided into three groups: first-time acute stroke, recurrent acute stroke, and chronic stroke. Culprit plaque (defined as the only lesion or the most stenotic lesion when multiple plaques were present within the same vascular territory of the stroke) and non-culprit plaque characteristics, including total plaque number, plaque thickness, plaque area, plaque burden (calculated as plaque area divided by outer wall area), enhancement ratio (ER), eccentricity, and stenosis, were measured and compared across the three groups. Associations between plaque characteristics and recurrent acute stroke were investigated by multivariate analysis. Results: A total of 176 participants (aged 61 ± 10 years, 109 men) with 702 intracranial plaques were included in this study. There were 80 patients with first-time acute stroke, 42 patients with recurrent acute stroke, and 54 patients with chronic stroke. More intracranial plaques were found per patient in the recurrent acute stroke group than in the first-time acute stroke or chronic stroke group (5.19 ± 1.90 vs. 3.71 ± 1.96 and 3.46 ± 1.33, p < 0.001). Patients in the recurrent acute stroke group had greater culprit plaque burden (p < 0.001) and higher culprit ER (p < 0.001) than the other two groups. After adjustment of clinical demographic factors, in multivariate analysis, coronary artery disease (CAD) (odds ratio, OR = 4.61; p = 0.035), total plaque number (OR = 1.54; p = 0.003), culprit plaque ER (OR = 2.50; p = 0.036), and culprit plaque burden (OR per 10% increment = 2.44; p = 0.010) were all independently associated with recurrent acute stroke compared to the first-time acute stroke. Conclusion: Increased intracranial atherosclerotic plaque number, higher culprit plaque ER, greater culprit plaque burden, and CAD are independently associated with recurrent acute stroke.

Identification of high risk clinical and imaging features for intracranial artery dissection using high-resolution cardiovascular magnetic resonance

BACKGROUND

Intracranial artery dissection (IAD) often causes headache and cerebral vascular ischemic events. The imaging characteristics of IAD remain unclear. This study aims to characterize the appearance of culprit and non-culprit IAD using high-resolution cardiovascular magnetic resonance imaging (hrCMR) and quantify the incremental value of hrCMR in identifying higher risk lesions.

METHODS

Imaging data from patients who underwent intervention examination or treatment using digital subtraction angiography (DSA) and hrCMR using a 3 T CMR system within 30 days after the onset of neurological symptoms were collected. The CMR protocol included diffusion-weighted imaging (DWI), black blood T1-, T2- and contrast-enhanced T1-weighted sequences. Lesions were classified as culprit and non-culprit according to imaging findings and patient clinical presentations. Univariate and multivariate analyses were performed to assess the difference between culprit and non-culprit lesions and complementary value of hrCMR in identifying higher risk lesions.

RESULTS

In total, 75 patients were included in this study. According to the morphology, lesions could be classified into five types: Type I, classical dissection (n = 50); Type II, fusiform aneurysm (n = 1); Type III, long dissected aneurysm (n = 3); Type IV, dolichoectatic dissecting aneurysm (n = 9) and Type V, saccular aneurysm (n = 12). Regression analyses showed that age and hypertension were both associated with culprit lesions (age: OR, 0.83; 95% CI 0.75-0.92; p < 0.001 and hypertension: OR, 66.62; 95% CI 5.91-751.11; p = 0.001). Hematoma identified by hrCMR was significantly associated with culprit lesions (OR, 16.80; 95% CI 1.01-280.81; p = 0.037). Moreover, 17 cases (16 lesions were judged to be culprit) were diagnosed as IAD but not visible in DSA and 15 were Type I lesion.

CONCLUSION

hrCMR is helpful in visualizing and characterizing IAD. It provides a significant complementary value over DSA for the diagnosis of IAD.

One-step evaluation of intraplaque hemorrhage in the carotid artery and vertebrobasilar artery using simultaneous non-contrast angiography and intraplaque hemorrhage

PURPOSE

To investigate the one-step detection of intraplaque hemorrhage (IPH) in the carotid artery (CA) and vertebrobasilar artery (VBA) using simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP).

METHODS

From January 2019 to March 2020, 1820 consecutive patients who visited our emergency room for evaluation of neurologic symptoms underwent brain MR imaging, including the SNAP sequence. SNAP imaging examined the coronal section from the CA to the VBA. IPH was defined as plaque in the CA and VBA with 200 % higher signal intensity on SNAP than in adjacent muscle in at least two consecutive slices.

RESULTS

Of these patients, 360 (19.8 %) had carotid plaque (both sides = 141, 39.2 %; single side = 219, 61.8 %). Of patients with carotid plaque, 185 (51.4 %) had IPH. Of 141 patients with plaques on both sides, 35 (24.8 %) had bilateral IPH. In total, 73 (4.0 %) patients had VBA IPH (30 with carotid plaque, 43 without carotid plaque). In addition, 18 (1.0 %) patients had carotid IPH and VBA IPH. Maximal wall thickness was significantly higher in the carotid IPH groups (4.5 ± 0.1 vs. 4.1 ± 0.1, p = 0.009). Prevalence of high grade stenosis (>70 %) was significantly higher in the carotid IPH group (17.5 % vs. 6.2 %, p < 0.001).

CONCLUSIONS

SNAP imaging can be evaluated with a one-step examination of CA and VBA IPH.

Association of Type 2 Diabetes Mellitus and Glycemic Control With Intracranial Plaque Characteristics in Patients With Acute Ischemic Stroke

BACKGROUND

Type 2 diabetes mellitus (T2DM) has shown to be associated with carotid plaque vulnerability. However, the impact of T2DM on intracranial artery atherosclerosis is not well-understood.

PURPOSE

To evaluate the association of diabetes and glycemic control with intracranial atherosclerotic plaque characteristics identified by three-dimensional contrast enhanced MR vessel wall imaging in patients after acute ischemic stroke.

STUDY TYPE

Prospective.

POPULATION

Two hundred and eighty-eight symptomatic patients with acute ischemic stroke due to intracranial atherosclerotic plaque.

FIELD STRENGTH/SEQUENCE

T₁ WI volume isotropic turbo spin-echo acquisition sequence at 3.0 T.

ASSESSMENT

Clinical profiles, blood biomarkers, the number of intracranial plaques, plaque enhanced score, and the features (location, luminal stenotic rate, intraplaque hemorrhage, length, burden, enhancement grade, and ratio) of culprit plaque (defined as the most stenotic lesion ipsilateral to the ischemic event) and nonculprit plaque were analyzed by three radiologists.

STATISTICAL TESTS

Analysis of variance (ANOVA), Shapiro-Wilk normality test, Levene's test, ANOVA with Bonferroni post-hoc test, Kruskal Wallis H test with subsequent pairwise comparisons, chi-square with Bonferroni post-hoc test, generalized linear regression, Pearson correlation test, Kendall's W and intra-class correlation coefficient.

RESULTS

Two hundred and twenty-five participants (age 60 ± 10 years, 58.7% male) with 958 intracranial plaques were included. More intracranial plaques were found in the T2DM group than the non-T2DM group (4.80 ± 2.22 vs. 3.60 ± 1.78, P < 0.05). Patients with poorly-controlled T2DM exhibited higher culprit plaque enhancement ratio than patients with well-controlled T2DM and non-T2DM (2.32 ± 0.61 vs. 1.60 ± 0.62 and 1.39 ± 0.39; respectively, P < 0.05). After adjusting for other clinical variables, T2DM was independently associated with increased intracranial plaque number (β = 0.269, P < 0.05), and HbA1c level was independently associated with culprit plaque enhancement ratio (β = 0.641, P < 0.05) in multivariate analysis.

DATA CONCLUSION

T2DM is associated with an increased intracranial plaque number. Higher HbA1c is associated with stronger plaque enhancement. 3D contrast enhanced MR vessel wall imaging may help better understand the association of T2DM and glycemic control with intracranial plaque.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 3.

Association of Hypertension With Both Occurrence and Outcome of Symptomatic Patients With Mild Intracranial Atherosclerotic Stenosis: A Prospective Higher Resolution Magnetic Resonance Imaging Study

Background

Intracranial atherosclerotic plaque causing mild luminal stenosis might lead to acute ischemic events. However, the difference between culprit and nonculprit lesions is unclear, as are the factors associated with favorable treatment outcomes.

Purpose

To quantify characteristics of intracranial atherosclerosis with mild luminal stenosis and to identify factors associated with lesion type (culprit or nonculprit) and with clinical outcomes.

Study Type

Prospective

Population

293 patients who had acute stroke with mild luminal stenosis (<50%) in the middle cerebral or basilar artery.

Field Strength/Sequence

3.0 T higher resolution magnetic resonance imaging (hrMRI) of intracranial arteries and whole brain MR images.

Assessment

Morphological and compositional analysis of plaques was performed. This included assessment of plaque volume, plaque burden, remodeling ratio, eccentricity, intraplaque hemorrhage, and enhancement ratio. Clinical outcomes were assessed according to the modified Rankin Scale (mRS) at day 90, with a favorable outcome being defined as a 90‐day mRS ≤2.

Statistical Tests

The odds ratios (ORs) with 95% confidence intervals (CIs) were calculated by a logistic regression model.

Results

Hypertension (OR 5.2; 95% CI 2.6–10.3; P < 0.05) and hrMRI enhancement ratio (OR 2.7; 95% CI 1.4–5.1; P < 0.05) were independently associated with lesion type. Patients without hypertension had significantly more (P < 0.05) favorable outcomes (124/144) than patients with hypertension (97/149). Most hypertensive patients without any previous blood pressure control (54/63) had a favorable outcome. However, these patients were significantly younger (P < 0.05) than those with adequate blood pressure control. After adjusting for all significant characteristics, hypertension duration (OR 1.19; 95% CI 1.09–1.29; P < 0.05), hypertension management (OR 2.49; 95% CI 1.18–5.26; P < 0.05), and enhancement ratio (OR 0.01; 95% CI 0.001–0.157; P < 0.05) were found to be independent high‐risk factors for outcome prediction.

Data Conclusion

hrMRI provided incremental value over traditional risk factors in identifying higher risk intracranial atherosclerosis with mild luminal stenosis.

Level of Evidence

2

Technical Efficacy

Stage 2

Progression of Plaque Burden of Intracranial Atherosclerotic Plaque Predicts Recurrent Stroke/Transient Ischemic Attack: A Pilot Follow-Up Study Using Higher-Resolution MRI

BACKGROUND

Patients with intracranial atherosclerotic disease (ICAD) have a high frequency of stroke recurrence. However, there has been little investigation into the prognostic value of higher-resolution magnetic resonance imaging (HR-MRI).

PURPOSE

To investigate the use of intracranial atherosclerotic plaques features in predicting risk of recurrent cerebrovascular ischemic events using HR-MRI.

STUDY TYPE

Prospective.

POPULATION

Fifty-eight patients with acute/subacute stroke (N = 46) or transient ischemic attack (N = 12).

FIELD STRENGTH/SEQUENCE

A 3.0 T, 3D time-of-flight gradient echo sequence and T1- and T2-weighted fast spin echo sequences with 0.31 x 0.39 mm² in-plane resolution, twice (with >3 months between scans) following the initial event.

ASSESSMENT

Patients were also followed clinically for recurrent ischemic events for up to 48 months or until a subsequent event occurred. The degree of stenosis, plaque burden (PB), minimal lumen area (MLA), and contrast enhancement ratio were assessed at each scanning session and the percentage change of each over time was calculated.

STATISTICAL TESTS

Univariable and multivariable Cox regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for predicting recurrent events.

RESULTS

The mean time interval between baseline and follow-up MRI scans was 6.2 ± 4.1 months. After the second MRI scan, 20.7% of patients (N = 12) had experienced ipsilateral recurrent TIA/stroke within 10.9 ± 9.2 months. Univariable analyses showed that baseline triglyceride, percentage change of PB, and progression of PB were significantly associated with recurrent events (all P < 0.05). Multivariable Cox regression indicated that progression of PB (HR, 6.293; 95% CI, 1.620-24.444; P < 0.05) was a significant independent imaging feature for recurrent ischemic events.

DATA CONCLUSION

Progression of PB was independently associated with recurrent ischemic cerebrovascular events. HR-MRI may help risk stratification of patients at risk of recurrent stroke.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 4.

Identification of high-risk carotid plaque with MRI-based radiomics and machine learning

OBJECTIVES

We sought to build a high-risk plaque MRI-based model (HRPMM) using radiomics features and machine learning for differentiating symptomatic from asymptomatic carotid plaques.

MATERIALS AND METHODS

One hundred sixty-two patients with carotid stenosis were randomly divided into training and test cohorts. Multi-contrast MRI including time of flight (TOF), T1- and T2-weighted imaging, and contrast-enhanced imaging was done. Radiological characteristics of the carotid plaques were recorded and calculated to build a traditional model. After extracting the radiomics features on these images, we constructed HRPMM with least absolute shrinkage and selection operator algorithm in the training cohort and evaluated its performance in the test cohort. A combined model was also built using both the traditional and radiomics features. The performance of all the models in the identification of high-risk carotid plaque was compared.

RESULTS

Intraplaque hemorrhage and lipid-rich necrotic core were independently associated with clinical symptoms and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.825 versus 0.804 in the training and test cohorts. The HRPMM and the combined model achieved an AUC of 0.988 versus 0.984 and of 0.989 versus 0.986 respectively in the two cohorts. Both the radiomics model and combined model outperformed the traditional model, whereas the combined model showed no significant difference with the HRPMM.

CONCLUSIONS

Our MRI-based radiomics model can accurately distinguish symptomatic from asymptomatic carotid plaques. It is superior to the traditional model in the identification of high-risk plaques.

KEY POINTS

• Carotid plaque multi-contrast MRI stores other valuable information to be further exploited by radiomics analysis. • Radiomics analysis can accurately distinguish symptomatic from asymptomatic carotid plaques. • The radiomics model is superior to the traditional model in the identification of high-risk plaques.

Imaging endpoints of intracranial atherosclerosis using vessel wall MR imaging: a systematic review

PURPOSE

The vessel wall MR imaging (VWI) literature was systematically reviewed to assess the criteria and measurement methods of VWI-related imaging endpoints for symptomatic intracranial plaque in patients with ischemic events.

METHODS

PubMed, Scopus, Web of Science, EMBASE, and Cochrane databases were searched up to October 2019. Two independent reviewers extracted data from 47 studies. A modified Guideline for Reporting Reliability and Agreement Studies was used to assess completeness of reporting.

RESULTS

The specific VWI-pulse sequence used to identify plaque was reported in 51% of studies. A VWI-based criterion to define plaque was reported in 38% of studies. A definition for culprit plaque was reported in 40% of studies. Frequently scored qualitative imaging endpoints were plaque quadrant (21%) and enhancement (21%). Frequently measured quantitative imaging endpoints were stenosis (19%), lumen area (15%), and remodeling index (14%). Reproducibility for all endpoints ranged from good to excellent (range: ICC_{T1 hyperintensity} = 0.451 to ICC_stenosis = 0.983). However, rater specialty and years of experience varied among studies.

CONCLUSIONS

Investigators are using different criteria to identify and measure VWI-imaging endpoints for culprit intracranial plaque. Early awareness of these differences to address methods of acquisition and measurement will help focus research resources and efforts in technique optimization and measurement reproducibility. Consensual definitions to detect plaque will be important to develop automatic lesion detection tools particularly in the era of radiomics.

Vessel wall MR imaging of intracranial atherosclerosis

Intracranial atherosclerotic disease (ICAD) is one of the most common causes of ischemic stroke worldwide. Along with high recurrent stroke risk from ICAD, its association with cognitive decline and dementia leads to a substantial decrease in quality of life and a high economic burden. Atherosclerotic lesions can range from slight wall thickening with plaques that are angiographically occult to severely stenotic lesions. Recent advances in intracranial high resolution vessel wall MR (VW-MR) imaging have enabled imaging beyond the lumen to characterize the vessel wall and its pathology. This technique has opened new avenues of research for identifying vulnerable plaque in the setting of acute ischemic stroke as well as assessing ICAD burden and its associations with its sequela, such as dementia. We now understand more about the intracranial arterial wall, its ability to remodel with disease and how we can use VW-MR to identify angiographically occult lesions and assess medical treatment responses, for example, to statin therapy. Our growing understanding of ICAD with intracranial VW-MR imaging can profoundly impact diagnosis, therapy, and prognosis for ischemic stroke with the possibility of lesion-based risk models to tailor and personalize treatment. In this review, we discuss the advantages of intracranial VW-MR imaging for ICAD, the potential of bioimaging markers to identify vulnerable intracranial plaque, and future directions of artificial intelligence and its utility for lesion scoring and assessment.

Quantitative Histogram Analysis on Intracranial Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging Study

BACKGROUND AND PURPOSE

Intracranial atherosclerosis is one of the main causes of stroke, and high-resolution magnetic resonance imaging provides useful imaging biomarkers related to the risk of ischemic events. This study aims to evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using high-resolution magnetic resonance imaging.

METHODS

Two hundred forty-seven patients with intracranial atherosclerosis who underwent high-resolution magnetic resonance imaging sequentially between January 2015 and December 2016 were recruited. Quantitative features, including stenosis, plaque burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and dispersion of signal intensity (coefficient of variation), were analyzed based on T2-, T1-, and contrast-enhanced T1-weighted images. Step-wise regression analysis was used to identify key determinates differentiating culprit and nonculprit plaques and to calculate the odds ratios (ORs) with 95% CIs.

RESULTS

In total, 190 plaques were identified, of which 88 plaques (37 culprit and 51 nonculprit) were located in the middle cerebral artery and 102 (57 culprit and 45 nonculprit) in the basilar artery. Nearly 90% of culprit lesions had a degree of luminal stenosis of <70%. Multiple logistic regression analyses showed that intraplaque hemorrhage (OR, 16.294 [95% CI, 1.043-254.632]; P=0.047), minimum luminal area (OR, 1.468 [95% CI, 1.032-2.087]; P=0.033), and coefficient of variation (OR, 13.425 [95% CI, 3.987-45.204]; P<0.001) were 3 significant features in defining culprit plaques in middle cerebral artery. The enhancement ratio (OR, 9.476 [95% CI, 1.256-71.464]; P=0.029), intraplaque hemorrhage (OR, 2.847 [95% CI, 0.971-10.203]; P=0.046), and coefficient of variation (OR, 10.068 [95% CI, 2.820-21.343]; P<0.001) were significantly associated with plaque type in basilar artery. Coefficient of variation was a strong independent predictor in defining plaque type for both middle cerebral artery and basilar artery with sensitivity, specificity, and accuracy being 0.79, 0.80, and 0.80, respectively.

CONCLUSIONS

Features characterized by high-resolution magnetic resonance imaging provided complementary values over luminal stenosis in defined lesion type for intracranial atherosclerosis; the dispersion of signal intensity in histogram analysis was a particularly effective predictive parameter.

Radiomics versus Visual and Histogram-based Assessment to Identify Atheromatous Lesions at Coronary CT Angiography: An ex Vivo Study

Background Visual and histogram-based assessments of coronary CT angiography have limited accuracy in the identification of advanced lesions. Radiomics-based machine learning (ML) could provide a more accurate tool. Purpose To compare the diagnostic performance of radiomics-based ML with that of visual and histogram-based assessment of ex vivo coronary CT angiography cross sections to identify advanced atherosclerotic lesions defined with histologic examination. Materials and Methods In this prospective study, 21 coronary arteries from seven hearts obtained from male donors (mean age, 52.3 years ± 5.3) were imaged ex vivo with coronary CT angiography between February 23, 2009, and July 31, 2010. From 95 coronary plaques, 611 histologic cross sections were coregistered with coronary CT cross sections. Lesions were considered advanced if early fibroatheroma, late fibroatheroma, or thin-cap atheroma was present. CT cross sections were classified as showing homogeneous, heterogeneous, or napkin-ring sign plaques on the basis of visual assessment. The area of low attenuation (<30 HU) and the average Hounsfield unit were quantified. Radiomic parameters were extracted and used as inputs to ML algorithms. Eight radiomics-based ML models were trained on randomly selected cross sections (training set, 75% of the cross sections) to identify advanced lesions. Visual assessment, histogram-based assessment, and the best ML model were compared on the remaining 25% of the data (validation set) by using the area under the receiver operating characteristic curve (AUC) to identify advanced lesions. Results After excluding sections with no visible plaque (n = 134) and with heavy calcium (n = 32), 445 cross sections were analyzed. Of those 445 cross sections, 134 (30.1%) were advanced lesions. Visual assessment of the 445 cross sections indicated that 207 (46.5%) were homogeneous, 200 (44.9%) were heterogeneous, and 38 (8.5%) demonstrated the napkin-ring sign. A radiomics-based ML model incorporating 13 parameters outperformed visual assessment (AUC = 0.73 with 95% confidence interval [CI] of 0.63, 0.84 vs 0.65 with 95% CI of 0.56, 0.73, respectively; P = .04), area of low attenuation (AUC = 0.55 with 95% CI of 0.42, 0.68; P = .01), and average Hounsfield unit (AUC = 0.53 with 95% CI of 0.42, 0.65; P = .004) in the identification of advanced atheromatous lesions. Conclusion Radiomics-based machine learning analysis improves the discriminatory power of coronary CT angiography in the identification of advanced atherosclerotic lesions. Published under a CC BY 4.0 license.

Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement

OBJECTIVES

To evaluate radiomics studies according to radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to provide objective measurement of radiomics research.

MATERIALS AND METHODS

PubMed and Embase were searched for studies published in high clinical imaging journals until December 2018 using the terms "radiomics" and "radiogenomics." Studies were scored against the items in the RQS and TRIPOD guidelines. Subgroup analyses were performed for journal type (clinical vs. imaging), intended use (diagnostic vs. prognostic), and imaging modality (CT vs. MRI), and articles were compared using Fisher's exact test and Mann-Whitney analysis.

RESULTS

Seventy-seven articles were included. The mean RQS score was 26.1% of the maximum (9.4 out of 36). The RQS was low in demonstration of clinical utility (19.5%), test-retest analysis (6.5%), prospective study (3.9%), and open science (3.9%). None of the studies conducted a phantom or cost-effectiveness analysis. The adherence rate for TRIPOD was 57.8% (mean) and was particularly low in reporting title (2.6%), stating study objective in abstract and introduction (7.8% and 16.9%), blind assessment of outcome (14.3%), sample size (6.5%), and missing data (11.7%) categories. Studies in clinical journals scored higher and more frequently adopted external validation than imaging journals.

CONCLUSIONS

The overall scientific quality and reporting of radiomics studies is insufficient. Scientific improvements need to be made to feature reproducibility, analysis of clinical utility, and open science categories. Reporting of study objectives, blind assessment, sample size, and missing data is deemed to be necessary.

KEY POINTS

• The overall scientific quality and reporting of radiomics studies is insufficient. • The RQS was low in demonstration of clinical utility, test-retest analysis, prospective study, and open science. • Room for improvement was shown in TRIPOD in stating study objective in abstract and introduction, blind assessment of outcome, sample size, and missing data categories.

Culprit intracranial plaque without substantial stenosis in acute ischemic stroke on vessel wall MRI: A systematic review

BACKGROUND AND AIMS

Intracranial atherosclerotic plaque is associated with ischemic strokes without substantial stenosis, and needs better characterization. We aim to investigate the clinical significance of intracranial plaque without substantial stenosis by high resolution vessel wall MRI (vwMRI) through a systematic review of existing studies.

METHODS

Studies investigating intracranial arterial atherosclerotic plaques without substantial stenosis in acute ischemic stroke patients using vwMRI were systematically identified by searching the PubMed and Medline database and article reference lists. Study characteristics were recorded, the methodological quality of eligible studies was assessed, relevant clinical data were extracted, and collective data was analyzed.

RESULTS

Twenty-one studies were identified as eligible. 463 patients were included without stenosis of the intracranial arteries, and 651 patients were included with stenosis <50%. The prevalence of intracranial plaque revealed by vwMRI among acute/subacute ischemic stroke patients with non-stenotic Magnetic Resonance Angiography (MRA) was 50.6% (95% confidence interval (CI), 46.1%-55.1%). The prevalence of <50% MRA stenotic culprit plaque among acute/subacute ischemic stroke patients with a clinical diagnosis of intracranial atherosclerosis was 51.2% (95% CI, 38.4%-64.0%). Plaques features, including wall enhancement, positive remodeling, intraplaque hemorrhage, plaque location and eccentricity, were associated with acute stroke, progressive motor deficits and unfavorable overall functional outcomes.

CONCLUSIONS

Intracranial high-risk plaque with zero or mild degree of stenosis is more prevalent than previously acknowledged, and is associated with ischemic stroke and unfavorable outcome. VwMRI can identify the high-risk plaque features, which may act as a promising tool to better risk stratify these patients.

Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis

PURPOSE

To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant haemangiopericytoma (HPC) from angiomatous meningioma (AM).

MATERIALS AND METHODS

Sixty-seven pathologically confirmed cases, including 24 malignant HPCs and 43 AMs between May 2013 and September 2017 were retrospectively reviewed. In each case, 498 radiomic features, including 12 clinical features and 486 texture features from MRI sequences (T2-FLAIR, DWI and enhanced T1WI), were extracted. Three neuroradiologists independently made diagnoses by vision. Four Support Vector Machine (SVM) classifiers were built, one based on clinical features and three based on texture features from three MRI sequences after feature selection. The diagnostic abilities of these classifiers and three neuroradiologists were evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Malignant HPCs were found to have larger sizes, slighter degrees of peritumoural oedema compared with AMs (P<0.05), and more serpentine-like vessels. The AUC of the enhanced T1WI-based classifier was 0.90, significantly higher than that of T2-FLAIR-based or DWI-based classifiers (0.77 and 0.73). The AUC of the SVM classifier based on clinical features was 0.66, slightly but not significantly lower than the performances of 3 neuroradiologists (AUC=0.69, 0.70 and 0.73).

CONCLUSION

Machine-learning models based on clinical features alone could not provide a better diagnostic performance than that of radiologists. The SVM classifier built by texture features extracted from enhanced T1WI is a promising tool to differentiate malignant HPC from AM before surgery.

Evaluation of the distribution and progression of intraluminal thrombus in abdominal aortic aneurysms using high-resolution MRI

BACKGROUND

Intraluminal thrombus (ILT) signal intensity on MRI has been studied as a potential marker of abdominal aortic aneurysm (AAA) progression.

PURPOSE

1) To characterize the relationship between ILT signal intensity and AAA diameter; 2) to evaluate ILT change over time; and 3) to assess the relationship between ILT features and AAA growth.

STUDY TYPE

Prospective.

SUBJECTS

Eighty AAA patients were imaged, and a subset (n = 41) were followed with repeated MRI for 16 ± 9 months.

FIELD STRENGTH/SEQUENCE

3D black-blood fast-spin-echo sequence at 3 T.

ASSESSMENT

ILT was designated as "bright" if the signal was greater than 1.2 times that of adjacent psoas muscle. AAAs were divided into three groups based on ILT: Type 1: bright ILT; Type 2: isointense ILT; Type 3: no ILT. During follow-up, an active ILT change was defined as new ILT formation or an increase in ILT signal intensity to bright; stable ILT was defined as no change in ILT type or ILT became isointense from bright previously.

STATISTICAL TESTS

Shapiro-Wilk test; Mann-Whitney U-test; Fisher's exact test; Kruskal-Wallis test; Spearman's r; intraclass correlation coefficient (ICC), Cohen's kappa.

RESULTS

AAAs with Type 1 ILT were larger than those with Types 2 and 3 ILT (5.1 ± 1.1 cm, 4.4 ± 0.9 cm, 4.2 ± 0.8 cm, P = 0.008). The growth rate of AAAs with Type 1 ILT was significantly greater than that of AAAs with Types 2 and 3 ILT (2.6 ± 2.5, 0.6 ± 1.3, 1.5 ± 0.6 mm/year, P = 0.01). During follow-up, AAAs with active ILT changes had a 3-fold increased growth rate compared with AAAs with stable ILT (3.6 ± 3.0 mm/year vs. 1.2 ± 1.5 mm/year, P = 0.008).

DATA CONCLUSION

AAAs with bright ILT are larger in diameter and grow faster. Active ILT change is associated with faster AAA growth. Black-blood MRI can characterize ILT features and monitor their change over time, which may provide new insights into AAA risk assessment.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:994-1001.

Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation

Background: Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly. The present study was performed to noninvasively predict tumor consistency based on magnetic resonance imaging and radiomics analysis. Methods: In total, 158 patients with acromegaly were randomized into the primary cohort (n = 100) and validation cohort (n = 58). The consistency of the tumor was classified as soft or firm according to the neurosurgeon's evaluation. The critical radiomics features were determined using the elastic net feature selection algorithm, and the radiomics signature was constructed. The most valuable clinical characteristics were then selected based on the multivariable logistic regression analysis. Next, a radiomics model was developed using the radiomics signature and clinical characteristics, and 30 patients with acromegaly were recruited for multicenter validation of the radiomics model. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, and other associated classification measures. Its calibration, discriminating capacity, and clinical usefulness were also evaluated. Results: The radiomics signature established according to four radiomics features screened in the primary cohort exhibited excellent discriminatory capacity in the validation cohort. The radiomics model, which incorporated both the radiomics signature and Knosp grade, displayed favorable discriminatory capacity and calibration, and the AUC was 0.83 (95% confidence interval, 0.81-0.85) and 0.81 (95% confidence interval, 0.78-0.83) in the primary and validation cohorts, respectively. Furthermore, compared with the clinical characteristics, the as-constructed radiomics model is more effective in prediction of the tumor consistency in patients with acromegaly. Moreover, the multicenter validation and decision curve analysis suggested that the radiomics model was clinically useful. Conclusions: This radiomics model can assist neurosurgeons in predicting tumor consistency in patients with acromegaly before surgery and facilitates the determination of individualized therapeutic schemes.