Imaging of Amyloid-beta-related Arteritis

Cerebral amyloid angiopathy (CAA) is a cerebrovascular disorder marked by the accumulation of amyloid-beta peptide (Aβ) within the leptomeninges and smaller blood vessels of the brain. CAA can be both noninflammatory and inflammatory, and the inflammatory version includes Aβ-related angiitis (ABRA). ABRA is a vasculitis of the central nervous system related to an inflammatory response to Aβ in the vascular walls, which necessitates differentiating ABRA from noninflammatory CAA, as ABRA may require immunosuppressive treatment. MR imaging is typically the most effective imaging modality of choice to screen for these conditions, and they should be obtained at varying time points to track disease progression.

Detection of Intracerebral Hemorrhage Using Low-Field, Portable Magnetic Resonance Imaging in Patients With Stroke

BACKGROUND

Neuroimaging is essential for detecting spontaneous, nontraumatic intracerebral hemorrhage (ICH). Recent data suggest ICH can be characterized using low-field magnetic resonance imaging (MRI). Our primary objective was to investigate the sensitivity and specificity of ICH on a 0.064T portable MRI (pMRI) scanner using a methodology that provided clinical information to inform rater interpretations. As a secondary aim, we investigated whether the incorporation of a deep learning (DL) reconstruction algorithm affected ICH detection.

METHODS

The pMRI device was deployed at Yale New Haven Hospital to examine patients presenting with stroke symptoms from October 26, 2020 to February 21, 2022. Three raters independently evaluated pMRI examinations. Raters were provided the images alongside the patient's clinical information to simulate real-world context of use. Ground truth was the closest conventional computed tomography or 1.5/3T MRI. Sensitivity and specificity results were grouped by DL and non-DL software to investigate the effects of software advances.

RESULTS

A total of 189 exams (38 ICH, 89 acute ischemic stroke, 8 subarachnoid hemorrhage, 3 primary intraventricular hemorrhage, 51 no intracranial abnormality) were evaluated. Exams were correctly classified as positive or negative for ICH in 185 of 189 cases (97.9% overall accuracy). ICH was correctly detected in 35 of 38 cases (92.1% sensitivity). Ischemic stroke and no intracranial abnormality cases were correctly identified as blood-negative in 139 of 140 cases (99.3% specificity). Non-DL scans had a sensitivity and specificity for ICH of 77.8% and 97.1%, respectively. DL scans had a sensitivity and specificity for ICH of 96.6% and 99.3%, respectively.

CONCLUSIONS

These results demonstrate improvements in ICH detection accuracy on pMRI that may be attributed to the integration of clinical information in rater review and the incorporation of a DL-based algorithm. The use of pMRI holds promise in providing diagnostic neuroimaging for patients with ICH.

Polygenic Susceptibility to Hypertension and Cognitive Performance in Middle-aged Persons Without Stroke or Dementia

BACKGROUND AND OBJECTIVES

Mounting evidence indicates that hypertension leads to a higher risk of dementia. Hypertension is a highly heritable trait, and a higher polygenic susceptibility to hypertension (PSH) is known to associate with a higher risk of dementia. We tested the hypothesis that a higher PSH leads to worse cognitive performance in middle-aged persons without dementia. Confirming this hypothesis would support follow-up research focused on using hypertension-related genomic information to risk-stratify middle-aged adults before hypertension develops.

METHODS

We conducted a nested cross-sectional genetic study within the UK Biobank (UKB). Study participants with a history of dementia or stroke were excluded. We categorized participants as having low (≤20th percentile), intermediate, or high (≥80th percentile) PSH according to results of 2 polygenic risk scores for systolic and diastolic blood pressure (BP) generated with data on 732 genetic risk variants. A general cognitive ability score was calculated as the first component of an analysis that included the results of 5 cognitive tests. Primary analyses focused on Europeans, and secondary analyses included all race/ethnic groups.

RESULTS

Of the 502,422 participants enrolled in the UKB, 48,118 (9.6%) completed the cognitive evaluation, including 42,011 (8.4%) of European ancestry. Multivariable regression models using systolic BP-related genetic variants indicated that compared with study participants with a low PSH, those with intermediate and high PSH had reductions of 3.9% (β -0.039, SE 0.012) and 6.6% (β -0.066, SE 0.014), respectively, in their general cognitive ability score (p < 0.001). Secondary analyses including all race/ethnic groups and using diastolic BP-related genetic variants yielded similar results (p < 0.05 for all tests). Analyses evaluating each cognitive test separately indicated that reaction time, numeric memory, and fluid intelligence drove the association between PSH and general cognitive ability score (all individual tests, p < 0.05).

DISCUSSION

Among nondemented, community-dwelling, middle-aged Britons, a higher PSH is associated with worse cognitive performance. These findings suggest that genetic predisposition to hypertension influences brain health in persons who have not yet developed dementia. Because information on genetic risk variants for elevated BP is available long before the development of hypertension, these results lay the foundation for further research focused on using genomic data for the early identification of high-risk middle-aged adults.

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial

BACKGROUND

Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making.

METHODS

For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic.

FINDINGS

From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger.

INTERPRETATION

The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool.

FUNDING

ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.

Suboptimal Sleep Duration is Associated with Poorer Neuroimaging Brain Health Profiles

Background

Cardiovascular health optimization during middle age benefits brain health. The American Heart Association's Life's Simple 7 recently added sleep duration as a key determinant of cardiovascular health becoming the Life's Essential 8. We tested the hypothesis that suboptimal sleep duration is associated with poorer neuroimaging brain health profiles in asymptomatic middle-aged adults.

Methods

We conducted a prospective MRI neuroimaging study in middle-aged persons without stroke, dementia, or multiple sclerosis enrolled in the UK Biobank. Self-reported sleep duration was categorized as short (<7 hours), optimal (7-<9 hours), or long (≥9 hours). Evaluated neuroimaging markers of brain health included white matter hyperintensities (presence and volume) and diffusion tensor imaging metrics (fractional anisotropy and mean diffusivity) evaluated in 48 distinct neuroanatomical regions. We used multivariable logistic and linear regression models, as appropriate, to test for association between sleep duration and neuroimaging markers of brain health.

Results

We evaluated 39,502 middle-aged persons (mean age 55, 53% female). Of these, 28,712 (72.7%) had optimal, 8,422 (21.3%) short, and 2,368 (6%) long sleep. Compared to optimal sleep, short sleep was associated with higher risk (OR 1.11; 95% CI 1.05-1.17; P<0.001) and larger volume (beta=0.06, SE=0.01; P<0.001) of white matter hyperintensities, while long sleep was associated with higher volume (beta=0.04, SE=0.02; P=0.01) but not higher risk (P>0.05) of white matter hyperintensities. Short (beta=0.03, SE=0.01; P=0.004) and long sleep (beta=0.07, SE=0.02; P<0.001) were associated with worse fractional anisotropy, while only long sleep associated with worse mean diffusivity (beta=0.05, SE=0.02; P=0.005).

Conclusions

Among middle-aged adults without clinically observed neurological disease, suboptimal sleep duration is associated with poorer neuroimaging brain health profiles. Because the evaluated neuroimaging markers precede stroke and dementia by several years, our findings support early interventions aimed at correcting this modifiable risk factor.

Malpractice Litigation Related to Diagnosis and Treatment of Intracranial Aneurysms

BACKGROUND AND PURPOSE

Approaches to management of intracranial aneurysms are inconsistent, in part due to apprehension relating to potential malpractice claims. The purpose of this article was to review the causes of action underlying medical malpractice lawsuits related to the diagnosis and management of intracranial aneurysms and to identify the factors associated and their outcomes.

MATERIALS AND METHODS

We consulted 2 large legal databases in the United States to search for cases in which there were jury awards and settlements related to the diagnosis and management of patients with intracranial aneurysms in the United States. Files were screened to include only those cases in which the cause of action involved negligence in the diagnosis and management of a patient with an intracranial aneurysm.

RESULTS

Between 2000 and 2020, two hundred eighty-seven published case summaries were identified, of which 133 were eligible for inclusion in the analysis. Radiologists constituted 16% of 159 physicians sued in these lawsuits. Failure to diagnose was the most common medical malpractice claim referenced (100/133 cases), with the most common subgroups being "failure to include cerebral aneurysm as a differential and thus perform adequate work-up" (30 cases), and "failure to correctly interpret aneurysm evidence on CT or MR imaging" (16 cases). Only 6 of these 16 cases were adjudicated at trial, with 2 decided in favor of the plaintiff (awarded $4,000,000 and $43,000,000, respectively).

CONCLUSIONS

Incorrect interpretation of imaging is relatively infrequent as a cause of malpractice litigation compared with failure to diagnose aneurysms in the clinical setting by neurosurgeons, emergency physicians, and primary care providers.

Poor Oral Health Is Associated with Worse Brain Imaging Profiles

Importance

Poor oral health is a modifiable risk factor that is associated with a variety of health outcomes. However, the relationship between oral and brain health is not well understood.

Objective

To test the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in persons without stroke or dementia.

Design

We conducted a 2-stage cross-sectional neuroimaging study using data from the UK Biobank (UKB). First, we tested for association between self-reported poor oral health and MRI neuroimaging markers of brain health. Second, we used Mendelian Randomization (MR) analyses to test for association between genetically-determined poor oral health and the same neuroimaging markers.

Setting

Ongoing population study in the United Kingdom. The UKB enrolled participants between 2006 and 2010. Data analysis was performed from September 1, 2022, to January 10, 2023.

Participants

40,175 persons aged 40 to 70 enrolled between 2006 to 2010 who underwent a dedicated research brain MRI between 2012 and 2013.

Exposures

During MRI assessment, poor oral health was defined as the presence of dentures or loose teeth. As instruments for the MR analysis, we used 116 independent DNA sequence variants known to significantly increase the composite risk of decayed, missing, or filled teeth and dentures.

Main Outcomes and Measures

As neuroimaging markers of brain health, we assessed the volume of white matter hyperintensities (WMH), as well as aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), two metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging. These measurements were evaluated across 48 distinct brain regions, with FA and MD values for each region also considered as individual outcomes for the MR method.

Results

Among study participants, 5,470 (14%) had poor oral health. We found that poor oral health was associated with a 9% increase in WMH volume (beta = 0.09, standard deviation (SD) = 0.014, p P< 0.001), a 10% change in the aggregate FA score (beta = 0.10, SD = 0.013, P < 0.001), and a 5% change in the aggregate MD score (beta = 0.05, SD = 0.013, P < 0.001). Genetically-determined poor oral health was associated with a 30% increase in WMH volume (beta = 0.30, SD = 0.06, P < 0.001), a 43% change in aggregate FA score (beta = 0.42, SD = 0.06, P < 0.001), and an 10% change in aggregate MD score (beta = 0.10, SD = 0.03, P = 0.01).

Conclusions and Relevance

Among middle age Britons without stroke or dementia enrolled in a large population study, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potential causal association. Because the neuroimaging markers evaluated in the current study are established risk factors for stroke and dementia, our results suggest that oral health may be a promising target for interventions focused on improving brain health.

Abstract 131: Brain Mri Biomarkers Of Impaired Balance And Slow Walk Speed: Atherosclerosis Risk In Communities And UK Biobank Studies

Background and Objectives: Vascular brain injury (VBI) may contribute to imbalance and slow walk speed, but this is uncertain. We hypothesize that MRI biomarkers of VBI associate with impaired balance and slow walk speed.

Methods: We performed separate, cross-sectional analyses in the Atherosclerosis Risk in Communities (ARIC) and UK Biobank (UKB) studies. Eligible participants had no prior clinical stroke and underwent a brain MRI and balance and walk speed ascertainment. MRI biomarkers of VBI analyzed were: ventricular volume, white matter hyperintensity volume (WMH), non-lacunar infarction, lacunar infarction, microhemorrhage in ARIC; ventricular volume, brain volume, WMH, fractional anisotropy (FA), mean diffusivity (MD), intra-cellular volume fraction, isotropic free water volume fraction in UKB. Quantitative biomarker levels were classified into tertiles, the unhealthiest tertile designated as the exposure. Our outcomes were poor balance and slow walk speed. We constructed multivariable logistic regression models to examine the associations between each MRI biomarker and the outcomes, adjusting for demographics and clinical history.

Results: We included 1,626 ARIC participants (mean age 76.2 years; 23.4% impaired balance, 25.0% slow walk speed) and 40,098 UKB participants (mean age 55 years; 15.8% impaired balance, 2.8% slow walk speed). In ARIC, impaired balance was associated with 4 of 5 MRI measures of VBI in adjusted analysis (all p-values <0.05). The strongest association was with WMH (OR 1.36; 95% C.I. 1.04-1.76). Slow walk speed in ARIC was significantly associated with 4 of 5 MRI measures; the strongest association was with silent lacunar infarcts (OR 2.17; 95% C.I. 1.61-2.93). In UKB, poor balance was associated with all MRI biomarkers except WMH. The strongest association was with FA (OR 1.16; 95% C.I. 1.08-1.24). Slow walking speed was associated with WMH, FA, and MD. The strongest association was with FA (OR 1.42; 95% C.I. 1.21-1.67).

Conclusions: We demonstrate that MRI measures of VBI are independently associated with impaired balance and slow walk speed in two studies of community-dwelling adults with no history of clinical stroke. Consequences of VBI may extend beyond clinically apparent stroke to also include mobility.

Abstract 126: Genetic Analyses Of Oral Health And Neuroimaging Markers Of Brain Health In Persons Without Stroke

Background: Oral health is a modifiable risk factor for stroke. However, the role of oral health in the brain health of clinically asymptomatic persons remains understudied. We hypothesize that genetically-determined poor oral health leads to worse neuroimaging brain health profiles in persons without stroke.

Methods: We conducted a two-sample Mendelian Randomization (MR) study. As instruments, we used 105 genetic variants known to be associated (p<5x10 -8 ) with a composite of caries, dentures and missing teeth in the GLIDE Consortium. In stroke-free participants enrolled in the UK Biobank, we tested for association between these genetic variants and white matter hyperintensity volume (natural log-transformed), fraction anisotropy and mean diffusivity. For the last two neuroimaging traits, we evaluated the first principal component of measurements obtained across 48 brain regions.

Results: Our primary analysis using the inverse variance-weighted MR method indicated that genetically-increased risk of poor oral health was associated with: (1) higher burden of silent cerebrovascular disease, as represented by higher volumes of white matter hyperintensities (beta=0.24, SE=0.07 p-value=0.001), and (2) increased microstructural damage, as represented by lower fractional anisotropy (beta=-2.53, SE=0.38; p=1x10 -9 ) and higher mean diffusivity (beta=3.42, SE=0.41; p=2x10 -11 ). Sensitivity analyses identified horizontal pleiotropy in our primary results, but an outlier-corrected analysis confirmed all three initial results (all p-values <0.001, Table 1).

Conclusion: Among persons without stroke, genetically-determined poor oral health is associated with worse neuroimaging brain health profiles. Because gene-disease associations are immune to confounding, our results indicate that this association is causal. Early treatment of poor oral health may lead to significant brain health benefits, even in persons without stroke.

Abstract TMP79: Deep Learning Model For Prediction Of Supratentorial Intracerebral Hemorrhage (ich) Expansion

Purpose: Hematoma expansion (HE) is associated with early clinical deterioration, worse long-term outcome, and higher mortality in ICH. Identification of patients at risk of HE may allow targeted anti-expansion therapies in future trials. We aimed to train and validate a deep learning model for prediction of HE based on admission non-contrast head CT.

Methods: We utilized the clinical and imaging information from the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-2) trial and Yale ICH registry. All patients with spontaneous supratentorial ICH who had baseline head CT (<24 h from last know well), follow-up scan (within 16-48 h of baseline), axial slice thickness <5.7 mm, and slices numbers >28 were included. The HE was defined as an increase volume >33% or 6 ml. We trained a DenseNet121-based 3D convolutional neural network for prediction of HE (pipeline summarized in the Figure).

Results: A total of 749 patients (610 from ATACH-2 and 183 from Yale) were randomly split (4:1) into 634 training/cross-validation (368 (58%) male, mean age of 62.8±13.1 years, median NIHSS 10, and 144 (23%) with HE); and 159 independent test set (92 (58%) male, mean age of 62.5±14.6 years, median NIHSS 12, and 36 (23%) with HE). The average of validation AUC in five-fold training/cross-validation was 0.62, with the best performing model achieving an AUC of 0.71 in validation fold. We then tested this model in the independent validation cohort isolated from training process, achieving an AUC of 0.67 in predicting HE. The attention heatmaps (Figure) confirm that deep learning model predictions were based on head CT regions containing ICH and perilesional edema.

Conclusion: We could train, and independently validate a deep learning model for identification of supratentorial ICH patients at risk of impending HE. Compared to visual markers (including CTA spot sign), such model can provide an automated objective tool for future trial enrollment based on readily available non-contrast CT

Abstract 57: Sleep Duration Is Associated With Clinically Silent Brain Injury In Middle-aged Persons Without Stroke

Background: Evidence indicates that optimization of cardiovascular health during middle age leads to brain health benefits later in life. The AHA Life’s Simple 7, a research and public health construct capturing determinants of cardiovascular health, recently added sleep as a risk factor, becoming the Life’s Essential 8 (LE8). We hypothesize that suboptimal sleep duration worsens neuroimaging brain health profiles in middle-aged persons without stroke.

Methods: We conducted a nested, cross-sectional neuroimaging analysis within the UK Biobank, a large population study conducted in the United Kingdom. We included participants without stroke/dementia who underwent a research brain MRI. We created a 6-category sleep score according to hours of sleep (best to worse): 7 to <9h; 9 to <10h; 6 to <7h; 5 to <6 or >=10h; 4 to <5h; and <4h. We evaluated 3 neuroimaging traits: white matter hyperintensity volume (natural log-transformed), fractional anisotropy (FA) and mean diffusivity (MD). For the last two traits, we evaluated the first principal component of measurements obtained across 48 neuroanatomical regions.

Results: Of 502,408 participants enrolled in the UKB, 39,937 (7.9%) stroke/dementia-free enrollees participated in the brain MRI study (mean age 55, 53% female). The distribution of sleep categories was: 1 (n=28,958, 72.51%), 2 (n=2060, 5.16%), 3 (n=7165, 17.94%), 4 (n=1,562, 3.91%), 5 (n=163, 0.41%) and 6 (n=29, 0.07%). In multivariable linear regression analyses, a higher (worse) sleep score was associated with larger WMH volume (beta 0.026, SE=0.0046; p<0.001) and worse FA profile (beta 0.003, SE=0.0008; p<0.001), with no association observed for MD (p=0.77). For FA, when evaluating the 48 neuroanatomical regions separately, the most compromised areas were the right cerebral peduncle (p<0.001) and left and right cerebellar peduncles (p<0.001).

Conclusion: Among middle-aged participants enrolled in the UKB, suboptimal sleep duration was significantly associated with adverse neuroimaging brain health profiles. These results emphasize the importance of sleep duration, the new component of the LE8, in determining brain health in middle-aged persons who have not developed clinically evident manifestations of poor brain health (stroke).

Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction

Purpose

Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient’s medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction.

Materials and methods

An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations.

Results

UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study.

Conclusion

Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.

Deep Learning Applications for Acute Stroke Management

Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.

Abstract 9: Pervasive White Matter Microstructure Dysintegrity Related To High Blood Pressure Among Asymptomatic Population

Background: Blood pressure is the strongest contributor to clinically evident cerebrovascular disease. We investigated the role of blood pressure on white matter (WM) microstructure among asymptomatic individuals without clinically evident cerebrovascular disease.

Methods: We conducted a nested study within the UK Biobank, restricting our analysis to participants without cerebrovascular disease or dementia and with available brain diffusion tensor imaging (DTI). We tested for association between systolic blood pressure (SBP) and six different DTI metrics: fractional anisotropy (FA), mean diffusivity (MD), tensor mode (MO), intra-cellular volume fraction (ICVF), isotropic or free water volume fraction (ISOVF), and orientation dispersion index (OD) across 48 WM tracts using multivariable linear regression adjusted for potential confounders. We used Bonferroni-corrected p-values (0.05/48) for statistical significance.

Results: We analyzed 33,440 participants. Mean age was 63.0 (SD 7.7), and 17,688 (53%) were female. Higher SBP is independently associated with pervasive decrease in FA and ICVF and increase in MD (Figure), after adjustment for vascular risk-factors. SBP was also associated with lower neurite OD (a more specific metric of axonal damage) in bilateral posterior corona radiata, external capsule, medial lemniscus, and corticospinal tracs. FA and OD of external capsule and posterior corona radiata had the largest drop per 10 mmHg increase of SBP (steepest slope).

Conclusions: Higher SBP is associated with pervasive WM microstructure dysintegrity in asymptomatic individuals without evident cerebrovascular disease. DTI metrics of the posterior corona radiata and external capsule are most reflective of variations in SBP and may provide a potential biomarker to assess subtle WM microstructural damage in hypertensive patients or monitor treatment response in clinical trials.

Abstract WP111: Deployment Of Portable, Bedside, Low-field Magnetic Resonance Imaging In The Emergency Department To Evaluate Patients With Acute Stroke

Background and Aims: MRI is critical for diagnosing acute stroke and guiding candidate selection for potential reperfusion therapy. However, rapid stroke evaluation using MRI is often dissuaded by the time required for patients to travel to access-controlled, high-field (1.5-3T) systems. Advances in low-field MRI enable the acquisition of clinically valuable images at the bedside. We report neuroimaging in patients presenting to the Emergency Department (ED) with stroke symptoms using a low-field portable MRI (pMRI) device.

Methods: A 64mT pMRI device was deployed in the Yale-New Haven Hospital ED from August 2020 to July 2021. Patients presenting as a “Stroke Code” or “Intracranial Hemorrhage Alert” with no MRI contraindications were scanned. Exams were performed at the bedside, in the vicinity of ED room equipment. Research staff acquired imaging via tablet, with images available immediately after acquisition. Sequences obtained and axial scan times (in minutes) included T1-weighted imaging (4:54), T2-weighted imaging (7:03), fluid-attenuated inversion recovery imaging (9:31), and diffusion-weighed imaging with apparent diffusion coefficient mapping (9:04). Patients’ demographic information, hours from the time of patients' last known normal (LKN) to time of scan, and discharge diagnoses (determined from final imaging interpretation) were assessed.

Results: pMRI exams were obtained on 54 patients (28 females, 51.9%; median age 71 years, 20-98 years). Discharge diagnoses included ischemic stroke (42.6%) no intracranial abnormality (31.5%), intraparenchymal hemorrhage (7.4%), atherosclerosis (7.4%), tumor (5.6%), subdural hematoma (3.7%), and intraventricular hemorrhage (1.9%). Patient LKN times ranged from 2 to 144 hours (median of 12 hours; 3 patients with no LKN excluded). The pMRI did not interfere with ED equipment and no significant adverse events occurred.

Conclusion: We report the use of a pMRI for bedside neuroimaging in the ED. This approach suggests that pMRI may be viable for supporting rapid diagnosis and treatment candidate selection in patients presenting with stroke symptoms to the ED.

Abstract 67: Observed And Genomic Life’S Simple 7 Influence Brain Health-related Neuroimaging Traits In Persons Without Stroke Or Dementia

Background: The AHA Life’s Simple 7 (LS7) promote cardiovascular health. We hypothesized that a better LS7 profile translates into significant brain health benefits in persons without stroke or dementia. We also evaluated whether genomic information can effectively recapitulate the observed LS7.

Methods: We conducted a nested study within the UK Biobank, restricting analysis to stroke- and dementia-free participants with brain MRI and genomic data. We ascertained the LS7 (blood pressure, LDL cholesterol, HbA1c, smoking, exercise, diet and BMI) clinically and genomically. For the latter, we used genetic variants known to influence each trait. The total LS7 score ranges from 0 (poor) to 14 (optimal), and was categorized as poor (≤4), average (4<score≤9) and optimal (>9). We tested for association between observed/genomic LS7 and two neuroimaging markers of brain health: white matter hyperintensities (WMH) volume and brain volume.

Results: We analyzed 35,914 participants. For WMH, compared to persons with poor observed LS7, those with average and optimal had 18% (beta -0.17; se=0.02; p<0.001) and 43% (beta -0.37; se=0.02; p<0.001) lower volumes. Similar results were obtained when using the genomic LS7 (all p<0.001). For brain volume, those with average and optimal LS7 had 0.86% (beta 0.12; se=0.02; p<0.001) and 2.4% (beta 0.18; se=0.02; p<0.001) higher volumes. The genomic LS7 were not associated with brain volume (all p>0.05). Blood pressure and HbA1c were the most powerful contributors to WMH and brain volume, respectively (Figure).

Conclusions: Better LS7 profiles are associated with better profiles of 2 brain health-related neuroimaging markers in persons without stroke/dementia. Genomic information appropriately recapitulated one of these associations. These results emphasize the beneficial role of cardiovascular health optimization in persons without stroke/dementia and point to genomic data as potentially useful to identify high risk individuals.

Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries

Background

While there are innumerable machine learning (ML) research algorithms used for segmentation of gliomas, there is yet to be a US FDA cleared product. The aim of this study is to explore the systemic limitations of research algorithms that have prevented translation from concept to product by a review of the current research literature.

Methods

We performed a systematic literature review on 4 databases. Of 11 727 articles, 58 articles met the inclusion criteria and were used for data extraction and screening using TRIPOD.

Results

We found that while many articles were published on ML-based glioma segmentation and report high accuracy results, there were substantial limitations in the methods and results portions of the papers that result in difficulty reproducing the methods and translation into clinical practice.

Conclusions

In addition, we identified that more than a third of the articles used the same publicly available BRaTS and TCIA datasets and are responsible for the majority of patient data on which ML algorithms were trained, which leads to limited generalizability and potential for overfitting and bias.

NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW

PURPOSE

Machine learning (ML) technologies have demonstrated highly accurate prediction of glioma grade, though it is unclear which methods and algorithms are superior. We have conducted a systematic review of the literature in order to identify the ML applications most promising for future research and clinical implementation.

MATERIALS AND METHODS

A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Screening of publications was done in Covidence, and TRIPOD was used for bias assessment.

RESULTS

The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles published since 1995 met the criteria for our study. 79% of the articles were published between 2018 and 2020. The average glioma prediction accuracy of the highest performing model in each study was 90% (range: 53% to 100%). The most common algorithm used for cML studies was Support Vector Machine (SVM) and for DL studies was Convolutional Neural Network (CNN). BRATS and TCIA datasets were used in 47% of the studies, with the average patient number of study datasets being 186 (range: 23 to 662). The average number of features used in machine learning prediction was 55 (range: 2 to 580). Classical machine learning (cML) was the primary machine learning model in 68% of studies, with deep learning (DL) used in 32%.

CONCLUSIONS

Using multimodal sequences in ML methods delivers significantly higher grading accuracies than single sequences. Potential areas of improvement for ML glioma grade prediction studies include increasing sample size, incorporating molecular subtypes, and validating on external datasets.

NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW

PURPOSE

Nowadays Machine learning (ML) algorithms are often used for segmentation of gliomas, but which algorithms provide the most accurate method for implementation into clinical practice has not fully been identified. We performed a systematic review of the literature to characterize the methods used for glioma segmentation and their accuracy.

METHODS

In accordance to PRISMA, a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Publications were screened in Covidence and the bias analysis was done in agreement with TRIPOD.

RESULTS

Sixty-six articles were used for data extraction. BRATS and TCIA datasets were used in 36.6% of all studies, with average number of patients being 141 (range: 1 to 622). ML methods represented 45.3% of studies, with deep learning used in 54.7%; Dice score for the tumor core ranged from 0.72 to 0.95. The most common algorithm used in the machine learning papers was support vector machines (SVM) and for deep learning papers, it was Convolutional Neural Networks (CNN). Preliminary TRIPOD analysis yielded an average score from 12 (range: 7-16) with the majority of papers demonstrating deficiencies in description of the ML algorithm, funding role, data acquisition and measures of model performance.

CONCLUSION

In the last years, many articles were published on segmentation of gliomas using machine learning, thus establishing this method for tumor segmentation with high accuracy. However, the major limitations for clinically applicable use of ML in glioma segmentation include more than one-third of publications use the same datasets, thus limiting generalizability, increase the likelihood of overfitting, show and lack of ML network description and standardization in accuracy reporting.

NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION

PURPOSE

Generalizability, reproducibility and objectivity are critical elements that need to be considered when translating machine learning models into clinical practice. While a large body of literature has been published on machine learning methods for segmentation of brain tumors, a systematic evaluation of paper quality and reproducibility has not been done. We investigated the use of “Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis” (TRIPOD) items, among papers published in this relatively new and growing field.

METHODS

According to PRISMA a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and a second time in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. The publications were assessed in order to the TRIPOD items.

RESULTS

37 publications from our database search were screened in TRIPOD and yielded an average score of 12.08 with the maximum score being 16 and the minimum score 7. The best scoring item was interpretation (item 19) where all papers scored a point. The lowest scoring items were the title, the abstract, risk groups and the model performance (items number 1, 2, 11 and 16), where no paper scored a point. Less than 1% of the papers discussed the problem of missing data (item 9) and the funding of research (item 22).

CONCLUSION

TRIPOD analysis showed that a majority of the papers do not score high on critical elements that allow reproducibility, translation, and objectivity of research. An average score of 12.08 (40%) indicates that the publications usually achieve a relatively low score. The categories that were consistently poorly described include the ML network description, measuring model performance, title details and inclusion of information into the abstract.

Cost-effectiveness of endovascular thrombectomy in patients with acute stroke and M2 occlusion

BACKGROUND

The cost-effectiveness of endovascular thrombectomy (EVT) in patients with acute ischemic stroke due to M2 branch occlusion remains uncertain.

OBJECTIVE

To evaluate the cost-effectiveness of EVT compared with medical management in patients with acute stroke presenting with M2 occlusion using a decision-analytic model.

METHODS

A decision-analytic study was performed with Markov modeling to estimate the lifetime quality-adjusted life years and associated costs of EVT-treated patients compared with no-EVT/medical management. The study was performed over a lifetime horizon with a societal perspective in the Unites States setting. Base case, one-way, two-way, and probabilistic sensitivity analyses were performed.

RESULTS

EVT was the long-term cost-effective strategy in 93.37% of the iterations in the probabilistic sensitivity analysis, and resulted in difference in health benefit of 1.66 QALYs in the 65-year-old age groups, equivalent to 606 days in perfect health. Varying the outcomes after both strategies shows that EVT was more cost-effective when the probability of good outcome after EVT was only 4-6% higher relative to medical management in clinically likely scenarios. EVT remained cost-effective even when its cost exceeded US$200 000 (threshold was US$209 111). EVT was even more cost-effective for 55-year-olds than for 65-year-old patients.

CONCLUSION

Our study suggests that EVT is cost-effective for treatment of acute M2 branch occlusions. Faster and improved reperfusion techniques would increase the relative cost-effectiveness of EVT even further in these patients.

OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review

Purpose

Medical staging, surgical planning, and therapeutic decisions are significantly different for brain metastases versus gliomas. Machine learning (ML) algorithms have been developed to differentiate these pathologies. We performed a systematic review to characterize ML methods and to evaluate their accuracy.

Methods

Studies on the application of machine learning in neuro-oncology were searched in Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection. A search strategy was designed in compliance with a clinical librarian and confirmed by a second librarian. The search strategy comprised of controlled vocabulary including artificial intelligence, machine learning, deep learning, magnetic resonance imaging, and glioma. The initial search was performed in October 2020 and then updated in February 2021. Candidate articles were screened in Covidence by at least two reviewers each. A bias analysis was conducted in agreement with TRIPOD, a bias assessment tool similar to CLAIM.

Results

Twenty-nine articles were used for data extraction. Four articles specified model development for solitary brain metastases. Classical ML (cML) algorithms represented 85% of models used, while deep learning (DL) accounted for 15%. cML algorithms performed with an average accuracy, sensitivity, and specificity of 82%, 78%, 88%, respectively; DL performed 84%, 79%, 81%. The support vector machine (SVM) algorithm was the most common used cML model in the literature and convolutional neural networks (CNN) were standard for DL models. We also found T1, T1 post-gadolinium and T2 sequences were most commonly used for feature extraction. Preliminary TRIPOD analysis yielded an average score of 14.25 (range 8–18).

Conclusion

ML algorithms that can accurately classify glioma from brain metastases have been developed. SVM and CNN are leading approaches with high accuracy. Standardized algorithm performance reporting is a clear limitation to be addressed in future studies.

Cost-effectiveness of endovascular thrombectomy in patients with low Alberta Stroke Program Early CT Scores (< 6) at presentation

OBJECTIVE

The utility of endovascular thrombectomy (EVT) in patients with acute ischemic stroke, large vessel occlusion (LVO), and low Alberta Stroke Program Early CT Scores (ASPECTS) remains uncertain. The objective of this study was to determine the health outcomes and cost-effectiveness of EVT versus medical management in patients with ASPECTS < 6.

METHODS

A decision-analytical study was performed with Markov modeling to estimate the lifetime quality-adjusted life-years (QALYs) and associated costs of EVT-treated patients compared to medical management. The study was performed over a lifetime horizon with a societal perspective in the US setting.

RESULTS

The incremental cost-effectiveness ratios were $412,411/QALY and $1,022,985/QALY for 55- and 65-year-old groups in the short-term model. EVT was the long-term cost-effective strategy in 96.16% of the iterations and resulted in differences in health benefit of 2.21 QALYs and 0.79 QALYs in the 55- and 65-year-old age groups, respectively, equivalent to 807 days and 288 days in perfect health. EVT remained the more cost-effective strategy when the probability of good outcome with EVT was above 16.8% or as long as the good outcome associated with the procedure was at least 1.6% higher in absolute value than that of medical management. EVT remained cost-effective even when its cost exceeded $100,000 (threshold was $108,036). Although the cost-effectiveness decreased with age, EVT was cost-effective for 75-year-old patients as well.

CONCLUSIONS

This study suggests that EVT is the more cost-effective approach compared to medical management in patients with ASPECTS < 6 in the long term (lifetime horizon), considering the poor outcomes and significant disability associated with nonreperfusion.

Abstract MP13: Polygenic Susceptibility to Atrial Fibrillation is Associated With Silent Cerebrovascular Disease in Stroke-Free Persons Without Atrial Fibrillation

Introduction: Clinically silent cerebrovascular disease is present in 40% of persons over the age of 60. We hypothesize that polygenic susceptibility to atrial fibrillation is associated with the burden of white matter disease in persons without atrial fibrillation or history of ischemic stroke.

Methods: We conducted a nested genetic and neuroimaging study within the UK Biobank, a large cohort study that enrolled community dwelling Britons aged 40 to 65 at recruitment. We used data on a subcohort of patients evaluated with brain MRIs. The volume of white matter hyperintensities (WMH) was estimated using the BIANCA lesion segmentation tool. Genomic data was ascertained via genotyping with the Affymetrix UK Biobank Axiom array followed by imputation with 1000 Genomes reference panels. To model the polygenic susceptibility to atrial fibrillation (AFIB), we constructed a polygenic risk score (PRS) using 957 independent genetic risk variants known to significantly associate with atrial fibrillation. We used logistic and linear regression to test for association between the PRS and WMH.

Results: A total of 38,914 study participants underwent brain MRI imaging in the UK Biobank. Of these, we excluded 124 (0.3%) with a history of stroke and 926 (2.4%) with AFIB. 37,864 study participants were included in this study, of which 19,059 (50.3%) had WMH. High genetic risk of AFIB was not associated with no-versus-any WMH (p=0.51). When evaluating persons with WMH lesions, high genetic risk of AFIB was associated with higher WMH volume (per 1 SD increase of the PRS, beta 0.019, SE 0.006; p=0.01). Gender was an important effect modifier of this association (interaction p=0.03): while high genetic risk of AFIB was associated with a significant increase in WMH volume in females (per 1 SD increase of the PRS, beta 0.03, SE 0.008; p<0.001), no association was found for males (p=0.99).

Conclusions: Polygenic susceptibility to atrial fibrillation is associated with more severe silent cerebrovascular disease in persons without atrial fibrillation. Further research should evaluate whether this genetic information can be used to identify persons for tailored diagnostic or therapeutic interventions.

Outcomes after Thrombectomy for Minor Stroke: A Meta-Analysis

PURPOSE

To determine the outcomes after mechanical thrombectomy (MT) versus medical management in patients with minor stroke symptomatology.

METHODS

A meta-analysis was performed for studies reporting outcomes after MT, either as stand-alone therapy or with intravenous thrombolysis in patients with minor stroke and large-vessel occlusion.

RESULTS

Fourteen studies with 2134 patients met the selection criteria and were included. Two studies compared immediate thrombectomy versus best medical management (with rescue thrombectomy) and the odds ratios of excellent outcomes, good outcomes, mortality and incidence of symptomatic intracranial hemorrhage (sICH) after immediate thrombectomy versus best medical management were 1.07 (95% confidence interval [CI] 0.93-1.22%), 1.15 (95% CI 1.05-1.25), 0.65 (95% CI 0.30-1.38), and 2.89 (95% CI 0.82-10.13), respectively. Among the 8 studies that compared MT outcomes versus medical management (without thrombectomy), odds ratios of excellent outcomes, good outcomes, mortality, and incidence of sICH after MT versus medical management were 0.98 (95% CI 0.89-1.07), 0.94 (95% CI 0.89-1.00), 1.61 (95% CI 1.08-2.41), and 2.59 (95% CI 1.35-4.96), respectively. Among all 14 studies, pooled proportions of excellent outcomes, good outcomes, mortality, and sICH after thrombectomy were 58.7%, 76.2%, 6.82%, and 3.23%, respectively.

CONCLUSIONS

Our study shows significant selection bias and heterogeneity in the literature with differences in baseline characteristics (age, stroke severity, prestroke modified Rankin Scale score, side of infarct, vessel and site of occlusion, use of intravenous thrombolysis, criteria for clinical deterioration, and selection bias for rescue MT and rates of reperfusion), emphasizing the need for a randomized controlled trial.

Abstract WP413: Portable, Bedside, Point of Care Magnetic Resonance Imaging in an Intensive Care Setting for Intracranial Hemorrhage

Background: Radiographic diagnosis of intracranial hemorrhage (ICH) is a critical determinant of stroke care pathways requiring patient transport to a neuroimaging suite. Advances in low-field MRI have made it possible to obtain clinically useful imaging at the point of care (POC).

Aim: The aim of this study was to obtain preliminary data regarding the ability of a bedside POC MRI scanner to detect ICH.

Methods: We studied 36 patients with a diagnosis of ICH (n=18) or ischemic stroke (n=18). Five blinded readers independently evaluated T2W and FLAIR exams acquired prospectively on a 64 mT, portable bedside MRI system (Hyperfine Research, Inc). Kappa coefficients (κ) were calculated to determine inter-rater agreement. Ground truth was obtained from the clinical report of the closest conventional imaging study (17.9 ± 10.4 hours) and verified by a core reader. For each exam, majority consensus among raters was used to determine sensitivity.

Results: ICH volume ranged from 4 to 101 cc (median of 13 cc). Exams were acquired within 7 days of symptom onset (51.1 ± 28.8 hours). A pathologic lesion was identified on every exam with 100% sensitivity. Sensitivity for distinguishing any hemorrhage was 89% and specificity was 83%. The mean sensitivity and specificity for individual raters was 79% and 69%, respectively. When limited to supratentorial hemorrhage, consensus sensitivity was 94%. For ICH cases detected by all raters (n=9), there was 100% accuracy for localizing the bleed (lobar vs. non-lobar) with perfect agreement among raters (κ = 1, p <0.0001). There was substantial agreement for identifying intraventricular hemorrhage (IVH) (κ = 0.72, p < 0.0001). Sensitivity for IVH was 100% based on rater consensus. Figure 1 shows a POC exam with an ICH and IVH.

Conclusions: These data suggest that low-field, POC MRI may be used to detect hemorrhagic stroke at the bedside. Further work is needed to evaluate this approach in the hyperacute setting and across a wide range of ICH characteristics.

Abstract 57: Deployment of Portable, Bedside, Low-Field Magnetic Resonance Imaging for Evaluation of Stroke Patients

Background: Magnetic resonance imaging (MRI) is a powerful modality for diagnosing stroke. Conventionally, patients must travel to the location of a high-field MRI device. Advances in low-field MRI have enabled acquisition of clinically useful images using a portable device at the bedside. The feasibility of using point of care (POC) MRI in a clinical stroke setting is unknown.

Objective: To determine the safety and feasibility of portable, bedside, low-field MRI in a clinical setting.

Design/Methods: POC MRI exams were performed in Yale’s Neuroscience Intensive Care Unit (NICU) from July 2018 to August 2019. Images were acquired at the bedside using a standard 110V, 15A power outlet. The environment included the bedside vitals monitor, ventilators and intravenous infusion pumps. Exams were performed by research staff trained to operate the POC scanner in the absence of a trained MRI technician. No special precautions were necessary to remove ferrous metals from the room. Scan parameters were controlled using a tablet computer interface, and images were available immediately after acquisition.

Results: POC MRI was obtained in 85 stroke cases (46% female, ages 18-96 years, 46% ischemic stroke, 34% intracerebral hemorrhage, 20% subarachnoid hemorrhage). Scans were obtained within 7 days of symptom onset. NIHSS scores ranged from 1 to 29 (median of 7). Of the 85 patients analyzed, 68 underwent T2-weighted imaging, 72 underwent FLAIR imaging, and 39 underwent diffusion weighted imaging (DWI). DWI was only tested in ischemic stroke cases. Patients’ BMI ranged from 20.0 to 46.5 with a median of 26.7. The majority 74 (87%) of patients completed the entire exam. Five patients (6%) were unable to fit in the scanner’s 30 cm opening, while 6 patients (7%) experienced claustrophobia resulting in early termination of the exam. Mean exam time was 28.9 ± 8.4 minutes. The 64 mT static magnetic field, gradient and RF pulses of the POC MRI scanner did not interfere with NICU equipment, and no significant adverse events occurred.

Conclusions: We report the first use of a portable, low-field MRI system to image stroke patients at the bedside. This early work suggests our approach is safe and viable in a complex clinical care environment.

Vessel wall magnetic resonance imaging in intracranial aneurysms: Principles and emerging clinical applications

Intracranial high-resolution vessel wall magnetic resonance imaging is an imaging paradigm that complements conventional imaging modalities used in the evaluation of neurovascular pathology. This review focuses on the emerging utility of vessel wall magnetic resonance imaging in the characterization of intracranial aneurysms. We first discuss the technical principles of vessel wall magnetic resonance imaging highlighting methods to determine aneurysm wall enhancement and how to avoid common interpretive pitfalls. We then review its clinical application in the characterization of ruptured and unruptured intracranial aneurysms, in particular, the emergence of aneurysm wall enhancement as a biomarker of aneurysm instability. We offer our perspective from a high-volume neurovascular center where vessel wall magnetic resonance imaging is in routine clinical use.

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma

PURPOSE

Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians.

METHODS

We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists.

RESULTS

A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 (P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 (P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance.

CONCLUSION

Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.

CT Perfusion in Acute Lacunar Stroke: Detection Capabilities Based on Infarct Location

BACKGROUND AND PURPOSE

Recent studies demonstrated superiority of CTP to NCCT/CTA at detecting lacunar infarcts. This study aimed to assess CTP's capability to identify lacunae in different intracranial regions.

MATERIALS AND METHODS

Over 5.5 years, 1085 CTP examinations were retrospectively reviewed in patients with acute stroke symptoms with CTP within 12 hours and MRI within 7 days of symptom onset. Patients had infarcts ≤2 cm or no acute infarct on DWI; patients with concomitant infarcts >2 cm on DWI were excluded. CTP postprocessing was automated by a delay-corrected algorithm. Three blinded reviewers were given patient NIHSS scores and symptoms; infarcts were recorded based on NCCT/CTA, CTP (CBF, CBV, MTT, and TTP), and DWI.

RESULTS

One hundred thirteen patients met inclusion criteria (53.1% female). On DWI, lacunar infarcts were present in 37 of 113 (32.7%), and absent in 76 of 113 (67.3%). On CTP, lacunar infarcts typically appeared as abnormalities larger than infarct size on DWI. Interobserver κ for CTP ranged from 0.38 (CBF) (P < .0001) to 0.66 (TTP) (P < .0001); interobserver κ for DWI was 0.88 (P < 0.0001). In all intracranial regions, sensitivity of CTP ranged from 18.9% (CBV) to 48.7% (TTP); specificity ranged from 97.4% (CBF and TTP) to 98.7% (CBV and MTT). CTP's sensitivity was highest in the subcortical white matter with or without cortical involvement (21.7%-65.2%) followed by periventricular white matter (12.5%-37.5%); sensitivity in the thalami or basal ganglia was 0%.

CONCLUSIONS

CTP has low sensitivity and high specificity in identifying lacunar infarcts. Sensitivity is highest in the subcortical white matter with or without cortical involvement, but limited in the basal ganglia and thalami.

Acute Ischemic Stroke Infarct Topology: Association with Lesion Volume and Severity of Symptoms at Admission and Discharge

BACKGROUND AND PURPOSE

Acute stroke presentation and outcome depend on both ischemic infarct volume and location. We aimed to determine the association between acute ischemic infarct topology and lesion volume and stroke severity at presentation and discharge.

MATERIALS AND METHODS

Patients with acute ischemic stroke who underwent MR imaging within 24 hours of symptom onset or last seen well were included. Infarcts were segmented and coregistered on the Montreal Neurological Institute-152 brain map. Voxel-based analyses were performed to determine the distribution of infarct lesions associated with larger volumes, higher NIHSS scores at admission and discharge, and greater NIHSS/volume ratios.

RESULTS

A total of 238 patients were included. Ischemic infarcts involving the bilateral lentiform nuclei, insular ribbons, middle corona radiata, and right precentral gyrus were associated with larger infarct volumes (average, 76.7 ± 125.6 mL versus 16.4 ± 24.0 mL, P < .001) and higher admission NIHSS scores. Meanwhile, brain stem and thalami infarctions were associated with higher admission NIHSS/volume ratios. The discharge NIHSS scores were available in 218 patients, in whom voxel-based analysis demonstrated that ischemic infarcts of the bilateral posterior insular ribbons, middle corona radiata, and right precentral gyrus were associated with more severe symptoms at discharge, whereas ischemic lesions of the brain stem, bilateral thalami, and, to a lesser extent, the middle corona radiata were associated with higher ratios of discharge NIHSS score/infarct volume.

CONCLUSIONS

Acute ischemic infarcts of the insulae, lentiform nuclei, and middle corona radiata tend to have larger volumes, more severe presentations, and worse outcomes, whereas brain stem and thalamic infarcts have greater symptom severity relative to smaller lesion volumes.

Malignant CTA collateral profile is highly specific for large admission DWI infarct core and poor outcome in acute stroke

BACKGROUND AND PURPOSE

Large admission DWI lesion volumes are associated with poor outcomes despite acute stroke treatment. The primary aims of our study were to determine whether CTA collaterals correlate with admission DWI lesion volumes in patients with AIS with proximal occlusions, and whether a CTA collateral profile could identify large DWI volumes with high specificity.

MATERIALS AND METHODS

We studied 197 patients with AIS with M1 and/or intracranial ICA occlusions. We segmented admission and follow-up DWI lesion volumes, and categorized CTA collaterals by using a 5-point CS system. ROC analysis was used to determine CS accuracy in predicting DWI lesion volumes >100 mL. Patients were dichotomized into 2 categories: CS = 0 (malignant profile) or CS>0. Univariate and multivariate analyses were performed to compare imaging and clinical variables between these 2 groups.

RESULTS

There was a negative correlation between CS and admission DWI lesion volume (ρ = -0.54, P < .0001). ROC analysis revealed that CTA CS was a good discriminator of DWI lesion volume >100 mL (AUC = 0.84, P < .001). CS = 0 had 97.6% specificity and 54.5% sensitivity for DWI volume >100 mL. CS = 0 patients had larger mean admission DWI volumes (165.8 mL versus 32.7 mL, P < .001), higher median NIHSS scores (21 versus 15, P < .001), and were more likely to become functionally dependent at 3 months (95.5% versus 64.0%, P = .003). Admission NIHSS score was the only independent predictor of a malignant CS (P = .007).

CONCLUSIONS

In patients with AIS with PAOs, CTA collaterals correlate with admission DWI infarct size. A malignant collateral profile is highly specific for large admission DWI lesion size and poor functional outcome.

Location-weighted CTP analysis predicts early motor improvement in stroke: a preliminary study

OBJECTIVE

To develop multivariate models for prediction of early motor deficit improvement in acute stroke patients with focal extremity paresis, using admission clinical and imaging data.

METHODS

Eighty consecutive patients with motor deficit due to first-ever unilateral stroke underwent CT perfusion (CTP) within 9 hours of symptom onset. Limb paresis was prospectively assessed using admission and discharge NIH Stroke Scale (NIHSS) scoring. CTP scans were coregistered to the MNI-152 brain space and subsegmented to 146 pairs of cortical/subcortical regions based on preset atlases. Stepwise multivariate binary logistic regressions were performed to determine independent clinical and imaging predictors of paresis improvement.

RESULTS

The rates of early motor deficit improvement were 18/49 (37%), 15/42 (36%), 8/25 (32%), and 7/23 (30%) for the right arm, right leg, left arm, and left leg, respectively. Admission NIHSS was the only independent clinical predictor of early limb motor deficit improvement. Relative CTP values of the inferior frontal lobe white matter, lower insular cortex, superior temporal gyrus, retrolenticular portion of internal capsule, postcentral gyrus, precuneus parietal gyri, putamen, and caudate nuclei were also independent predictors of motor improvement of different limbs. The multivariate predictive models of motor function improvement for each limb had 84%-92% accuracy, 79%-100% positive predictive value, 75%-94% negative predictive value, 83%-88% sensitivity, and 80%-100% specificity.

CONCLUSIONS

We developed pilot multivariate models to predict early motor functional improvement in acute stroke patients using admission NIHSS and atlas-based location-weighted CTP data. These models serve as a "proof-of-concept" for prospective location-weighted imaging prediction of clinical outcome in acute stroke.

Cranial CT with adaptive statistical iterative reconstruction: improved image quality with concomitant radiation dose reduction

BACKGROUND AND PURPOSE

To safeguard patient health, there is great interest in CT radiation-dose reduction. The purpose of this study was to evaluate the impact of an iterative-reconstruction algorithm, ASIR, on image-quality measures in reduced-dose head CT scans for adult patients.

MATERIALS AND METHODS

Using a 64-section scanner, we analyzed 100 reduced-dose adult head CT scans at 6 predefined levels of ASIR blended with FBP reconstruction. These scans were compared with 50 CT scans previously obtained at a higher routine dose without ASIR reconstruction. SNR and CNR were computed from Hounsfield unit measurements of normal GM and WM of brain parenchyma. A blinded qualitative analysis was performed in 10 lower-dose CT datasets compared with higher-dose ones without ASIR. Phantom data analysis was also performed.

RESULTS

Lower-dose scans without ASIR had significantly lower mean GM and WM SNR (P = .003) and similar GM-WM CNR values compared with higher routine-dose scans. However, at ASIR levels of 20%-40%, there was no statistically significant difference in SNR, and at ASIR levels of ≥60%, the SNR values of the reduced-dose scans were significantly higher (P < .01). CNR values were also significantly higher at ASIR levels of ≥40% (P < .01). Blinded qualitative review demonstrated significant improvements in perceived image noise, artifacts, and GM-WM differentiation at ASIR levels ≥60% (P < .01).

CONCLUSIONS

These results demonstrate that the use of ASIR in adult head CT scans reduces image noise and increases low-contrast resolution, while allowing lower radiation doses without affecting spatial resolution.

CT perfusion mean transit time maps optimally distinguish benign oligemia from true "at-risk" ischemic penumbra, but thresholds vary by postprocessing technique

BACKGROUND AND PURPOSE

Various CTP parameters have been used to identify ischemic penumbra. The purpose of this study was to determine the optimal CTP parameter and threshold to distinguish true "at-risk" penumbra from benign oligemia in acute stroke patients without reperfusion.

MATERIALS AND METHODS

Consecutive stroke patients were screened and 23 met the following criteria: 1) admission scanning within 9 hours of onset, 2) CTA confirmation of large vessel occlusion, 3) no late clinical or radiographic evidence of reperfusion, 4) no thrombolytic therapy, 5) DWI imaging within 3 hours of CTP, and 6) either CT or MR follow-up imaging. CTP was postprocessed with commercial software packages, using standard and delay-corrected deconvolution algorithms. Relative cerebral blood flow, volume, and mean transit time (rCBF, rCBV and rMTT) values were obtained by normalization to the uninvolved hemisphere. The admission DWI and final infarct were transposed onto the CTP maps and receiver operating characteristic curve analysis was performed to determine optimal thresholds for each perfusion parameter in defining penumbra destined to infarct.

RESULTS

Relative and absolute MTT identified penumbra destined to infarct more accurately than CBF or CBV*CBF (P < .01). Absolute and relative MTT thresholds for defining penumbra were 12s and 249% for the standard and 13.5s and 150% for the delay-corrected algorithms, respectively.

CONCLUSIONS

Appropriately thresholded absolute and relative MTT-CTP maps optimally distinguish "at-risk" penumbra from benign oligemia in acute stroke patients with large-vessel occlusion and no reperfusion. The precise threshold values may vary, however, depending on the postprocessing technique used for CTP map construction.

Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans

BACKGROUND AND PURPOSE

Prediction of functional outcome immediately after stroke onset can guide optimal management. Most prognostic grading scales to date, however, have been based on established global metrics such as total NIHSS score, admission infarct volume, or intracranial occlusion on CTA. Our purpose was to construct a more focused, location-weighted multivariate model for the prediction of early aphasia improvement, based not only on traditional clinical and imaging parameters, but also on atlas-based structure/function correlation specific to the clinical deficit, using CT perfusion imaging.

MATERIALS AND METHODS

Fifty-eight consecutive patients with aphasia due to first-time ischemic stroke of the left hemisphere were included. Language function was assessed on the basis of the patients admission and discharge NIHSS scores and clinical records. All patients had brain CTP and CTA within 9 hours of symptom onset. For image analysis, all CTPs were automatically co-registered to MNI-152 brain space and parcellated into mirrored cortical and subcortical regions. Multiple logistic regression analysis was used to find independent imaging and clinical predictors of language recovery.

RESULTS

By the time of discharge, 21 (36%) patients demonstrated improvement of language. Independent factors predicting improvement in language included rCBF of the angular gyrus GM (BA 39) and the lower third of the insular ribbon, proximal cerebral artery occlusion on admission CTA, and aphasia score on the admission NIHSS examination. Using these 4 variables, we developed a multivariate logistic regression model that could estimate the probability of early improvement in aphasia and predict functional outcome with 91% accuracy.

CONCLUSIONS

An imaging-based location-weighted multivariate model was developed to predict early language improvement of patients with aphasia by using admission data collected within 9 hours of stroke onset. This pilot model should be validated in a larger, prospective study; however, the semiautomated atlas-based analysis of brain CTP, along with the statistical approach, could be generalized for prediction of other outcome measures in patients with stroke.

Laparoscopic Antegrade Continent Enema through VQ Stoma Skin Flaps Using Two Ports: Long-Term Follow-Up

PURPOSE

To introduce a simple technique for laparoscopic appendicostomy using two ports through "V" and quadrilateral "Q"-shaped skin flaps to create antegrade continent enemas in children with a neuropathic bowel incontinence or intractable constipation.

PATIENTS AND METHODS

Laparoscopic appendicostomy was performed in 19 children through a V-shaped skin flap at McBurney's point. The first port was inserted into the peritoneal cavity under direct vision, and the second port was inserted after peritoneal insufflation. The appendix was brought to the abdominal surface, and its distal tip was resected and intubated. The spatulated appendix was used to create an anastomosis to the V-shaped skin flap. The appendix was then covered by a quadrilateral skin flap.

RESULTS

All patients were discharged from the hospital within 3 days (range 1-3 days) after surgery with a catheter in place. An irrigation regimen was initiated 3 weeks after surgery. All but one patient became continent without constipation and diaper free. The duration of follow-up ranged from 15 to 54 months (mean 35.8 months).

CONCLUSION

The laparoscopic antegrade continent enema through the VQ stoma skin flaps using two ports ensures rapid recovery, an excellent cosmetic appearance, and minimal complications in long-term follow-up. This is the first report of this technique, which shows promising results in stoma reconstruction.

Naltrexone, an opioid receptor antagonist, attenuates liver fibrosis in bile duct ligated rats

AIM

The aim of this study was to investigate the hypothesis that the opioid system is involved in the development of hepatic fibrosis.

METHODS

The effect of naltrexone (an opioid receptor antagonist) on hepatic fibrosis in bile duct ligated (BDL) or sham rats was assessed by histology and hepatic hydroxyproline levels. Liver matrix metalloproteinase 2 (MMP-2) was measured by zymography, and alpha smooth muscle actin (alpha-SMA) and CD45 (leucocyte common antigen) by immunohistochemistry. The redox state of the liver was assessed by hepatic glutathione (GSH)/oxidised glutathione (GSSG) and S-nitrosothiol levels. Subtypes of opioid receptors in cultured hepatic stellate cells (HSCs) were characterised by reverse transcriptase-polymerase chain reaction, and the effects of selective delta opioid receptor agonists on cellular proliferation, tissue inhibitor of metalloproteinase 1 (TIMP-1), and procollagen I expression in HSCs determined.

RESULTS

Naltrexone markedly attenuated the development of hepatic fibrosis as well as MMP-2 activity (p<0.01), and decreased the number of activated HSCs in BDL rats (p<0.05). The development of biliary cirrhosis altered the redox state with a decreased hepatic GSH/GSSG ratio and increased concentrations of hepatic S-nitrosothiols, which were partially or completely normalised by treatment with naltrexone, respectively. Activated rat HSCs exhibited expression of delta1 receptors, with increased procollagen I expression, and increased TIMP-1 expression in response to delta(1) and delta(2) agonists, respectively.

CONCLUSIONS

This is the first study to demonstrate that administration of an opioid antagonist prevents the development of hepatic fibrosis in cirrhosis. Opioids can influence liver fibrogenesis directly via the effect on HSCs and regulation of the redox sensitive mechanisms in the liver.