Blood pressure-related white matter microstructural disintegrity and associated cognitive function impairment in asymptomatic adults

BACKGROUND AND OBJECTIVES

We aimed to investigate the white matter (WM) microstructural/cytostructural disintegrity patterns related to higher systolic blood pressure (SBP), and whether they mediate SBP effects on cognitive performance in middle-aged adults.

METHODS

Using the UK Biobank study of community-dwelling volunteers aged 40-69 years, we included participants without a history of stroke, dementia, demyelinating disease or traumatic brain injury. We investigated the association of SBP with MRI diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (a measure of neurite density), isotropic (free) water volume fraction (ISOVF) and orientation dispersion across WM tracts. Then, we determined whether WM diffusion metrics mediated the effects of SBP on cognitive function.

RESULTS

We analysed 31 363 participants-mean age of 63.8 years (SD: 7.7), and 16 523 (53%) females. Higher SBP was associated with lower FA and neurite density, but higher MD and ISOVF. Among different WM tracts, diffusion metrics of the internal capsule anterior limb, external capsule, superior and posterior corona radiata were most affected by higher SBP. Among seven cognitive metrics, SBP levels were only associated with 'fluid intelligence' (adjusted p<0.001). In mediation analysis, the averaged FA of external capsule, internal capsule anterior limb and superior cerebellar peduncle mediated 13%, 9% and 13% of SBP effects on fluid intelligence, while the averaged MD of external capsule, internal capsule anterior and posterior limbs, and superior corona radiata mediated 5%, 7%, 7% and 6% of SBP effects on fluid intelligence, respectively.

DISCUSSION

Among asymptomatic adults, higher SBP is associated with pervasive WM microstructure disintegrity, partially due to reduced neuronal count, which appears to mediate SBP adverse effects on fluid intelligence. Diffusion metrics of select WM tracts, which are most reflective of SBP-related parenchymal damage and cognitive impairment, may serve as imaging biomarkers to assess treatment response in antihypertensive trials.

Utility of Gadolinium-Based Contrast in Initial Evaluation of Seizures in Children Presenting Emergently

BACKGROUND AND PURPOSE

The frequency and utility of gadolinium in evaluation of acute pediatric seizure presentation is not well known. The purpose of this study was to assess the utility of gadolinium-based contrast agents in MR imaging performed for the evaluation of acute pediatric seizure presentation.

MATERIALS AND METHODS

We identified consecutive pediatric patients with new-onset seizures from October 1, 2016, to September 30, 2021, who presented to the emergency department and/or were admitted to the inpatient unit and had an MR imaging of the brain for the evaluation of seizures. The clinical and imaging data were recorded, including the patient's age and sex, the use of IV gadolinium, and the underlying cause of epilepsy when available.

RESULTS

A total of 1884 patients were identified for inclusion. Five hundred twenty-four (28%) patients had potential epileptogenic findings on brain MR imaging, while 1153 (61%) patients had studies with normal findings and 207 (11%) patients had nonspecific signal changes. Epileptogenic findings were subclassified as the following: neurodevelopmental lesions, 142 (27%); intracranial hemorrhage (traumatic or germinal matrix), 89 (17%); ischemic/hypoxic, 62 (12%); hippocampal sclerosis, 44 (8%); neoplastic, 38 (7%); immune/infectious, 20 (4%); phakomatoses, 19 (4%); vascular anomalies, 17 (3%); metabolic, 3 (<1%); and other, 90 (17%). Eight hundred seventy-four (46%) patients received IV gadolinium. Of those, only 48 (5%) cases were retrospectively deemed to have necessitated the use of IV gadolinium: Fifteen of 48 (31%) cases were subclassified as immune/infectious, while 33 (69%) were neoplastic. Of the 1010 patients with an initial noncontrast study, 15 (1.5%) required repeat MR imaging with IV contrast to further evaluate the findings.

CONCLUSIONS

Gadolinium contrast is of limited additive benefit in the imaging of patients with an acute onset of pediatric seizures in most instances.

Contrast Agent Administration as a Source of Liability: A Legal Database Analysis

Background Radiology ranks high in terms of specialties implicated in medical malpractice claims. While most radiologists understand the risks of liability for missed findings or lapses of communication, liability for the use of contrast agents in imaging procedures may be underappreciated. Purpose To review the clinical context and outcomes of lawsuits alleging medical malpractice for contrast-related imaging procedures. Materials and Methods Two large U.S. legal databases were queried using the terms "Contrast" and "Radiology OR Radiologist" from database inception to October 31, 2022, to identify cases with published decisions or settlements related to medical malpractice in patients who underwent contrast-related imaging procedures. The search results were screened to include only those cases involving the practice area of health care law where there was at least one claim of medical negligence against a health care institution or provider. Data on the medical complications alleged by patients after contrast agent administration and on the trial were extracted and reported using descriptive statistics. Results A total of 151 published case summaries were included in the analysis. Anaphylactic reaction following contrast agent administration was the most common medical complication observed (30% [45 of 151 cases]), of which failure to diagnose developing anaphylaxis or failure to treat the anaphylactic reaction made up the majority of allegations (93% [42 of 45]). Inappropriate management of contrast media extravasation (27% [41 of 151]) and alleged contrast agent-induced acute kidney injury (13% [19 of 151]) were the next most frequent causes of lawsuits. Of the 11 cases of alleged kidney injury that went to trial, all resulted in a judgment in favor of the defense. Conclusion This study highlights the key reasons for medical malpractice lawsuits associated with use of contrast media and outcomes from these lawsuits. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Trop in this issue.

Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers

Objective

To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH).

Methods

Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance.

Results

A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance.

Conclusion

Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.

American College of Radiology Appropriateness Criteria®: a bibliometric analysis of panel members

OBJECTIVE

To assess the features of panel members involved in the writing of the ACR-AC and identify alignment with research output and topic-specific research publications.

METHODS

A cross-sectional analysis was performed on the research output of panel members of 34 ACR-AC documents published in 2021. For each author, we searched Medline to record total number of papers (P), total number of ACR-AC papers (C) and total number of previously published papers that are relevant to the ACR-AC topic (R).

RESULTS

Three hundred eighty-three different panel members constituted 602 panel positions for creating 34 ACR-AC in 2021 with a median panel size of 17 members. Sixty-eight (17.5%) of experts had been part of ≥10 previously published ACR-AC papers and 154 (40%) were members in ≥ 5 published ACR-AC papers. The median number of previously published papers relevant to the ACR-AC topic was 1 (IQR: 0-5). 44% of the panel members had no previously published paper relevant to the ACR-AC topic. The proportion of ACR-AC papers (C/P) was higher for authors with ≥ 5 ACR-AC papers (0.21) than authors with < 5 ACR-AC papers (0.11, p < 0.0001); however, proportion of relevant papers per topic (R/P) was higher for authors with < 5 ACR-AC papers (0.10) than authors with ≥ 5 ACR-AC papers (0.07).

CONCLUSION

The composition of the ACR Appropriateness Criteria panels reflects many members with little or no previously published literature on the topic of consideration. Similar pool of experts exists on multiple expert panels formulating imaging appropriateness guidelines.

KEY POINTS

There were 68 (17.5%) panel experts on ≥ 10 ACR-AC panels. Nearly 45% of the panel experts had zero median number of relevant papers. Fifteen panels (44%) had > 50% of members having zero relevant papers.

Identification of White Matter Hyperintensities in Routine Emergency Department Visits Using Portable Bedside Magnetic Resonance Imaging

Background White matter hyperintensity (WMH) on magnetic resonance imaging (MRI) of the brain is associated with vascular cognitive impairment, cardiovascular disease, and stroke. We hypothesized that portable magnetic resonance imaging (pMRI) could successfully identify WMHs and facilitate doing so in an unconventional setting. Methods and Results In a retrospective cohort of patients with both a conventional 1.5 Tesla MRI and pMRI, we report Cohen's kappa (κ) to measure agreement for detection of moderate to severe WMH (Fazekas ≥2). In a subsequent prospective observational study, we enrolled adult patients with a vascular risk factor being evaluated in the emergency department for a nonstroke complaint and measured WMH using pMRI. In the retrospective cohort, we included 33 patients, identifying 16 (49.5%) with WMH on conventional MRI. Between 2 raters evaluating pMRI, the interrater agreement on WMH was strong (κ=0.81), and between 1 rater for conventional MRI and the 2 raters for pMRI, intermodality agreement was moderate (κ=0.66, 0.60). In the prospective cohort we enrolled 91 individuals (mean age, 62.6 years; 53.9% men; 73.6% with hypertension), of which 58.2% had WMHs on pMRI. Among 37 Black and Hispanic individuals, the Area Deprivation Index was higher (versus White, 51.8±12.9 versus 37.9±11.9; P<0.001). Among 81 individuals who did not have a standard-of-care MRI in the preceding year, we identified WMHs in 43 of 81 (53.1%). Conclusions Portable, low-field imaging could be useful for identifying moderate to severe WMHs. These preliminary results introduce a novel role for pMRI outside of acute care and the potential role for pMRI to reduce disparities in neuroimaging.

Future of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI

In the 20th century, the advent of neuroimaging dramatically altered the field of neurologic care. However, despite iterative advances since the invention of CT and MRI, little progress has been made to bring MR neuroimaging to the point of care. Recently, the emergence of a low-field (<1 T) portable MRI (pMRI) is setting the stage to revolutionize the landscape of accessible neuroimaging. Users can transport the pMRI into a variety of locations, using a standard 110-220 V wall outlet. In this article, we discuss current applications for pMRI, including in the acute and critical care settings, the barriers to broad implementation, and future opportunities.

Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018

Importance

Aside from widely known cardiovascular implications, higher weight in children may have negative associations with brain microstructure and neurodevelopment.

Objective

To evaluate the association of body mass index (BMI) and waist circumference with imaging metrics that approximate brain health.

Design, Setting, and Participants

This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study to examine the association of BMI and waist circumference with multimodal neuroimaging metrics of brain health in cross-sectional and longitudinal analyses over 2 years. From 2016 to 2018, the multicenter ABCD study recruited more than 11 000 demographically representative children aged 9 to 10 years in the US. Children without any history of neurodevelopmental or psychiatric disorders were included in this study, and a subsample of children who completed 2-year follow-up (34%) was included for longitudinal analysis.

Exposures

Children's weight, height, waist circumference, age, sex, race and ethnicity, socioeconomic status, handedness, puberty status, and magnetic resonance imaging scanner device were retrieved and included in the analysis.

Main Outcomes and Measures

Association of preadolescents' BMI z scores and waist circumference with neuroimaging indicators of brain health: cortical morphometry, resting-state functional connectivity, and white matter microstructure and cytostructure.

Results

A total of 4576 children (2208 [48.3%] female) at a mean (SD) age of 10.0 years (7.6 months) were included in the baseline cross-sectional analysis. There were 609 (13.3%) Black, 925 (20.2%) Hispanic, and 2565 (56.1%) White participants. Of those, 1567 had complete 2-year clinical and imaging information at a mean (SD) age of 12.0 years (7.7 months). In cross-sectional analyses at both time points, higher BMI and waist circumference were associated with lower microstructural integrity and neurite density, most pronounced in the corpus callosum (fractional anisotropy for BMI and waist circumference at baseline and second year: P < .001; neurite density for BMI at baseline: P < .001; neurite density for waist circumference at baseline: P = .09; neurite density for BMI at second year: P = .002; neurite density for waist circumference at second year: P = .05), reduced functional connectivity in reward- and control-related networks (eg, within the salience network for BMI and waist circumference at baseline and second year: P < .002), and thinner brain cortex (eg, for the right rostral middle frontal for BMI and waist circumference at baseline and second year: P < .001). In longitudinal analysis, higher baseline BMI was most strongly associated with decelerated interval development of the prefrontal cortex (left rostral middle frontal: P = .003) and microstructure and cytostructure of the corpus callosum (fractional anisotropy: P = .01; neurite density: P = .02).

Conclusions and Relevance

In this cross-sectional study, higher BMI and waist circumference among children aged 9 to 10 years were associated with imaging metrics of poorer brain structure and connectivity as well as hindered interval development. Future follow-up data from the ABCD study can reveal long-term neurocognitive implications of excess childhood weight. Imaging metrics that had the strongest association with BMI and waist circumference in this population-level analysis may serve as target biomarkers of brain integrity in future treatment trials of childhood obesity.

Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms

There is a rapidly increasing number of artificial intelligence (AI) products cleared by the Food and Drug Administration (FDA) for quantification, identification, and even diagnosis in clinical radiology. This review article aims to summarize the landscape of current commercial software products in neuroimaging and musculoskeletal radiology. We will discuss key applications, provide an overview of current FDA cleared products, and summarize relevant peer reviewed publications of these products when available.

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.

Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children

Objectives

Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.

Methods

From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.

Results

Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.

Conclusion

Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.

Cost-effectiveness of thrombectomy in patients with minor stroke and large vessel occlusion: effect of thrombus location on cost-effectiveness and outcomes

BACKGROUND

To evaluate the cost-effectiveness of endovascular thrombectomy (EVT) to treat large vessel occlusion (LVO) in patients with acute, minor stroke (National Institute of Health Stroke Scale (NIHSS) <6) and impact of occlusion site.

METHODS

A Markov decision-analytic model was constructed accounting for both costs and outcomes from a societal perspective. Two different management strategies were evaluated: EVT and medical management. Base case analysis was done for three different sites of occlusion: proximal M1, distal M1 and M2 occlusions. One-way, two-way and probabilistic sensitivity analyses were performed.

RESULTS

Base-case calculation showed EVT to be the dominant strategy in 65-year-old patients with proximal M1 occlusion and NIHSS <6, with lower cost (US$37 229 per patient) and higher effectiveness (1.47 quality-adjusted life years (QALYs)), equivalent to 537 days in perfect health or 603 days in modified Rankin score (mRS) 0-2 health state. EVT is the cost-effective strategy in 92.7% of iterations for patients with proximal M1 occlusion using a willingness-to-pay threshold of US$100 000/QALY. EVT was cost-effective if it had better outcomes in 2%-3% more patients than intravenous thrombolysis (IVT) in absolute numbers (base case difference -16%). EVT was cost-effective when the proportion of M2 occlusions was less than 37.1%.

CONCLUSIONS

EVT is cost-effective in patients with minor stroke and LVO in the long term (lifetime horizon), considering the poor outcomes and significant disability associated with non-reperfusion. Our study emphasizes the need for caution in interpreting previous observational studies which concluded similar results in EVT versus medical management in patients with minor stroke due to a high proportion of patients with M2 occlusions in the two strategies.

Brain injury patterns in hypoxic ischemic encephalopathy of term neonates

BACKGROUND AND PURPOSE

Topographic patterns of brain injury in neonates can help with differentiation and prognostic categorization of hypoxic ischemic encephalopathy (HIE). In this study, we quantitatively and objectively characterized the location of hypoxic ischemic lesions in term neonates with varying severity of HIE.

METHODS

We analyzed term neonates (born ≥37 postmenstrual gestational weeks) with MRI diffusion-weighted imaging (DWI) and diagnoses of HIE. Neonates' HIE was categorized into mild, moderate, and severe. The hypoxic ischemic lesions were segmented on DWI series with attention to T1- and T2-weighted images and then co-registered onto standard brain space to generate summation maps for each severity category. Applying voxel-wise general linear models, we also identified cerebral regions more likely to infarct with increasing severity of HIE, after correction for lesion volume and time-to-scan as covariates.

RESULTS

We included 33 neonates: 20 with mild, eight with moderate, and five with severe HIE. Infarct volumes (p = .00052) and Appearance, Pulse, Grimace, Activity, and Respiration scores at 1 minute (p = .032) differed between HIE severity categories. Hypoxic ischemic lesions in neonates with mild and moderate HIE were predominant in subcortical and deep white matter along the border zones of arterial supply territories, while severe HIE also involved basal ganglia, hippocampus, and thalamus. In voxel-wise analysis, higher severity of HIE was associated with the presence of lesions in hippocampus, thalamus, and lentiform nucleus.

CONCLUSIONS

In term neonates, mild/moderate HIE is associated with infarctions of arterial territory watershed zones, whereas severe HIE distinctively involves basal ganglia, thalami, and hippocampi.

A 1-Tesla MRI system for dedicated brain imaging in the neonatal intensive care unit

Objective

To assess the feasibility of a point-of-care 1-Tesla MRI for identification of intracranial pathologies within neonatal intensive care units (NICUs).

Methods

Clinical findings and point-of-care 1-Tesla MRI imaging findings of NICU patients (1/2021 to 6/2022) were evaluated and compared with other imaging modalities when available.

Results

A total of 60 infants had point-of-care 1-Tesla MRI; one scan was incompletely terminated due to motion. The average gestational age at scan time was 38.5 ± 2.3 weeks. Transcranial ultrasound (n = 46), 3-Tesla MRI (n = 3), or both (n = 4) were available for comparison in 53 (88%) infants. The most common indications for point-of-care 1-Tesla MRI were term corrected age scan for extremely preterm neonates (born at greater than 28 weeks gestation age, 42%), intraventricular hemorrhage (IVH) follow-up (33%), and suspected hypoxic injury (18%). The point-of-care 1-Tesla scan could identify ischemic lesions in two infants with suspected hypoxic injury, confirmed by follow-up 3-Tesla MRI. Using 3-Tesla MRI, two lesions were identified that were not visualized on point-of-care 1-Tesla scan: (1) punctate parenchymal injury versus microhemorrhage; and (2) small layering IVH in an incomplete point-of-care 1-Tesla MRI with only DWI/ADC series, but detectable on the follow-up 3-Tesla ADC series. However, point-of-care 1-Tesla MRI could identify parenchymal microhemorrhages, which were not visualized on ultrasound.

Conclusion

Although limited by field strength, pulse sequences, and patient weight (4.5 kg)/head circumference (38 cm) restrictions, the Embrace^® point-of-care 1-Tesla MRI can identify clinically relevant intracranial pathologies in infants within a NICU setting.

Dataset on acute stroke risk stratification from CT angiographic radiomics

With advances in high-throughput image processing technologies and increasing availability of medical mega-data, the growing field of radiomics opened the door for quantitative analysis of medical images for prediction of clinically relevant information. One clinical area in which radiomics have proven useful is stroke neuroimaging, where rapid treatment triage is vital for patient outcomes and automated decision assistance tools have potential for significant clinical impact. Recent research, for example, has applied radiomics features extracted from CT angiography (CTA) images and a machine learning framework to facilitate risk-stratification in acute stroke. We here provide methodological guidelines and radiomics data supporting the referenced article "CT angiographic radiomics signature for risk-stratification in anterior large vessel occlusion stroke." The data were extracted from the stroke center registry at Yale New Haven Hospital between 1/1/2014 and 10/31/2020; and Geisinger Medical Center between 1/1/2016 and 12/31/2019. It includes detailed radiomics features of the anterior circulation territories on admission CTA scans in stroke patients with large vessel occlusion stroke who underwent thrombectomy. We also provide the methodological details of the analysis framework utilized for training, optimization, validation and external testing of the machine learning and feature selection algorithms. With the goal of advancing the feasibility and quality of radiomics-based analyses to improve patient care within and beyond the field of stroke, the provided data and methodological support can serve as a baseline for future studies applying radiomics algorithms to machine-learning frameworks, and allow for analysis and utilization of radiomics features extracted in this study.

Age-dependent white matter microstructural disintegrity in autism spectrum disorder

There has been increasing evidence of White Matter (WM) microstructural disintegrity and connectome disruption in Autism Spectrum Disorder (ASD). We evaluated the effects of age on WM microstructure by examining Diffusion Tensor Imaging (DTI) metrics and connectome Edge Density (ED) in a large dataset of ASD and control patients from different age cohorts. N = 583 subjects from four studies from the National Database of Autism Research were included, representing four different age groups: (1) A Longitudinal MRI Study of Infants at Risk of Autism [infants, median age: 7 (interquartile range 1) months, n = 155], (2) Biomarkers of Autism at 12 months [toddlers, 32 (11)m, n = 102], (3) Multimodal Developmental Neurogenetics of Females with ASD [adolescents, 13.1 (5.3) years, n = 230], (4) Atypical Late Neurodevelopment in Autism [young adults, 19.1 (10.7)y, n = 96]. For each subject, we created Fractional Anisotropy (FA), Mean- (MD), Radial- (RD), and Axial Diffusivity (AD) maps as well as ED maps. We performed voxel-wise and tract-based analyses to assess the effects of age, ASD diagnosis and sex on DTI metrics and connectome ED. We also optimized, trained, tested, and validated different combinations of machine learning classifiers and dimensionality reduction algorithms for prediction of ASD diagnoses based on tract-based DTI and ED metrics. There is an age-dependent increase in FA and a decline in MD and RD across WM tracts in all four age cohorts, as well as an ED increase in toddlers and adolescents. After correction for age and sex, we found an ASD-related decrease in FA and ED only in adolescents and young adults, but not in infants or toddlers. While DTI abnormalities were mostly limited to the corpus callosum, connectomes showed a more widespread ASD-related decrease in ED. Finally, the best performing machine-leaning classification model achieved an area under the receiver operating curve of 0.70 in an independent validation cohort. Our results suggest that ASD-related WM microstructural disintegrity becomes evident in adolescents and young adults-but not in infants and toddlers. The ASD-related decrease in ED demonstrates a more widespread involvement of the connectome than DTI metrics, with the most striking differences being localized in the corpus callosum.

The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume

BACKGROUND

Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage.

AIMS

To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage.

METHODS

We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman's Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3.

RESULTS

Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 (p = 0.006), an AUC_[mRS>2] of 0.71 versus 0.68 (p = 0.004), and an AUC_[mRS>3] of 0.71 versus 0.69 (p = 0.029). In multiple regression analysis adjusted for known outcome predictors, the maximum coronal diameter was independently associated with three-month mRS (p < 0.001).

CONCLUSIONS

A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems.

Bedside monitoring of hypoxic ischemic brain injury using low-field, portable brain magnetic resonance imaging after cardiac arrest

BACKGROUND

Assessment of brain injury severity is critically important after survival from cardiac arrest (CA). Recent advances in low-field MRI technology have permitted the acquisition of clinically useful bedside brain imaging. Our objective was to deploy a novel approach for evaluating brain injury after CA in critically ill patients at high risk for adverse neurological outcome.

METHODS

This retrospective, single center study involved review of all consecutive portable MRIs performed as part of clinical care for CA patients between September 2020 and January 2022. Portable MR images were retrospectively reviewed by a blinded board-certified neuroradiologist (S.P.). Fluid-inversion recovery (FLAIR) signal intensities were measured in select regions of interest.

RESULTS

We performed 22 low-field MRI examinations in 19 patients resuscitated from CA (68.4% male, mean [standard deviation] age, 51.8 [13.1] years). Twelve patients (63.2%) had findings consistent with HIBI on conventional neuroimaging radiology report. Low-field MRI detected findings consistent with HIBI in all of these patients. Low-field MRI was acquired at a median (interquartile range) of 78 (40-136) hours post-arrest. Quantitatively, we measured FLAIR signal intensity in three regions of interest, which were higher amongst patients with confirmed HIBI. Low-field MRI was completed in all patients without disruption of intensive care unit equipment monitoring and no safety events occurred.

CONCLUSION

In a critically ill CA population in whom MR imaging is often not feasible, low-field MRI can be deployed at the bedside to identify HIBI. Low-field MRI provides an opportunity to evaluate the time-dependent nature of MRI findings in CA survivors.

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

Age-related topographic map of magnetic resonance diffusion metrics in neonatal brains

Accelerated maturation of brain parenchyma close to term-equivalent age leads to rapid changes in diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) metrics of neonatal brains, which can complicate the evaluation and interpretation of these scans. In this study, we characterized the topography of age-related evolution of diffusion metrics in neonatal brains. We included 565 neonates who had MRI between 0 and 3 months of age, with no structural or signal abnormality-including 162 who had DTI scans. We analyzed the age-related changes of apparent diffusion coefficient (ADC) values throughout brain and DTI metrics (fractional anisotropy [FA] and mean diffusivity [MD]) along white matter (WM) tracts. Rate of change in ADC, FA, and MD values across 5 mm cubic voxels was calculated. There was significant reduction of ADC and MD values and increase of FA with increasing gestational age (GA) throughout neonates' brain, with the highest temporal rates in subcortical WM, corticospinal tract, cerebellar WM, and vermis. GA at birth had significant effect on ADC values in convexity cortex and corpus callosum as well as FA/MD values in corpus callosum, after correcting for GA at scan. We developed online interactive atlases depicting age-specific normative values of ADC (ages 34-46 weeks), and FA/MD (35-41 weeks). Our results show a rapid decrease in diffusivity metrics of cerebral/cerebellar WM and vermis in the first few weeks of neonatal age, likely attributable to myelination. In addition, prematurity and low GA at birth may result in lasting delay in corpus callosum myelination and cerebral cortex cellularity.

CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke

BACKGROUND AND PURPOSE

As "time is brain" in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics.

METHODS

We automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: "Radiomics", "Radiomics + Treatment" (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), "Clinical + Treatment" (baseline clinical variables and treatment), and "Combined" (radiomics, treatment, and baseline clinical variables).

RESULTS

For discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction.

CONCLUSION

Machine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.

Analysis of Clinical Traits Associated With Cardiovascular Health, Genomic Profiles, and Neuroimaging Markers of Brain Health in Adults Without Stroke or Dementia

IMPORTANCE

The American Heart Association (AHA) Life's Simple 7 (LS7) score captures 7 biological and lifestyle factors associated with promoting cardiovascular health.

OBJECTIVES

To test whether healthier LS7 profiles are associated with significant brain health benefits in persons without stroke or dementia, and to evaluate whether genomic information can recapitulate the observed LS7.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study was a nested neuroimaging study within the UK Biobank, a large population-based cohort study in the United Kingdom. Between March 2006 and October 2010, the UK Biobank enrolled 502 480 community-dwelling persons aged 40 to 69 years at recruitment. This study focused on a subset of 35 914 participants without stroke or dementia who completed research brain magnetic resonance imaging (MRI) and had available genome-wide data. All analyses were conducted between March 2021 and March 2022.

EXPOSURES

The LS7 (blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, smoking, exercise, diet, and body mass index) profiles were ascertained clinically and genomically. Independent genetic variants known to influence each of the traits included in the LS7 were assessed. The total LS7 score ranges from 0 (worst) to 14 (best) and was categorized as poor (≤4), average (>4 to 9) and optimal (>9).

MAIN OUTCOMES AND MEASURES

The outcomes of interest were 2 neuroimaging markers of brain health: white matter hyperintensity (WMH) volume and brain volume (BV).

RESULTS

The final analytical sample included 35 914 participants (mean [SD] age 64.1 [7.6] years; 18 830 [52.4%] women). For WMH, compared with persons with poor observed LS7 profiles, those with average profiles had 16% (β = -0.18; SE, 0.03; P < .001) lower mean volume and those with optimal profiles had 39% (β = -0.39; SE, 0.03; P < .001) lower mean volume. Similar results were obtained using the genomic LS7 for WMH (average LS7 profile: β = -0.06; SE, 0.014; P < .001; optimal LS7 profile: β = -0.08; SE, 0.018; P < .001). For BV, compared with persons with poor observed LS7 profiles, those with average LS7 profiles had 0.55% (β = 0.09; SE, 0.02; P < .001) higher volume, and those with optimal LS7 profiles had 1.9% (β = 0.14; SE, 0.02; P < .001) higher volume. The genomic LS7 profiles were not associated with BV.

CONCLUSIONS AND RELEVANCE

These findings suggest that healthier LS7 profiles were associated with better profiles of 2 neuroimaging markers of brain health in persons without stroke or dementia, indicating that cardiovascular health optimization was associated with improved brain health in asymptomatic persons. Genomic information appropriately recapitulated 1 of these associations, confirming the feasibility of modeling the LS7 genomically and pointing to an important role of genetic predisposition in the observed association among cardiometabolic and lifestyle factors and brain health.

Impact of collateral flow on cost-effectiveness of endovascular thrombectomy

OBJECTIVE

Acute ischemic stroke patients with large-vessel occlusion and good collateral blood flow have significantly better outcomes than patients with poor collateral circulation. The purpose of this study was to evaluate the cost-effectiveness of endovascular thrombectomy (EVT) based on collateral status and, in particular, to analyze its effectiveness in ischemic stroke patients with poor collaterals.

METHODS

A decision analysis study was performed with Markov modeling to estimate the lifetime quality-adjusted life-years (QALYs) and associated costs of EVT based on collateral status. The study was performed over a lifetime horizon with a societal perspective in the US setting. Base-case analysis was done for good, intermediate, and poor collateral status. One-way, two-way, and probabilistic sensitivity analyses were performed.

RESULTS

EVT resulted in greater effectiveness of treatment compared to no EVT/medical therapy (2.56 QALYs in patients with good collaterals, 1.88 QALYs in those with intermediate collaterals, and 1.79 QALYs in patients with poor collaterals), which was equivalent to 1050, 771, and 734 days, respectively, in a health state characterized by a modified Rankin Scale (mRS) score of 0-2. EVT also resulted in lower costs in patients with good and intermediate collaterals. For patients with poor collateral status, the EVT strategy had higher effectiveness and higher costs, with an incremental cost-effectiveness ratio (ICER) of $44,326/QALY. EVT was more cost-effective as long as it had better outcomes in absolute numbers in at least 4%-8% more patients than medical management.

CONCLUSIONS

EVT treatment in the early time window for good outcome after ischemic stroke is cost-effective irrespective of the quality of collateral circulation, and patients should not be excluded from thrombectomy solely on the basis of collateral status. Despite relatively lower benefits of EVT in patients with poor collaterals, even smaller differences in better outcomes have significant long-term financial implications that make EVT cost-effective.

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis

Objectives

To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.

Methods

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9.

Results

The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%).

Conclusions

The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice.

Systematic Review Registration

PROSPERO, identifier CRD42020209938.

Brain Tumor Imaging: Applications of Artificial Intelligence

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.