MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study.

OBJECTIVES: Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence. METHODS: A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort's support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set. RESULTS: For contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set. CONCLUSIONS: This study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans. CLINICAL RELEVANCE STATEMENT: We propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.

Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes.

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

A novel score for early prediction of urinary tract infection risk in patients with acute ischemic stroke: a nomogram-based retrospective cohort study.

This study aimed to construct and externally validate a user-friendly nomogram-based scoring model for predicting the risk of urinary tract infections (UTIs) in patients with acute ischemic stroke (AIS). A retrospective real-world cohort study was conducted on 1748 consecutive hospitalized patients with AIS. Out of these patients, a total of 1132 participants were ultimately included in the final analysis, with 817 used for model construction and 315 utilized for external validation. Multivariate regression analysis was applied to develop the model. The discriminative capacity, calibration ability, and clinical effectiveness of the model were evaluated. The overall incidence of UTIs was 8.13% (92/1132), with Escherichia coli being the most prevalent causative pathogen in patients with AIS. After multivariable analysis, advanced age, female gender, National Institute of Health Stroke Scale (NIHSS) score ≥ 5, and use of urinary catheters were identified as independent risk factors for UTIs. A nomogram-based SUNA model was constructed using these four factors (Area under the receiver operating characteristic curve (AUC) = 0.810), which showed good discrimination (AUC = 0.788), calibration, and clinical utility in the external validation cohort. Based on four simple and readily available factors, we derived and externally validated a novel and user-friendly nomogram-based scoring model (SUNA score) to predict the risk of UTIs in patients with AIS. The model has a good predictive value and provides valuable information for timely intervention in patients with AIS to reduce the occurrence of UTIs.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set. MATERIALS AND METHODS: Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. RESULTS: 31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. CONCLUSION: The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach.

BACKGROUND: Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. PURPOSE: This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. METHODS: We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). RESULTS: The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. CONCLUSION: The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks.

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.

Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model.

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure.

The Value of the Cardiac Magnetic Resonance Intravoxel Incoherent Motion Technique in Evaluating Microcirculatory Dysfunction in Hypertrophic Cardiomyopathy.

Objective: To evaluate the value of the cardiac magnetic resonance intravoxel incoherent motion (IVIM) technique in microcirculatory dysfunction in patients with hypertrophic cardiomyopathy (HCM). Methods: The medical records of 19 patients with HCM in our hospital from January 2020 to May 2021 were collected retrospectively, and 23 healthy people with a similar age and gender distribution to the patients with HCM were included as controls. All the included subjects underwent clinical assessment and cardiac magnetic resonance imaging. The original IVIM images were analysed, and the imaging parameters of each segment were measured. The HCM group was divided into non-hypertrophic myocardium and hypertrophic myocardium groups. The differences in imaging parameters between the normal and HCM groups were compared. A Spearman correlation analysis was used to explore the correlation between end-diastolic thickness (EDTH) and each IVIM parameter. Results: The D∗ and f values in the HCM group were lower than those in the normal group (p < 0.0001 and p = 0.004, respectively). The f, D, D∗, and EDTH values of the hypertrophic segment, non-hypertrophic segment, and normal groups were statistically significant (p < 0.05). The difference in D∗ values among the mild, moderate, severe, and very severe HCM groups was statistically significant (p < 0.05). There was a statistically significant difference in EDTH among the mild, moderate, severe, and very severe groups (p < 0.001). There were significant differences in the values of D, D∗, and f between the non-delayed enhancement group and the delayed enhancement group (p < 0.05). The EDTH values of 304 segments in the HCM group were negatively correlated with f (r = -0.219, p = 0.028) and D∗ values (r = -0.310, p < 0.001). Conclusion: The use of IVIM technology can achieve a non-invasive early quantitative assessment of microvascular disease in HCM without the injection of a contrast agent and provide a reference for the early diagnosis of and intervention in myocardial ischemia in patients with HCM.

A region-specific modulation of sleep slow waves on interictal epilepsy markers in focal epilepsy.

OBJECTIVE: Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy. METHODS: Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording. Scalp SSWs and intracranial spikes and ripples (80-250 Hz) were detected; ripples were classified as type I (co-occurring with spikes) or type II (occurring alone). The Hilbert transform was used to analyze the distributions of spikes and ripples in the phases of SSWs. RESULTS: Thirty patients with discrete seizure-onset zone (SOZ) and discernable sleep architecture were included. Intracranial spikes and ripples accumulated around the negative peaks of SSWs and increased with SSW amplitude. Phase analysis revealed that spikes and both ripple subtypes in SOZ were similarly facilitated by SSWs exclusively during down state. In exclusively irritative zones outside SOZ (EIZ), SSWs facilitated spikes and type I ripples across a wider range of phases and to a greater extent than those in SOZ. The type II and type I ripples in EIZ were modulated by SSWs in different patterns. Ripples in normal zones decreased specifically during the up-to-down transition and then increased after the negative peak of SSW, with a characteristically high post-/pre-negative peak ratio. SIGNIFICANCE: SSWs modulate interictal events in an amplitude-dependent and region-specific pattern. Pathological ripples and spikes were facilitated predominantly during the cortical down state. Coupling analysis of SSWs could improve the discrimination of pathological and physiological ripples and facilitate seizure localization.

Preparation and properties of fucoxanthin-loaded liposomes stabilized by sea cucumber derived cholesterol sulfate instead of cholesterol.

The preparation of steady-state phospholipid liposomes requires cholesterol as a stabilizer, but excessive intake of cholesterol may increase the risk of cardiovascular disease. The sulfated sterols extracted from sea cucumber, mainly including sulfated 24-methylene cholesterol and cholesterol sulfate, have been reported to have a variety of physiological activities. Sulfated sterols are similar to cholesterol in structure and have the potential to replace cholesterol to prepare novel stable multifunctional liposomes, allowing the liposomes to act as carriers for the delivery of less bioavailable nutrients while allowing sulfated sterols in the lipid bilayer to exert physiologically active effects. This study aimed to prepare a novel multifunctional nanoliposome stabilized with sulfated sterols from sea cucumber instead of cholesterol by ultrasound-assisted thin-film dispersion method. The results showed that stable and uniformly dispersed nanoliposomes could be formed when the substitution ratio of sea cucumber-derived cholesterol sulfate was 100% and the ratio of lecithin to cholesterol sulfate was 3:1. Fucoxanthin encapsulated liposome with egg yolk lecithin/sea cucumber-derived cholesterol sulfate/fucoxanthin mass ratio of 6:2:3 was successfully prepared, with an average particle size of 214 ± 3 nm, polydispersity index (PDI) value of 0.297 ± 0.006, the zeta potential of -57.2 ± 1.10 mV, and the encapsulation efficiency of 85.5 ± 0.8%. The results of digestion and absorption in vitro and in vivo showed that liposomes could significantly improve the bioavailability of fucoxanthin and prolong its residence time in serum. As an efficient multifunctional carrier, this novel liposome has great potential for applications in functional foods and biomedicine.

CIABNet: Category imbalance attention block network for the classification of multi-differentiated types of esophageal cancer.

BACKGROUND: Esophageal cancer has become one of the important cancers that seriously threaten human life and health, and its incidence and mortality rate are still among the top malignant tumors. Histopathological image analysis is the gold standard for diagnosing different differentiation types of esophageal cancer. PURPOSE: The grading accuracy and interpretability of the auxiliary diagnostic model for esophageal cancer are seriously affected by small interclass differences, imbalanced data distribution, and poor model interpretability. Therefore, we focused on developing the category imbalance attention block network (CIABNet) model to try to solve the previous problems. METHODS: First, the quantitative metrics and model visualization results are integrated to transfer knowledge from the source domain images to better identify the regions of interest (ROI) in the target domain of esophageal cancer. Second, in order to pay attention to the subtle interclass differences, we propose the concatenate fusion attention block, which can focus on the contextual local feature relationships and the changes of channel attention weights among different regions simultaneously. Third, we proposed a category imbalance attention module, which treats each esophageal cancer differentiation class fairly based on aggregating different intensity information at multiple scales and explores more representative regional features for each class, which effectively mitigates the negative impact of category imbalance. Finally, we use feature map visualization to focus on interpreting whether the ROIs are the same or similar between the model and pathologists, thus better improving the interpretability of the model. RESULTS: The experimental results show that the CIABNet model outperforms other state-of-the-art models, which achieves the most advanced results in classifying the differentiation types of esophageal cancer with an average classification accuracy of 92.24%, an average precision of 93.52%, an average recall of 90.31%, an average F1 value of 91.73%, and an average AUC value of 97.43%. In addition, the CIABNet model has essentially similar or identical to the ROI of pathologists in identifying histopathological images of esophageal cancer. CONCLUSIONS: Our experimental results prove that our proposed computer-aided diagnostic algorithm shows great potential in histopathological images of multi-differentiated types of esophageal cancer.

A rare case report of primary Sjögren's syndrome with clinical characteristics similar to those of CLIPPERS.

BACKGROUND: Primary Sjögren's syndrome (pSS) is an autoimmune inflammatory disease characterized by dryness of the eyes, mouth and other mucous membranes. Patients with pSS can also present with extraglandular manifestations, such as pulmonary, kidney and nervous system involvement. Central nervous system (CNS) manifestations have rarely been described in pSS. CASE PRESENTATION: A 33-year-old man was admitted with a one-month history of dizziness, speech disturbance, and walking instability. His brain enhanced magnetic resonance imaging (MRI) showed symmetrical, enhanced "salt-and-pepper-like" speckled lesions in the brainstem, basal ganglia, and subcortical regions, and his diagnosis was considered possible chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids (CLIPPERS). Further examination revealed that anti-SSA antibody was positive, and the Schirmer test and labial salivary gland histopathology were abnormal, which supported the diagnosis of pSS. CONCLUSION: pSS is a chronic systemic autoimmune disease that involves neurological complications. This case suggests that CNS lesions of pSS can present with clinical and MRI findings similar to those of CLIPPERS.

The Role of the Three-Dimensional Edge-Enhancing Gradient Echo Sequence at 3T MRI in the Detection of Focal Cortical Dysplasia: A Technical Case Report and Literature Review.

INTRODUCTION: Focal cortical dysplasia (FCD) is a most common cause of intractable focal epilepsy in children. Surgery is considered as a radical option for such patients with the prerequisite of lesion detection. Magnetic resonance imaging (MRI) plays a significant role in detection of FCDs in epilepsy patients; however, the detection of FCDs even in epilepsy dedicated MRI sequence shows relatively low positive rate. Last year, Middlebrooks et al introduced the novel three-dimensional Edge-Enhancing Gradient Echo (3D-EDGE) MRI sequence and using this sequence successfully identified five cases of FCDs which indicates its potential role in those epilepsy patients who may have FCDs. CASE PRESENTATION: We present a 14-year-old, right-handed, male patient who has suffered from drug-resistant epilepsy over the past 3 years. It was unable to localize the lesion of the seizure, even using the series of epilepsy dedicated MRI sequences. Inspired by the previous report, the lesion of the seizure was successfully targeted by 3D-EDGE sequence. Combined with intraoperative navigation and precisely removed the lesion. He was uneventfully recovered with no signs of cerebral dysfunction and no seizure recurrence 8 months after surgery. CONCLUSION: The 3D-EDGE sequences show a higher sensitivity for FCD detection in epilepsy patients compared with a series of epilepsy-dedicated MRI protocols. We confirmed that the study by Middlebrooks et al is of great clinical value. If the findings on routine MRI sequences or even epilepsy-dedicated MRI sequences were reported as negative, however, the semiology, video-electroencephalography, and fluorodeoxyglucose-positron emission tomography results suggest a local abnormality, and the results are concordant with each other, a 3D-EDGE sequence may be a good option.

A Study of Spontaneous Brain Activity on Resting-State Functional Magnetic Resonance Imaging in Adults with MRI-Negative Temporal Lobe Epilepsy.

Purpose: Patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE-N) represent an important subgroup of temporal lobe epilepsy (TLE). Here, we aimed to combine three voxel-based local brain area analysis methods of resting-state functional MRI (rs-fMRI), to examine the TLE-N patients' resting brain function based on neural synchronization and intensity of local brain areas. Methods: The study included 47 patients with TLE, including 28 cases of drug-controlled TLE (cTLE-N) and 19 cases of drug-resistant TLE-N (rTLE-N), as well as 30 participants in the healthy control (HC) group. To comprehensively assess the altered brain function associated with TLE-N patients, we analyzed three data-driven rs-fMRI algorithms for amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF) and regional homogeneity (ReHo). Results: Compared to the HC group, the distribution of abnormal functional brain areas in cTLE-N patients was dominated by occipital lobe activation, as measured by increased fALFF values in the superior occipital gyrus (SOG) and increased ReHo values in the lingual gyrus (Lin), fusiform gyrus, and middle occipital gyrus. Patients with rTLE-N exhibited a diffuse distribution of abnormal functional brain areas, showing increased fALFF values in the SOG, Lin, superior temporal gyrus, and postcentral gyrus, and decreased fALFF values in the inferior frontal gyrus orbital, parahippocampal gyrus, and superior frontal gyrus orbital. The ReHo values were reduced in the orbital region of the middle frontal gyrus, the precuneus, and the parietal inferior angular gyrus; while ReHo values were elevated values in several frontal, temporal, occipital, and subcortical brain areas. Conclusion: Patients with rTLE-N have local brain activity changes in the prefrontal limbic system and default model network dysfunction, while cTLE-N patients have local brain activity changes in the visual functional areas. Different epilepsy networks exist between cTLE-N and rTLE-N.

The DNMT1-PAS1-PH20 axis drives breast cancer growth and metastasis.

PH20 is a member of the human hyaluronidase family that degrades hyaluronan in the extracellular matrix and controls tumor progression. Inhibition of DNA methyltransferases (DNMTs) leads to elevated hyaluronan levels; however, whether DNMT inhibitors control PH20 remains unclear. Here, we report that the DNMT1 inhibitor, decitabine, suppresses PH20 expression by activating the long non-coding RNA PHACTR2-AS1 (PAS1). PAS1 forms a tripartite complex with the RNA-binding protein vigilin and histone methyltransferase SUV39H1. The interaction between PAS1 and vigilin maintains the stability of PAS1. Meanwhile, PAS1 recruits SUV39H1 to trigger the H3K9 methylation of PH20, resulting in its silencing. Functionally, PAS1 inhibits breast cancer growth and metastasis, at least partially, by suppressing PH20. Combination therapy of decitabine and PAS1-30nt-RNA, which directly binds to SUV39H1, effectively blocked breast cancer growth and metastasis in mice. Taken together, DNMT1, PAS1, and PH20 comprise a regulatory axis to control breast cancer growth and metastasis. These findings reveal that the DNMT1-PAS1-PH20 axis is a potential therapeutic target for breast cancer.

Predictive power of high-resolution vessel wall magnetic resonance imaging in ischemic stroke.

BACKGROUND AND OBJECTIVE: Intracranial atherosclerotic disease (ICAD) is a key contributor to ischemic stroke and has a high recurrence rate. This study aimed to investigate the function of high-resolution vessel wall MRI (HR-VW-MRI) and evaluate plaque characteristics in patients with ICAD. METHODS: A consecutive series of patients with ICAD who underwent HR-VW-MRI were enrolled, and imaging measurements were acquired. Baseline clinical characteristics were identified. Telephone follow-up was conducted every three months. The endpoint events were the first onset or recurrence of ischemic stroke and new clinical vascular events. Patients were divided into groups with or without events according to whether the endpoint event occurred. RESULTS: A total of 70 patients (mean age = 57.6 years old) were enrolled. The median follow-up duration was 182 days. During the follow-up, 10 patients developed ischemic stroke, experienced endpoint events, and were found with 44 plaques in the artery area. A total of 169 plaques were further found in 70 patients. There were significant differences in EI, HST1, surface features, and WA reference between the two groups (P < 0.05). Logistic analysis showed that grade 2 enhancements, stenosis degree ≥ 50%, HST1, and surface features were independent prognostic factors of the onset of stroke, caused by ICAD. CONCLUSION: This prospective study demonstrates that HR-VW-MRI can identify atherosclerotic plaques in the cerebral artery and high-risk plaques, which may contribute to the prevention of ICAD and guide clinical treatment.