Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.

PURPOSE: This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning-based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images. MATERIALS AND METHODS: In total, 148 patients with locally advanced rectal cancer(T2-4 or N+) who underwent MR imaging at baseline and after chemoradiotherapy between January 2010 and May 2021 were included. A region of interest for each tumor mass was drawn by a radiologist on oblique axial T2-weighted images, and main features were selected using principal component analysis after dimension reduction among 116 radiomics and three clinical features. Among eight learning models that were used for prediction model development, the model showing best performance was selected. Treatment responses were classified as either good or poor based on the MR-assessed TRG (mrTRG) and pathologic TRG (pTRG). The model performance was assessed using the area under the receiver operating curve (AUROC) to classify the response group. RESULTS: Approximately 49% of the patients were in the good response (GR) group based on mrTRG (73/148) and 26.9% based on pTRG (28/104). The AUCs of clinical data, radiomics models, and combined radiomics with clinical data model for predicting mrTRG were 0.80 (95% confidence interval [CI] 0.73, 0.87), 0.74 (95% CI 0.66, 0.81), and 0.75(95% CI 0.68, 0.82), and those for predicting pTRG was 0.62 (95% CI 0.52, 0.71), 0.74 (95% CI 0.65, 0.82), and 0.79 (95% CI 0.71, 0.87). CONCLUSION: Radiomics combined with clinical data model using baseline T2-weighted MR images demonstrated feasible diagnostic performance in predicting both MR-assessed and pathologic treatment response in patients with rectal cancer after NCRT.

National trends in physical activity among adolescents in South Korea before and during the COVID-19 pandemic, 2009-2021.

With the ongoing COVID-19 pandemic, several previous studies from different countries showed that physical activity (PA) decreased during the COVID-19 outbreak. However, few studies have examined the recent tendency of PA in the adolescent population. Thus, we aimed to investigate the long-term trend of PA in Korean youth and the prevalence changes between before and during the COVID-19 pandemic. Data from Korea Youth Risk Behavior Web-Based Survey (KYRBS) was collected for consecutive years between 2009 and 2021. The period was separated into prepandemic (2009-2019), early-pandemic (2020), and mid-pandemic (2021). Self-reported amount of PA was categorized into four groups (insufficient, aerobic, muscle strengthening, and both physical activities) according to World Health Organization (WHO) PA guidelines. A total of 840 488 adolescents aged 12-18 who fully responded to the survey were selected (response rate: 95.2%). The 13-year trends in the proportion of adolescents who reported aerobic and muscle-strengthening activities met or exceeded 2020 WHO exercise guidelines for adolescents plateaued (11.9% from 2009 to 2011, 14.2% from 2018 to 2019, 14.4% from 2020, and 14.0% from 2021); however, the slope decreased during the pandemic (ßdiff , -0.076; 95% confidence interval [CI], -0.123 to -0.029). Proportion of sufficient aerobic exercise among adolescents sharply decreased midst the pandemic (28.0% from 2009 to 2011, 29.4% from 2018 to 2019, and 23.8% from 2020; ßdiff , -0.266; 95% CI, -0.306 to -0.226) but increased again in 2021 (26.0% from mid-COVID 19; 95% CI, 25.4-26.7). Similar patterns were observed in Metabolic Equivalent Task (MET) score (MET-min/week; 804.1 from 2018 to 2019, 720.9 from 2020, and 779.6 from 2021). The mean difference in MET score between pre-COVID and post-COVID was -55.4 MET-min/week (95% CI, -70.5 to -40.3). Through a nationwide representative study, there was no significant difference with regard to the number of Korean adolescents who achieved the PA guidelines (pre and postpandemic); however, the prevalence of recommended levels of PA needs to increase more based on the trend before the COVID-19 outbreak. The findings of this study suggest reinforcement of the importance of public health policies for Korean youths to be more physically active, especially during and after the pandemic.

An aptamer-based magnetic resonance imaging contrast agent for detecting oligomeric amyloid-ß in the brain of an Alzheimer's disease mouse model.

The oligomeric amyloid-ß (oAß) is a reliable feature for an early diagnosis of Alzheimer's disease (AD). Therefore, the objective of this study was to demonstrate imaging of oAß deposits using our developed DNA aptamer called ob5 conjugated with gadolinium (Gd)-dodecane tetraacetic acid (DOTA) as a contrast agent for early diagnosis of AD using MRI. An oAß-specific aptamer was developed by amide bond formation and conjugated to Gd-DOTA MRI contrast agent and/or cyanine5 (cy5). We verified the performance of our new contrast agent with an AD mouse model using in vivo and ex vivo fluorescent imaging and animal MRI experiments. The presence of soluble Aß in 3xTg AD mice was detected using GdDOTA-ob5-cy5 probe ex vivo. Fluorescence intensities of the GdDOTA-ob5-cy5 contrast agent were high in the brains of 3xTg-AD mice, but relatively low in the brains of control mice. The GdDOTA-ob5 contrast agent had higher relaxivity than a clinically available contrast agent. T1-weighted MRI signals in 5-month-old 3xTg AD mice increased at 5 min, were prolonged until 10 min, then decreased 15 min after injecting the GdDOTA-ob5 contrast agent. Our targeted DNA aptamer GdDOTA-ob5 contrast agent could be potentially useful for validating the efficacy of a novel diagnostic contrast agent for selectively targeting neurotoxic oAß. It could ultimately be used for early diagnosis of AD.

A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism.

OBJECTIVES: This study aimed to compare susceptibility map-weighted imaging (SMwI) using various MRI machines (three vendors) with N-3-fluoropropyl-2-ß-carbomethoxy-3-ß-(4-iodophe nyl)nortropane (18F-FP-CIT) PET in the diagnosis of neurodegenerative parkinsonism in a multi-centre setting. METHODS: We prospectively recruited 257 subjects, including 157 patients with neurodegenerative parkinsonism, 54 patients with non-neurodegenerative parkinsonism, and 46 healthy subjects from 10 hospitals between November 2019 and October 2020. All participants underwent both SMwI and 18F-FP-CIT PET. SMwI was interpreted by two independent reviewers for the presence or absence of abnormalities in nigrosome 1, and discrepancies were resolved by consensus. 18F-FP-CIT PET was used as the reference standard. Inter-observer agreement was tested using Cohen's kappa coefficient. McNemar's test was used to test the agreement between the interpretations of SMwI and 18F-FP-CIT PET per participant and substantia nigra (SN). RESULTS: The inter-observer agreement was 0.924 and 0.942 per SN and participant, respectively. The diagnostic sensitivity of SMwI was 97.9% and 99.4% per SN and participant, respectively; its specificity was 95.9% and 95.2%, respectively, and its accuracy was 97.1% and 97.7%, respectively. There was no significant difference between the results of SMwI and 18F-FP-CIT PET (p > 0.05, for both SN and participant). CONCLUSIONS: This study demonstrated that the high diagnostic performance of SMwI was maintained in a multi-centre setting with various MRI scanners, suggesting the generalisability of SMwI for determining nigrostriatal degeneration in patients with parkinsonism. KEY POINTS: • Susceptibility map-weighted imaging helps clinicians to predict nigrostriatal degeneration. • The protocol for susceptibility map-weighted imaging can be standardised across MRI vendors. • Susceptibility map-weighted imaging showed diagnostic performance comparable to that of dopamine transporter PET in a multi-centre setting with various MRI scanners.

Parosmia in Right-lateralized Semantic Variant Primary Progressive Aphasia: A Case Report.

Parosmia, defined as the distorted perception of an odor stimulus, has been reported to be associated with head trauma, upper respiratory tract infections, sinonasal diseases, and toxin/drug consumption. To date, little is known about parosmia in right-lateralized semantic variant primary progressive aphasia. A 60-year-old right-handed man presented with a 2-year history of parosmia and prosopagnosia. Brain magnetic resonance imaging demonstrated severe atrophy of the right anterior and mesial temporal lobe, particularly in the fusiform cortex and the regions known as the primary olfactory cortex. 18F-fluorodeoxyglucose position emission tomography showed asymmetric hypometabolism of the bilateral temporal lobes (right > left). We clinically diagnosed him with right-lateralized semantic variant primary progressive aphasia. As the right hemisphere is known to be more involved in the processing of pleasant odors than the left hemisphere, we speculate that the unique manifestation of parosmia observed in this patient might be associated with the lateralization of the olfactory system.

Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance.

PURPOSE: Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented. MATERIALS AND METHODS: The proposed framework consists of the following phases: (1) pre-processing (skull extraction), (2) the first training with the ground truth labeled using CMB, (3) the second training with the ground truth labeled with CMB mimicking the same subjects, and (4) post-processing (cerebrospinal fluid (CSF) filtering). The proposed technique was validated on a dataset of 1133 CBMs that consisted of 5284 images for training and 1737 images for testing. We applied a two-stage approach using a region-based CNN method based on You Only Look Once (YOLO) to investigate a novel CMB detection technique. RESULTS: The sensitivity, precision, F1-score and false positive per person (FPavg) were evaluated as 80.96, 60.98, 69.57 and 6.57, 59.69, 62.70, 61.16 and 4.5, 66.90, 79.75, 72.76 and 2.15 for YOLO with a single label, YOLO with double labels, and YOLO + CSF filtering, respectively, and YOLO + CSF filtering showed the highest precision performance, F1-score and lowest FPavg. CONCLUSIONS: Using proposed framework, we developed an optimized CMB learning model with low false positives and a balanced performance in clinical practice.

A case report of multiple cervical artery dissection after peripheral type facial palsy and use of steroids.

BACKGROUND: Cervical artery dissection is one of the most important causes of ischemic stroke in young age patients. However, multiple cervical artery dissection simultaneously involving the anterior and posterior circulation is uncommon. Here, we would like to report a case of a patient with bilateral vertebral artery (VA) and internal carotid artery dissection (ICA) after a use of systemic steroid due to peripheral facial palsy. CASE PRESENTATION: A 44-year-old man with hypertension visited emergency department due to recurrent vertigo. He was receiving methyl prednisolone for two weeks for the treatment of right peripheral type facial palsy which occurred after retro-orbital headache. Neurologic examination revealed severe ataxia at left side. Sensory for pain and temperature was declined in the right arm and leg. Diffusion-weighted image showed an acute ischemic lesion at the whole territory of posterior-inferior cerebellar artery. Severe stenosis was observed from bilateral VAs and ICAs on conventional magnetic resonance angiography. Intramural hematoma and intimal flap was observed from the high-resolution MRI. CONCLUSIONS: Peripheral type facial palsy is an unusual presentation of carotid dissection. Steroids aggravate arterial dissection by increasing blood pressure and blood vessel fragility by its negative effect on connective tissue strength. Use of steroid in patients with peripheral type facial palsy with severe headache may need caution.

Prediction of hemifacial spasm re-appearing phenomenon after microvascular decompression surgery in patients with hemifacial spasm using dynamic susceptibility contrast perfusion MRI.

Objective: Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion MRI and two analytical methods: receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: This study enrolled sixty patients who underwent MVD for HFS. They were divided into two groups: Group A consisted of 32 patients who had early recurrence, and Group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: Group A had significantly lower relative cerebral blood flow (rCBF) than Group B in most of the selected brain regions, as shown by the region-of-interest (ROI)-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve (AUC) value of 0.845. Conclusion: By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function.

BACKGROUND: Apolipoprotein E (ApoE) ε4 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis. OBJECTIVE: To predict ApoE ε4 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods. METHODS: We recruited 74 participants (25 probable AD, 24 amnestic mild cognitive impairment, and 25 cognitively normal older people) with known ApoE genotype (22 ApoE ε4 carriers and 52 noncarriers) and scanned them with three-dimensional (3D) T1-weighted (T1W) and 3D double inversion recovery (DIR) sequences. We extracted GMV from regions of interest related to AD pathology and used them as features along with age and mini-mental state examination (MMSE) scores to train different ML models. We performed both receiver operating characteristic curve analysis and the prediction analysis of the ApoE ε4 carrier with different ML models. RESULTS: The best model of ML analyses was a cubic support vector machine (SVM3) that used age, the MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus as features (AUC = .88). This model outperformed models using T1W GMV or demographic data alone. CONCLUSION: Our results suggest that brain atrophy with DIR GMV and cognitive decline with aging can be useful biomarkers for predicting ApoE ε4 status and identifying individuals at risk of AD progression.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance.

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.

Background: Meningiomas are the most common primary central nervous system tumors, and magnetic resonance imaging (MRI), especially contrast-enhanced T1 weighted image (CE T1WI), is used as a fundamental imaging modality for the detection and analysis of the tumors. In this study, we propose an automated deep-learning model for meningioma detection using the dural tail sign. Methods: The dataset included 123 patients with 3,824 dural tail signs on sagittal CE T1WI. The dataset was divided into training and test datasets based on specific time point, and 78 and 45 patients were comprised for the training and test dataset, respectively. To compensate for the small sample size of the training dataset, 39 additional patients with 69 dural tail signs from the open dataset were appended to the training dataset. A You Only Look Once (YOLO) v4 network was trained with sagittal CE T1WI to detect dural tail signs. The normal group dataset, comprised of 51 patients with no abnormal finding on MRI, was employed to evaluate the specificity of the trained model. Results: The sensitivity and false positive average were 82.22% and 29.73, respectively, in the test dataset. The specificity and false positive average were 17.65% and 3.16, respectively, in the normal dataset. Most of the false-positive cases in the test dataset were enhancing vessels, misinterpreted as dural thickening. Conclusions: The proposed model demonstrates an automated detection system for the dural tail sign to identify meningioma in general screening MRI. Our model can facilitate and alleviate radiologists' reading process by notifying the possibility of incidental dural mass based on dural tail sign detection.

Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women.

The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.

Blood viscosity associated with stroke mechanism and early neurological deterioration in middle cerebral artery atherosclerosis.

Blood viscosity may affect the mechanisms of stroke and early neurological deterioration (END). We aimed to investigate the relationship between blood viscosity, stroke mechanisms, and END in patients with middle cerebral artery (MCA) infarction. Patients with symptomatic MCA atherosclerosis (≥ 50% stenosis) were recruited. Blood viscosity was compared across patients with different mechanisms of symptomatic MCA disease: in situ thrombo-occlusion (sMCA-IST), artery-to-artery embolism (sMCA-AAE), and local branch occlusion (sMCA-LBO). END was defined as four points increase in the National Institutes of Health Stroke Scale score from baseline during the first week. The association between blood viscosity and END was also evaluated. A total of 360 patients (76 with sMCA-IST, 216 with sMCA-AAE, and 68 with sMCA-LBO) were investigated. Blood viscosity was highest in patients with sMCA-IST, followed by sMCA-AAE and sMCA-LBO (P < 0.001). Blood viscosity was associated with END in patients with MCA disease. Low shear viscosity was associated with END in patients with sMCA- LBO (adjusted odds ratio, aOR 1.524; 95% confidence interval, CI 1.035-2.246), sMCA- IST (aOR 1.365; 95% CI 1.013-1.839), and sMCA- AAE (aOR 1.285; 95% CI 1.010-1.634). Blood viscosity was related to END in patients with stroke caused by MCA disease.

Wall Shear Stress Associated with Stroke Occurrence and Mechanisms in Middle Cerebral Artery Atherosclerosis.

BACKGROUND AND PURPOSE: Various mechanisms are involved in the etiology of stroke caused by atherosclerosis of the middle cerebral artery (MCA). Here, we compared differences in plaque nature and hemodynamic parameters according to stroke mechanism in patients with MCA atherosclerosis. METHODS: Consecutive patients with asymptomatic and symptomatic MCA atherosclerosis (≥50% stenosis) were enrolled. MCA plaque characteristics (location and plaque enhancement) and wall shear stress (WSS) were measured using high-resolution vessel wall and four-dimensional flow magnetic resonance imaging, respectively, at five points (initial, upstream, minimal lumen, downstream, and terminal). These parameters were compared between patients with asymptomatic and symptomatic MCA atherosclerosis with infarctions of different mechanisms (artery-to-artery embolism vs. local branch occlusion). RESULTS: In total, 110 patients (46 asymptomatic, 32 artery-to-artery embolisms, and 32 local branch occlusions) were investigated. Plaques were evenly distributed in the MCA of patients with asymptomatic MCA atherosclerosis, more commonly observed in the distal MCA of patients with artery-to-artery embolism, and in the middle MCA of patients with local branch occlusion. Maximum WSS and plaque enhancement were more prominent in the minimum lumen area of patients with asymptomatic MCA atherosclerosis or those with local branch occlusion, and were more prominent in the upstream area in those with artery-to-artery embolism. The elevated variability in the maximum WSS was related to stroke caused by artery-to-artery embolism. CONCLUSION: Stroke caused by artery-to-artery embolism was related to plaque enhancement and the highest maximum WSS at the upstream point of the plaque, and was associated with elevated variability of maximum WSS.

Diagnostic Challenge in Rapidly Growing Langerhans Cell Histiocytosis with Aneurysmal Bone Cyst in the Maxilla: A Case Report.

Langerhans cell histiocytosis (LCH) is a rare neoplastic disorder characterized by the clonal proliferation of CD1a +/CD 207 + dendritic cells, whose features are similar to those of epidermal Langerhans cells. LCH is more common in children than in adults. Localized osteolytic lesions in the craniofacial bones are the most common manifestations of LCH. However, LCH can also present as a multifocal and multisystem disease with poor prognosis. Locally aggressive LCH needs to be differentiated from various diseases such as osteomyelitis, malignant bone tumors, and soft tissue sarcomas. However, it is difficult to diagnose, since the imaging findings are nonspecific. We report a case of a highly aggressive LCH in the maxilla accompanied by a fluid-fluid level.

Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

OBJECTIVE: Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography. MATERIALS AND METHODS: A total of 2,208 mammography phantom images were collected for periodic accreditation of the scanner from 1,755 institutions. The dataset was randomly split into training (1,808 images) and testing (400 images) datasets with subgroups (76 images) for the multi-reader study. To develop an interpretable model that contains two deep learning networks in series, five processing steps were performed: mammography phantom detection, phantom object detection, post-processing, score evaluation, and a report with a comment about ambiguous results. RESULTS: For phantom detection, the accuracy and mean intersection over union (mIOU) were 1.00 and 0.938 in the test dataset, respectively. During phantom object detection, a total of 6,369 out of 6,400 objects were detected as the correct object class, and the accuracy and mIOU were 0.995 and 0.813, respectively. The predicted score for each object showed a consensus of 97.40% excluding ambiguous points and 59.10% for ambiguous points of the groups. CONCLUSIONS: The interpretable deep learning model using large-scale data from multiple centers shows high performance and reasonable object scoring, successfully validating the reliability and feasibility of mammography phantom image quality management.

Diagnostic Performance of High-Resolution Vessel Wall Magnetic Resonance Imaging and Digital Subtraction Angiography in Intracranial Vertebral Artery Dissection.

PURPOSE: Intracranial vertebral artery dissection (VAD) is being increasingly recognized as a leading cause of Wallenberg syndrome and subarachnoid hemorrhage. Conventional angiography is considered the standard diagnostic modality, but the diagnosis of VAD remains challenging. This study aimed to compare the diagnostic performance of high-resolution vessel wall imaging (HR-VWI) with digital subtraction angiography (DSA) for intracranial VAD. MATERIALS AND METHODS: Twenty-four patients with 27 VADs, who underwent both HR-VWI and DSA within 2 weeks, were consecutively enrolled in the study from March 2016 to September 2020. HR-VWI and DSA were performed to diagnose VAD and to categorize its angiographic features as either definite dissection or suspicious dissection. Features of HR-VWI were used to evaluate direct arterial wall imaging. The reference standard was set from the clinicoradiologic diagnosis. Two independent raters evaluated the angiographic features, dissection signs, and interrater agreement. Each subject was also dichotomized into two groups (suspicious or definite VAD) in each modality, and diagnosis from HR-VWI and DSA was compared with the final diagnosis by consensus. RESULTS: HR-VWI had higher agreement (90.6% vs. 53.1%) with the final diagnosis and better interrater reliability (kappa value (κ) = 0.91; 95% confidence interval (CI) = 0.64-1.00) compared with DSA (κ = 0.58; 95% CI = 0.35-1.00). HR-VWI provided a more detailed identification of dissection signs (77.7% vs. 22.2%) and better reliability (κ = 0.88; 95% CI = 0.58-1.00 vs. κ = 0.75; 95% CI = 0.36-1.00), compared to DSA. HR-VWI was comparable to DSA for the depiction of angiographic features for VAD. CONCLUSIONS: HR-VWI may be useful to evaluate VAD, with better diagnostic confidence compared to DSA.