Diffusion-Weighted Imaging Fluid-Attenuated Inversion Recovery Mismatch on Portable, Low-Field Magnetic Resonance Imaging Among Acute Stroke Patients

OBJECTIVE

For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke.

METHODS

Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived.

RESULTS

The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3-14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70-0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5-6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity.

INTERPRETATION

A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset. ANN NEUROL 2024.

Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers

We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.

Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival

The mortality rate of acute intracerebral hemorrhage (ICH) can reach up to 40%. Although the radiomics of ICH have been linked to hematoma expansion and outcomes, no research to date has explored their correlation with mortality. In this study, we determined the admission non-contrast head CT radiomic correlates of survival in supratentorial ICH, using the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) trial dataset. We extracted 107 original radiomic features from n = 871 admission non-contrast head CT scans. The Cox Proportional Hazards model, Kaplan-Meier Analysis, and logistic regression were used to analyze survival. In our analysis, the "first-order energy" radiomics feature, a metric that quantifies the sum of squared voxel intensities within a region of interest in medical images, emerged as an independent predictor of higher mortality risk (Hazard Ratio of 1.64, p < 0.0001), alongside age, National Institutes of Health Stroke Scale (NIHSS), and baseline International Normalized Ratio (INR). Using a Receiver Operating Characteristic (ROC) analysis, "the first-order energy" was a predictor of mortality at 1-week, 1-month, and 3-month post-ICH (all p < 0.0001), with Area Under the Curves (AUC) of >0.67. Our findings highlight the potential role of admission CT radiomics in predicting ICH survival, specifically, a higher "first-order energy" or very bright hematomas are associated with worse survival outcomes.

Accuracy of Financial Disclosures in Radiology Journals

PURPOSE

The accuracy and completeness of self-disclosures of the value of industry payments by authors publishing in radiology journals are not well known. The aim of this study was to assess the accuracy of financial disclosures by US authors in five prominent radiology journals.

METHODS

Financial disclosures provided by US-based authors in five prominent radiology journals from original research and review articles published in 2021 were reviewed. For each author, payment reports were extracted from the Open Payments Database (OPD) in the previous 36 months related to general, research, and ownership payments. Each author was analyzed individually to determine if the reported disclosures matched results from the OPD.

RESULTS

A total of 4,076 authorships, including 3,406 unique authors, were selected from 643 articles across the five journals; 1,388 (1,032 unique authors) received industry payments within the previous 36 months, with a median total amount received per authorship of $6,650 (interquartile range, $355-$87,725). Sixty-one authors (4.4%) disclosed all industry relationships, 205 (14.8%) disclosed some of the OPD-reported relationships, and 1,122 (80.8%) failed to disclose any relationships. Undisclosed payments totaled $186,578,350, representing 67.2% of all payments. Radiology had the highest proportion of authorships disclosing some or all OPD-reported relationships (32.3%), compared with the Journal of Vascular and Interventional Radiology (18.2%), the American Journal of Neuroradiology (17.3%), JACR (13.1%), and the American Journal of Roentgenology (10.3%).

CONCLUSIONS

Financial relationships with industry are common among US physician authors in prominent radiology journals, and nondisclosure rates are high.

Young Professionals in Neuroradiology: Opportunities, Challenges, and Future Directions

The Young Professionals Committee of the American Society of Neuroradiology identifies and serves the interests of young professionals in neuroradiology, defined as those still in training or within 5 years of its completion. Being a young professional is an exciting, dynamic, and demanding stage of one's professional career. As the landscape of neuroradiology practice changes, new opportunities and challenges occur for those in the early stage of their career. It is important to recognize and support the needs of young professionals because an investment in their professional development is an investment in the future of our specialty. In this article, we aimed to address the most notable developments relevant to current and future young professionals in neuroradiology as well as highlight the work done by the Young Professionals Committee of the American Society of Neuroradiology in serving the needs of these young professionals, focusing on early neuroradiology engagement, flexible work arrangements, private practice, social media, artificial intelligence, and international collaborations.

Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke

BACKGROUND

A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics.

METHODS

We extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy.

RESULTS

We included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort.

CONCLUSIONS

Automated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.

Time-Dependent Changes in Hematoma Expansion Rate after Supratentorial Intracerebral Hemorrhage and Its Relationship with Neurological Deterioration and Functional Outcome

BACKGROUND

Hematoma expansion (HE) following an intracerebral hemorrhage (ICH) is a modifiable risk factor and a treatment target. We examined the association of HE with neurological deterioration (ND), functional outcome, and mortality based on the time gap from onset to baseline CT.

METHODS

We included 567 consecutive patients with supratentorial ICH and baseline head CT within 24 h of onset. ND was defined as a ≥4-point increase on the NIH stroke scale (NIHSS) or a ≥2-point drop on the Glasgow coma scale. Poor outcome was defined as a modified Rankin score of 4 to 6 at 3-month follow-up.

RESULTS

The rate of HE was higher among those scanned within 3 h (124/304, 40.8%) versus 3 to 24 h post-ICH onset (53/263, 20.2%) (p < 0.001). However, HE was an independent predictor of ND (p < 0.001), poor outcome (p = 0.010), and mortality (p = 0.003) among those scanned within 3 h, as well as those scanned 3-24 h post-ICH (p = 0.043, p = 0.037, and p = 0.004, respectively). Also, in a subset of 180/567 (31.7%) patients presenting with mild symptoms (NIHSS ≤ 5), hematoma growth was an independent predictor of ND (p = 0.026), poor outcome (p = 0.037), and mortality (p = 0.027).

CONCLUSION

Despite decreasing rates over time after ICH onset, HE remains an independent predictor of ND, functional outcome, and mortality among those presenting >3 h after onset or with mild symptoms.

Impaired mobility and MRI markers of vascular brain injury: Atherosclerosis Risk in Communities and UK Biobank studies

Background

Vascular brain injury (VBI) may be an under-recognised contributor to mobility impairment. We examined associations between MRI VBI biomarkers and impaired mobility.

Methods

We separately analysed Atherosclerosis Risk in Communities (ARIC) and UK Biobank (UKB) study cohorts. Inclusion criteria were no prevalent clinical stroke, and available brain MRI and balance and gait data. MRI VBI biomarkers were (ARIC: ventricular and white matter hyperintensity (WMH) volumes, non-lacunar and lacunar infarctions, microhaemorrhage; UKB: ventricular, brain and WMH volumes, fractional anisotropy (FA), mean diffusivity (MD), intracellular and isotropic free water volume fractions). Quantitative biomarkers were categorised into tertiles. Mobility impairment outcomes were imbalance and slow walk in ARIC and recent fall and slow walk in UKB. Adjusted multivariable logistic regression analyses were performed.

Results

We included 1626 ARIC (mean age 76.2 years; 23.4% imbalance, 25.0% slow walk) and 40 098 UKB (mean age 55 years; 15.8% falls, 2.8% slow walk) participants. In ARIC, imbalance associated with four of five VBI measures (all p values<0.05), most strongly with WMH (adjusted OR, aOR 1.64; 95% CI 1.18 to 2.29). Slow walk associated with four of five VBI measures, most strongly with WMH (aOR 2.32; 95% CI 1.66 to 3.24). In UKB, falls associated with all VBI measures except WMH, most strongly with FA (aOR 1.16; 95% CI 1.08 to 1.24). Slow walking associated with WMH, FA and MD, most strongly with FA (aOR 1.57; 95% CI 1.32 to 1.87).

Conclusions

VBI is associated with mobility impairment in community-dwelling, clinically stroke-free cohorts. Consequences of VBI may extend beyond clinically apparent stroke to include mobility.

Association of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank

BACKGROUND AND OBJECTIVES

Poor oral health is a modifiable risk factor that is associated with clinically observed cardiovascular disease. However, the relationship between oral and brain health is not well understood. We tested the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in middle-aged persons without stroke or dementia.

METHODS

We performed a 2-stage cross-sectional neuroimaging study using UK Biobank data. 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. 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 linked to increased composite risk of dentures or teeth that are decayed, missing, or filled. Neuroimaging markers of brain health included white matter hyperintensity (WMH) volume and aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), 2 metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging across 48 brain regions.

RESULTS

We included 40,175 persons (mean age 55 years, female sex 53%) enrolled from 2006 to 2010, who underwent a dedicated research brain MRI between 2014 and 2016. Among participants, 5,470 (14%) had poor oral health. Poor oral health was associated with a 9% increase in WMH volume (β = 0.09, SD = 0.014, p < 0.001), 10% change in aggregate FA score (β = 0.10, SD = 0.013, p < 0.001), and 5% change in aggregate MD score (β = 0.05, SD = 0.013, p < 0.001). Genetically determined poor oral health was associated with a 30% increase in WMH volume (β = 0.30, SD = 0.06, p < 0.001), 43% change in aggregate FA score (β = 0.43, SD = 0.06, p < 0.001), and 10% change in aggregate MD score (β = 0.10, SD = 0.03, p < 0.01).

DISCUSSION

Among middle age Britons without stroke or dementia, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potentially causal association. Because the neuroimaging markers evaluated in this study precede and are established risk factors of stroke and dementia, our results suggest that oral health, an easily modifiable process, may be a promising target for very early interventions focused on improving brain health.

Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage

INTRODUCTION

Perihematomal edema (PHE) represents secondary brain injury and a potential treatment target in intracerebral hemorrhage (ICH). However, studies differ on optimal PHE volume metrics as prognostic factor(s) after spontaneous, non-traumatic ICH. This study examines associations of baseline and 24-h PHE shape features with 3-month outcomes.

PATIENTS AND METHODS

We included 796 patients from a multicentric trial dataset and manually segmented ICH and PHE on baseline and follow-up CTs, extracting 14 shape features. We explored the association of baseline, follow-up, difference (baseline/follow-up) and temporal rate (difference/time gap) of PHE shape changes with 3-month modified Rankin Score (mRS) - using Spearman correlation. Then, using multivariable analysis, we determined if PHE shape features independently predict outcome adjusting for patients' age, sex, NIH stroke scale (NIHSS), Glasgow Coma Scale (GCS), and hematoma volume.

RESULTS

Baseline PHE maximum diameters across various planes, main axes, volume, surface, and sphericity correlated with 3-month mRS adjusting for multiple comparisons. The 24-h difference and temporal change rates of these features had significant association with outcome - but not the 24-h absolute values. In multivariable regression, baseline PHE shape sphericity (OR = 2.04, CI = 1.71-2.43) and volume (OR = 0.99, CI = 0. 98-1.0), alongside admission NIHSS (OR = 0.86, CI = 0.83-0.88), hematoma volume (OR = 0.99, CI = 0. 99-1.0), and age (OR = 0.96, CI = 0.95-0.97) were independent predictors of favorable outcomes.

CONCLUSION

In acute ICH patients, PHE shape sphericity at baseline emerged as an independent prognostic factor, with a less spherical (more irregular) shape associated with worse outcome. The PHE shape features absolute values over the first 24 h provide no added prognostic value to baseline metrics.

Suboptimal Sleep Duration Is Associated With Poorer Neuroimaging Brain Health Profiles in Middle-Aged Individuals Without Stroke or Dementia

BACKGROUND

The American Heart Association's Life's Simple 7, a public health construct capturing key determinants of cardiovascular health, became the Life's Essential 8 after the addition of sleep duration. The authors tested the hypothesis that suboptimal sleep duration is associated with poorer neuroimaging brain health profiles in asymptomatic middle-aged adults.

METHODS AND RESULTS

The authors conducted a prospective magnetic resonance neuroimaging study in middle-aged individuals without stroke or dementia 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 included the presence of white matter hyperintensities (WMHs), volume of WMH, and fractional anisotropy, with the latter evaluated as the average of 48 white matter tracts. Multivariable logistic and linear regression models were used to test for an association between sleep duration and these neuroimaging markers. The authors evaluated 39 771 middle-aged individuals. Of these, 28 912 (72.7%) had optimal, 8468 (21.3%) had short, and 2391 (6%) had long sleep duration. Compared with optimal sleep, short sleep was associated with higher risk of WMH presence (odds ratio, 1.11 [95% CI, 1.05-1.18]; P<0.001), larger WMH volume (beta=0.06 [95% CI, 0.04-0.08]; P<0.001), and worse fractional anisotropy profiles (beta=-0.04 [95% CI, -0.06 to -0.02]; P=0.001). Compared with optimal sleep, long sleep duration was associated with larger WMH volume (beta=0.04 [95% CI, 0.01-0.08]; P=0.02) and worse fractional anisotropy profiles (beta=-0.06 [95% CI, -0.1 to -0.02]; P=0.002), but not with WMH presence (P=0.6).

CONCLUSIONS

Among middle-aged adults without stroke or dementia, suboptimal sleep duration is associated with poorer neuroimaging brain health profiles. Because these neuroimaging markers precede stroke and dementia by several years, these findings are consistent with other findings evaluating early interventions to improve this modifiable risk factor.

Early brain microstructural development among preterm infants requiring caesarean section versus those delivered vaginally

It is known that the rate of caesarean section (C-section) has been increasing among preterm births. However, the relationship between C-section and long-term neurological outcomes is unclear. In this study, we utilized diffusion tensor imaging (DTI) to characterize the association of delivery method with brain white matter (WM) microstructural integrity in preterm infants. We retrospectively analyzed the DTI scans and health records of preterm infants without neuroimaging abnormality on pre-discharge term-equivalent MRI. We applied both voxel-wise and tract-based analyses to evaluate the association between delivery method and DTI metrics across WM tracts while controlling for numerous covariates. We included 68 preterm infants in this study (23 delivered vaginally, 45 delivered via C-section). Voxel-wise and tract-based analyses revealed significantly lower fractional anisotropy values and significantly higher diffusivity values across major WM tracts in preterm infants delivered via C-section when compared to those delivered vaginally. These results may be partially, but not entirely, mediated by lower birth weight among infants delivered by C-section. Nevertheless, these infants may be at risk for delayed neurodevelopment and could benefit from close neurological follow up for early intervention and mitigation of adverse long-term outcomes.

Distribution and Disparities of Industry Payments to Radiologists (2016-2020)

BACKGROUND

The frequency, magnitude, and distribution of industry payments to radiologists are not well understood.

RATIONALE AND OBJECTIVES

The aim of this study was to analyze the distribution of industry payments to physicians working in diagnostic radiology, interventional radiology, and radiation oncology, study the categories of payments and determine their correlation.

MATERIALS AND METHODS

The Open Payments Database from the Centers for Medicare & Medicaid Services was accessed and analyzed for the period from January 1, 2016 to December 31, 2020. Payments were grouped into six categories: consulting fees, education, gifts, research, speaker fees, and royalties/ownership. The total amount and types of industry payments going to the top 5% group were determined overall and for each category of payment.

RESULTS

From 2016 to 2020, a total of 513 020 payments, amounting to $370 782 608, were made to 28 739 radiologists suggesting that approximately 70% of the 41 000 radiologists in the US received at least one industry payment during the 5-year period. The median payment value was $27 (IQR: $15-$120) and the median number of payments per physician over the 5-year period was 4 (IQR: 1-13). Gifts were the most frequent payment type made (76.4%), but accounted for only 4.8% of payment value. The median total value of payments earned by members of the top 5% group over the 5-year period was $58 878 (IQR: $29 686-$162 425) ($11 776 per year) compared to $172 (IQR: $49-877) ($34 per year) in the bottom 95% group. Members of the top 5% group received a median of 67 (IQR: 26-147) individual payments (13 payments per year) while members of the bottom 95% group received a median of 3 (IQR: 1-11) (0.6 payments per year).

CONCLUSION

Between 2016 and 2020, industry payments to radiologists were highly concentrated both in terms of number/frequency and value of payments.

Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging

PURPOSE

The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grown in the past decade. The adoption of these algorithms into clinical practice depends largely on whether this technology provides value in the clinical setting. The objective of this study was to analyze trends in FDA-cleared AI algorithms for neuroimaging and understand their value proposition as advertised by the AI developers and vendors.

METHODS

A list of AI algorithms cleared by the FDA for neuroimaging between May 2008 and August 2022 was extracted from the ACR Data Science Institute AI Central database. Product information for each device was collected from the database. For each device, information on the advertised value as presented on the developer's website was collected.

RESULTS

A total of 59 AI neuroimaging algorithms were cleared by the FDA between May 2008 and August 2022. Most of these algorithms (24 of 59) were compatible with noncontrast CT, 21 with MRI, 9 with CT perfusion, 8 with CT angiography, 3 with MR perfusion, and 2 with PET. Six algorithms were compatible with multiple imaging techniques. Of the 59 algorithms, websites were located that discussed the product value for 55 algorithms. The most widely advertised value proposition was improved quality of care (38 of 55 [69.1%]). A total of 24 algorithms (43.6%) proposed saving user time, 9 (15.7%) advertised decreased costs, and 6 (10.9%) described increased revenue. Product websites for 26 algorithms (43.6%) showed user testimonials advertising the value of the technology.

CONCLUSIONS

The results of this study indicate a wide range of value propositions advertised by developers and vendors of AI algorithms for neuroimaging. Most vendors advertised that their products would improve patient care. Further research is necessary to determine whether the value claimed by developers is actually demonstrated in clinical practice.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes

Introduction

Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan.

Methods

We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds.

Results

In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults.

Discussion

We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

Diversity in Radiology Residents Relative to Other Specialties- Trends Over the Past Decade

RATIONALE AND OBJECTIVES

The objective of this study was to assess diversity among radiology residents relative to other specialties and compare it with historical trends.

MATERIALS AND METHODS

The Graduate Medical Education results from 2010-2011 to 2020-2021 were accessed for demographic information for major medical specialties (number of residents > 500 as of the 2020-2021 report). Subspecialties and fellowship programs were not included in this analysis. The racial and ethnicity breakdowns were extracted, including Black, White/Caucasian, Asian, Hispanic, and others. The changes in racial and ethnicity composition of residents in radiology was compared to other specialties using the Chi Squared test using a significance level of p < 0.05.

RESULTS

In 2020-2021, radiology ranked ninth in total resident enrollment among the 21 largest ACGME training programs, unchanged when compared to 2010-2011. Amongst all specialties, Radiology ranked 10th for Black and 9th for Hispanic representation in 2020-2021.The percentage of Black residents increased from 3.07% in 2010-2011 to 3.83% in 2020-2021. The percentage of Hispanic Radiology residents increased from 4.83% to 7.35%, constituting the third largest increase amongst all specialties.

CONCLUSION

The representation of Blacks and Hispanics in Radiology has improved relative to other medical specialties in the last decade.

Impact of postnatal weight gain on brain white matter maturation in very preterm infants

BACKGROUND AND PURPOSE

Very preterm infants (VPIs, <32 weeks gestational age at birth) are prone to long-term neurological deficits. While the effects of birth weight and postnatal growth on VPIs' neurological outcome are well established, the neurobiological mechanism behind these associations remains elusive. In this study, we utilized diffusion tensor imaging (DTI) to characterize how birth weight and postnatal weight gain influence VPIs' white matter (WM) maturation.

METHODS

We included VPIs with complete birth and postnatal weight data in their health record, and DTI scan as part of their predischarge Magnetic Resonance Imaging (MRI). We conducted voxel-wise general linear model and tract-based regression analyses to explore the impact of birth weight and postnatal weight gain on WM maturation.

RESULTS

We included 91 VPIs in our analysis. After controlling for gestational age at birth and time between birth and scan, higher birth weight Z-scores were associated with DTI markers of more mature WM tracts, most prominently in the corpus callosum and sagittal striatum. The postnatal weight Z-score changes over the first 4 weeks of life were also associated with increased maturity in these WM tracts, when controlling for gestational age at birth, birth weight Z-score, and time between birth and scan.

CONCLUSIONS

In VPIs, birth weight and post-natal weight gain are associated with markers of brain WM maturation, particularly in the corpus callosum, which can be captured on discharge MRI. These neuroimaging metrics can serve as potential biomarkers for the early effects of nutritional interventions on VPIs' brain development.

Clinical implications of Peri-hematomal edema microperfusion fraction in intracerebral hemorrhage intravoxel incoherent motion imaging - A pilot study

BACKGROUND AND PURPOSE

Perihematomal edema (PHE) represents the secondary brain injury after intracerebral hemorrhage (ICH). However, neurobiological characteristics of post-ICH parenchymal injury other than PHE volume have not been fully characterized. Using intravoxel incoherent motion imaging (IVIM), we explored the clinical correlates of PHE diffusion and (micro)perfusion metrics in subacute ICH.

MATERIALS AND METHODS

In 41 consecutive patients scanned 1-to-7 days after supratentorial ICH, we determined the mean diffusion (D), pseudo-diffusion (D*), and perfusion fraction (F) within manually segmented PHE. Using univariable and multivariable statistics, we evaluated the relationship of these IVIM metrics with 3-month outcome based on the modified Rankin Scale (mRS).

RESULTS

In our cohort, the average (± standard deviation) age of patients was 68.6±15.6 years, median (interquartile) baseline National Institute of Health Stroke Scale (NIHSS) was 7 (3-13), 11 (27 %) patients had poor outcomes (mRS>3), and 4 (10 %) deceased during the follow-up period. In univariable analyses, admission NIHSS (p < 0.001), ICH volume (p = 0.019), ICH+PHE volume (p = 0.016), and average F of the PHE (p = 0.005) had significant correlation with 3-month mRS. In multivariable model, the admission NIHSS (p = 0.006) and average F perfusion fraction of the PHE (p = 0.003) were predictors of 3-month mRS.

CONCLUSION

The IVIM perfusion fraction (F) maps represent the blood flow within microvasculature. Our pilot study shows that higher PHE microperfusion in subacute ICH is associated with worse outcomes. Once validated in larger cohorts, IVIM metrics may provide insight into neurobiology of post-ICH secondary brain injury and identify at-risk patients who may benefit from neuroprotective therapy.

Early brain microstructural development among preterm infants requiring caesarean section versus those delivered vaginally

It is known that the rate of caesarean section (C-section) has been increasing among preterm births. However, the relationship between C-section and long-term neurological outcomes is unclear. In this study, we utilized diffusion tensor imaging (DTI) to characterize the association of delivery method with brain white matter (WM) microstructural integrity in preterm infants. We retrospectively analyzed the DTI scans and health records of preterm infants without neuroimaging abnormality on pre-discharge term-equivalent MRI. We applied both voxel-wise and tract-based analyses to evaluate the association between delivery method and DTI metrics across WM tracts while controlling for numerous covariates. We included 68 preterm infants in this study (23 delivered vaginally, 45 delivered via C-section). Voxel-wise and tract-based analyses revealed significantly lower fractional anisotropy values and significantly higher diffusivity values across major WM tracts in preterm infants delivered via C-section when compared to those delivered vaginally. These results may be partially, but not entirely, mediated by lower birth weight among infants delivered by C-section. Nevertheless, these infants may be at risk for delayed neurodevelopment and could benefit from close neurological follow up for early intervention and mitigation of adverse long-term outcomes.

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.

Radiomics: A Primer on Processing Workflow and Analysis

Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.