Longitudinal Alterations of Cerebral Blood Flow in High-Contact Sports.

OBJECTIVE: Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects. We investigated whether exposure to head impacts causes longitudinal CBF changes. METHODS: We prospectively studied 63 American football (high-contact cohort) and 34 volleyball (low-contact controls) male collegiate athletes, tracking CBF using 3D pseudocontinuous arterial spin labeling magnetic resonance imaging for up to 4 years. Regional relative CBF (rCBF, normalized to cerebellar CBF) was computed after co-registering to T1-weighted images. A linear mixed effects model assessed the relationship of rCBF to sport, time, and their interaction. Within football players, we modeled rCBF against position-based head impact risk and baseline Standardized Concussion Assessment Tool score. Additionally, we evaluated early (1-5 days) and delayed (3-6 months) post-concussion rCBF changes (in-study concussion). RESULTS: Supratentorial gray matter rCBF declined in football compared with volleyball (sport-time interaction p = 0.012), with a strong effect in the parietal lobe (p = 0.002). Football players with higher position-based impact-risk had lower occipital rCBF over time (interaction p = 0.005), whereas players with lower baseline Standardized Concussion Assessment Tool score (worse performance) had relatively decreased rCBF in the cingulate-insula over time (interaction effect p = 0.007). Both cohorts showed a left-right rCBF asymmetry that decreased over time. Football players with an in-study concussion showed an early increase in occipital lobe rCBF (p = 0.0166). INTERPRETATION: These results suggest head impacts may result in an early increase in rCBF, but cumulatively a long-term decrease in rCBF. ANN NEUROL 2023;94:457-469.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.

PURPOSE: The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions. METHODS: In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report. RESULTS: A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08). CONCLUSION: The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION: In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Trends in stroke-related journals: Examination of publication patterns using topic modeling.

OBJECTIVES: This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. MATERIALS AND METHODS: Our study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. RESULTS: Out of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. CONCLUSIONS: The methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata.

Hypoperfusion Intensity Ratio Is Correlated With the Risk of Parenchymal Hematoma After Endovascular Stroke Treatment.

BACKGROUND: Parenchymal hematoma (PH) is a major complication after endovascular treatment (EVT) for ischemic stroke. The hypoperfusion intensity ratio (HIR) represents a perfusion parameter reflecting arterial collateralization and cerebral microperfusion in ischemic brain tissue. We hypothesized that HIR correlates with the risk of PH after EVT. METHODS: Retrospective multicenter cohort study of patients with large vessel occlusion who underwent EVT between 2013 and 2021 at one of the 2 comprehensive stroke centers (University Medical Center Hamburg-Eppendorf, Germany and Stanford University School of Medicine, CA). HIR was automatically calculated on computed tomography perfusion studies as the ratio of brain volume with time-to-max (Tmax) delay >10 s over volume with Tmax >6 s. Reperfusion hemorrhages were assessed according to the Heidelberg Bleeding Classification. Primary outcome was PH occurrence (PH+) or absence (PH-) on follow-up imaging. Secondary outcome was good clinical outcome defined as a 90-day modified Rankin Scale score of 0 to 2. RESULTS: A total of 624 patients met the inclusion criteria. We observed PH in 91 (14.6%) patients after EVT. PH+ patients had higher HIR on admission compared with PH- patients (median, 0.6 versus 0.4; P<0.001). In multivariable regression, higher admission blood glucose (adjusted odds ratio [aOR], 1.08 [95% CI, 1.04-1.13]; P<0.001), extensive baseline infarct defined as Alberta Stroke Program Early CT Score ≤5 (aOR, 2.48 [1.37-4.42]; P=0.002), and higher HIR (aOR, 1.22 [1.09-1.38]; P<0.001) were independent determinants of PH after EVT. Both higher HIR (aOR, 0.83 [0.75-0.92]; P<0.001) and PH on follow-up imaging (aOR, 0.39 [0.18-0.80]; P=0.013) were independently associated with lower odds of achieving good clinical outcome. CONCLUSIONS: Poorer (higher) HIR on admission perfusion imaging was strongly associated with PH occurrence after EVT. HIR as a surrogate for cerebral microperfusion might reflect tissue vulnerability for reperfusion hemorrhages. This automated and quickly available perfusion parameter might help to assess the need for intensive medical care after EVT.

Diagnosis, Workup, Risk Reduction of Transient Ischemic Attack in the Emergency Department Setting: A Scientific Statement From the American Heart Association.

At least 240 000 individuals experience a transient ischemic attack each year in the United States. Transient ischemic attack is a strong predictor of subsequent stroke. The 90-day stroke risk after transient ischemic attack can be as high as 17.8%, with almost half occurring within 2 days of the index event. Diagnosing transient ischemic attack can also be challenging given the transitory nature of symptoms, often reassuring neurological examination at the time of evaluation, and lack of confirmatory testing. Limited resources, such as imaging availability and access to specialists, can further exacerbate this challenge. This scientific statement focuses on the correct clinical diagnosis, risk assessment, and management decisions of patients with suspected transient ischemic attack. Identification of high-risk patients can be achieved through use of comprehensive protocols incorporating acute phase imaging of both the brain and cerebral vasculature, thoughtful use of risk stratification scales, and ancillary testing with the ultimate goal of determining who can be safely discharged home from the emergency department versus admitted to the hospital. We discuss various methods for rapid yet comprehensive evaluations, keeping resource-limited sites in mind. In addition, we discuss strategies for secondary prevention of future cerebrovascular events using maximal medical therapy and patient education.

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.

BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict ordinal 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by fusing a Deep Learning model of diffusion-weighted imaging images and clinical information from the acute period. METHODS: A total of 640 acute ischemic stroke patients who underwent magnetic resonance imaging within 1 to 7 days poststroke and had 90-day mRS follow-up data were randomly divided into 70% (n=448) for model training, 15% (n=96) for validation, and 15% (n=96) for internal testing. Additionally, external testing on a cohort from Lausanne University Hospital (n=280) was performed to further evaluate model generalization. Accuracy for ordinal mRS, accuracy within ±1 mRS category, mean absolute prediction error, and determination of unfavorable outcome (mRS score >2) were evaluated for clinical only, imaging only, and 2 fused clinical-imaging models. RESULTS: The fused models demonstrated superior performance in predicting ordinal mRS score and unfavorable outcome in both internal and external test cohorts when compared with the clinical and imaging models. For the internal test cohort, the top fused model had the highest area under the curve of 0.92 for unfavorable outcome prediction and the lowest mean absolute error (0.96 [95% CI, 0.77-1.16]), with the highest proportion of mRS score predictions within ±1 category (79% [95% CI, 71%-88%]). On the external Lausanne University Hospital cohort, the best fused model had an area under the curve of 0.90 for unfavorable outcome prediction and outperformed other models with an mean absolute error of 0.90 (95% CI, 0.79-1.01), and the highest percentage of mRS score predictions within ±1 category (83% [95% CI, 78%-87%]). CONCLUSIONS: A Deep Learning-based imaging model fused with clinical variables can be used to predict 90-day stroke outcome with reduced subjectivity and user burden.

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.

Benefit of Intravenous Alteplase before Thrombectomy Depends on ASPECTS.

PURPOSE: Baseline variables could be used to guide the administration of additional intravenous alteplase (IVT) before mechanical thrombectomy (MT). The aim of this study was to determine how baseline imaging and demographic parameters modify the effect of IVT on clinical outcomes in patients with ischemic stroke due to large vessel occlusion. METHODS: Multicenter retrospective cohort study of ischemic stroke patients triaged by multimodal-CT undergoing MT treatment after direct admission to an MT-eligible center. Inverse-probability weighting analysis (IPW) was used to assess the treatment effect of IVT adjusted for baseline variables. Multivariable logistic regression analysis with IPW-weighting and interaction terms for IVT was performed to predict functional independence (mRS 0-2 at 90-days). RESULTS: 720 patients were included, of which 366 (51%) received IVT. In IPW, the treatment effect of IVT on outcome (mRS 0-2) distinctively varied according to the ASPECTS subgroup (ASPECTS 9-10: +15%, ASPECTS 6-8: +7%, ASPECTS <6: -11%). In multivariable logistic regression analysis, IVT was independently associated with functional independence (aOR: 1.57, 95% CI: 1.16-2.14, p = 0.003) and the interaction term was significant for ASPECTS and IVT revealing that IVT was only significantly associated with better outcomes in patients with higher ASPECTS. No other significant baseline variable interaction terms were identified. INTERPRETATION: ASPECTS was the only baseline variable that showed a significant interaction with IVT for outcome prediction. Use of IVT prior to MT in patients with an ASPECTS of <6 was not associated with a treatment benefit and should be considered carefully. ANN NEUROL 2022;92:588-595.

Correlating volumetric and linear measurements of brain metastases on MRI scans using intelligent automation software: a preliminary study.

PURPOSE: The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) working group proposed a guide for treatment responses for BMs by utilizing the longest diameter; however, despite recognizing that many patients with BMs have sub-centimeter lesions, the group referred to these lesions as unmeasurable due to issues with repeatability and interpretation. In light of RANO-BM recommendations, we aimed to correlate linear and volumetric measurements in sub-centimeter BMs on contrast-enhanced MRI using intelligent automation software. METHODS: In this retrospective study, patients with BMs scanned with MRI between January 1, 2018, and December 31, 2021, were screened. Inclusion criteria were: (1) at least one sub-centimeter BM with an integer millimeter-longest diameter was noted in the MRI report; (2) patients were a minimum of 18 years of age; (3) patients with available pre-treatment three-dimensional T1-weighted spoiled gradient-echo MRI scan. The screening was terminated when there were 20 lesions in each group. Lesion volumes were measured with the help of intelligent automation software Jazz (AI Medical, Zollikon, Switzerland) by two readers. The Kruskal-Wallis test was used to compare volumetric differences. RESULTS: Our study included 180 patients. The agreement for volumetric measurements was excellent between the two readers. The volumes of the following groups were not significantly different: 1-2 mm, 1-3 mm, 1-4 mm, 2-3 mm, 2-4 mm, 3-4 mm, 3-5 mm, 4-5 mm, 5-6 mm, 5-7 mm, 6-7 mm, 6-8 mm, 6-9 mm, 7-8 mm, 7-9 mm, 8-9 mm. CONCLUSION: Our findings indicate that the largest diameter of a lesion may not accurately represent its volume. Additional research is required to determine which method is superior for measuring radiologic response to therapy and which parameter correlates best with clinical improvement or deterioration.

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques. MATERIAL AND METHODS: We conducted a multicenter, retrospective diagnostic study (March 2013-May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications. RESULTS: We included 790 patients (median age 72, IQR [61-80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63-76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58-0.78; p < .001) and sensitivity 80% (79-81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients. CONCLUSION: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy. CLINICAL RELEVANCE: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients. KEY POINTS: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.

Unfavorable cerebral venous outflow is associated with futile recanalization in acute ischemic stroke patients.

BACKGROUND AND PURPOSE: Mechanical thrombectomy (MT) has proven to be the standard of care for patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO). However, high revascularization rates do not necessarily result in favorable functional outcomes. We aimed to investigate imaging biomarkers associated with futile recanalization, defined as unfavorable functional outcome despite successful recanalization in AIS-LVO patients. METHODS: A retrospective multicenter cohort study was made of AIS-LVO patients treated by MT. Successful recanalization was defined as modified Thrombolysis in Cerebral Infarction score of 2b-3. A modified Rankin Scale score of 3-6 at 90 days was defined as unfavorable functional outcome. Cortical Vein Opacification Score (COVES) was used to assess venous outflow (VO), and the Tan scale was utilized to determine pial arterial collaterals on admission computed tomography angiography (CTA). Unfavorable VO was defined as COVES ≤ 2. Multivariable regression analysis was performed to investigate vascular imaging factors associated with futile recanalization. RESULTS: Among 539 patients in whom successful recanalization was achieved, unfavorable functional outcome was observed in 59% of patients. Fifty-eight percent of patients had unfavorable VO, and 31% exhibited poor pial arterial collaterals. In multivariable regression, unfavorable VO was a strong predictor (adjusted odds ratio = 4.79, 95% confidence interval = 2.48-9.23) of unfavorable functional outcome despite successful recanalization. CONCLUSIONS: We observe that unfavorable VO on admission CTA is a strong predictor of unfavorable functional outcomes despite successful vessel recanalization in AIS-LVO patients. Assessment of VO profiles could help as a pretreatment imaging biomarker to determine patients at risk for futile recanalization.

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). METHODS: This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements. RESULTS: One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2. CONCLUSION: While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.

Intravenous tPA (Tissue-Type Plasminogen Activator) Correlates With Favorable Venous Outflow Profiles in Acute Ischemic Stroke.

BACKGROUND: Intravenous tPA (tissue-type plasminogen activator) is often administered before endovascular thrombectomy (EVT). Recent studies have questioned whether tPA is necessary given the high rates of arterial recanalization achieved by EVT, but whether tPA impacts venous outflow (VO) is unknown. We investigated whether tPA improves VO profiles on baseline computed tomography (CT) angiography (CTA) images before EVT. METHODS: Retrospective multicenter cohort study of patients with acute ischemic stroke due to large vessel occlusion undergoing EVT triage. Included patients underwent CT, CTA, and CT perfusion before EVT. VO profiles were determined by opacification of the vein of Labbé, sphenoparietal sinus, and superficial middle cerebral vein on CTA as 0, not visible; 1, moderate opacification; and 2, full. Pial arterial collaterals were graded on CTA, and tissue-level collaterals were assessed on CT perfusion using the hypoperfusion intensity ratio. Clinical and demographic data were determined from the electronic medical record. Using multivariable regression analysis, we determined the correlation between tPA administration and favorable VO profiles. RESULTS: Seven hundred seventeen patients met inclusion criteria. Three hundred sixty-five patients received tPA (tPA+), while 352 patients were not treated with tPA (tPA-). Fewer tPA+ patients had atrial fibrillation (n=128 [35%] versus n=156 [44%]; P=0.012) and anticoagulants/antiplatelet treatment before acute ischemic stroke due to large vessel occlusion onset (n=130 [36%] versus n=178 [52%]; P<0.001) compared with tPA- patients. One hundred eighty-five patients (51%) in the tPA+ and 100 patients (28%) in the tPA- group exhibited favorable VO (P<0.001). Multivariable regression analysis showed that tPA administration was a strong independent predictor of favorable VO profiles (OR, 2.6 [95% CI, 1.7-4.0]; P<0.001) after control for favorable pial arterial CTA collaterals, favorable tissue-level collaterals on CT perfusion, age, presentation National Institutes of Health Stroke Scale, antiplatelet/anticoagulant treatment, history of atrial fibrillation and time from symptom onset to imaging. CONCLUSIONS: In patients with acute ischemic stroke due to large vessel occlusion undergoing thrombectomy triage, tPA administration was strongly associated with the presence of favorable VO profiles.

Diversity in Radiology: Current Status and Trends Over the Past Decade.

Background There have been growing efforts nationally and institutionally toward diversity in radiology. Purpose To analyze sex and racial and ethnic diversity over time (2010-2019) for the various levels of the U.S. academic radiology physician workforce in context of the available pipeline of medical students and trainees. Materials and Methods Data on sex and race and ethnicity were collected among medical school applicants, graduates, radiology residency applicants, residents, and different levels of academic radiology faculty. All trainee data were obtained from two time points, 2010-2011 and 2019-2020. Radiology faculty data were collected from 2010 to 2019. The sex and racial and ethnic composition at each academic level was compared between 2010 and 2019 using the χ2 test and a significance level of P < .05. Results In 2019, the percentage of female faculty among radiology instructors was 38% (251 of 655); assistant professors, 31% (1503 of 4801); associate professors, 28% (600 of 2161); professors, 22% (424 of 1901); and department chairs, 17% (37 of 220). The proportion of female faculty increased from 2010 to 2019, with the greatest relative increase in percentages among the more senior faculty positions. However, the proportion of female department chairs has only increased from 13% (27 of 203) in 2010 to 17% (37 of 220) in 2019. Across training levels, the most abrupt change in composition of female trainees occurred from medical school matriculates (52%, 11 160 of 21 614) to radiology residency applicants (29%, 656 of 2274), which largely stayed unchanged from 2010 to 2019. The proportion of Black or African American department chairs was 5% (10 of 220) in 2019, which was higher compared with that of assistant professor, associate professor, and professor levels for Black or African American faculty (3% [130 of 4949], 2% [41 of 2208], and 2% [35 of 1924], respectively), with proportions of Hispanic faculty at 5% (240 of 4949), 4% (96 of 2208), and 3% (60 of 1924), respectively. These proportions have not changed since 2010. Conclusion Identifying and addressing reasons for the low proportion of female radiology residency applicants despite a highly diverse pool of medical students would be key to increasing female representation in the field. The low representation of African American and Hispanic individuals in academic radiology is seen at all levels and has not changed much over time. Efforts to increase diversity may need to be focused toward the medical school and residency application levels. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Pandharipande and Shah in this issue.

Favourable arterial, tissue-level and venous collaterals correlate with early neurological improvement after successful thrombectomy treatment of acute ischaemic stroke.

BACKGROUND AND PURPOSE: Early neurological improvement (ENI) after thrombectomy is associated with better long-term outcomes in patients with acute ischaemic stroke due to large vessel occlusion (AIS-LVO). Whether cerebral collaterals influence the likelihood of ENI is poorly described. We hypothesised that favourable collateral perfusion at the arterial, tissue-level and venous outflow (VO) levels is associated with ENI after thrombectomy. MATERIALS AND METHODS: Multicentre retrospective study of patients with AIS-LVO treated by thrombectomy. Tissue-level collaterals (TLC) were measured on cerebral perfusion studies by the hypoperfusion intensity ratio. VO and pial arterial collaterals (PAC) were determined by the Cortical Vein Opacification Score and the modified Tan scale on CT angiography, respectively. ENI was defined as improvement of ≥8 points or a National Institutes of Health Stroke Scale score of 0 hour or 1 24 hours after treatment. Multivariable regression analyses were used to determine the association of collateral biomarkers with ENI and good functional outcomes (modified Rankin Scale 0-2). RESULTS: 646 patients met inclusion criteria. Favourable PAC (OR: 1.9, CI 1.2 to 3.1; p=0.01), favourable VO (OR: 3.3, CI 2.1 to 5.1; p<0.001) and successful reperfusion (OR: 3.1, CI 1.7 to 5.8; p<0.001) were associated with ENI, but favourable TLC were not (p=0.431). Good functional outcomes at 90-days were associated with favourable TLC (OR: 2.2, CI 1.4 to 3.6; p=0.001), VO (OR: 5.7, CI 3.5 to 9.3; p<0.001) and ENI (OR: 5.7, CI 3.3 to 9.8; p<0.001), but not PAC status (p=0.647). CONCLUSION: Favourable PAC and VO were associated with ENI after thrombectomy. Favourable TLC predicted longer term functional recovery after thrombectomy, but the impact of TLC on ENI is strongly dependent on vessel reperfusion.

Collateral status, hyperglycemia, and functional outcome after acute ischemic stroke.

BACKGROUND: Mixed data exist regarding the association between hyperglycemia and functional outcome after acute ischemic stroke when accounting for the impact of leptomeningeal collateral flow. We sought to determine whether collateral status modifies the association between treatment group and functional outcome in a subset of patients with large vessel occlusion enrolled in the Stroke Hyperglycemia Insulin Network Effort (SHINE) trial. METHODS: In this post-hoc analysis, we analyzed patients enrolled into the SHINE trial with anterior circulation large vessel occlusion who underwent imaging with CT angiography prior to glucose control treatment group assignment. The primary analysis assessed the degree to which collateral status modified the effect between treatment group and functional outcome as defined by the 90-day modified Rankin Scale score. Logistic regression was used to model the data, with adjustments made for thrombectomy status, age, post-perfusion thrombolysis in cerebral infarction (TICI) score, tissue plasminogen activator (tPA) use, and baseline National Institutes of Health Stroke Scale (NIHSS) score. Five SHINE trial centers contributed data for this analysis. Statistical significance was defined as a p-value < 0.05. RESULTS: Among the 1151 patients in the SHINE trial, 57 with angiographic data were included in this sub-analysis, of whom 19 had poor collaterals and 38 had good collaterals. While collateral status had no effect (p = 0.855) on the association between glucose control treatment group and functional outcome, patients with good collaterals were more likely to have a favorable functional outcome (p = 0.001, OR 5.02; 95% CI 1.37-16.0). CONCLUSIONS: In a post-hoc analysis using a subset of patients with angiographic data enrolled in the SHINE trial, collateral status did not modify the association between glucose control treatment group and functional outcome. However, consistent with prior studies, there was a significant association between good collateral status and favorable outcome in patients with large vessel occlusion stroke. TRIAL REGISTRATION: ClinicalTrials.gov Identifier is NCT01369069. Registration date is June 8, 2011.

The mid-term effects of carotid endarterectomy on cognition and regional neural activity analyzed with the amplitude of low frequency fluctuations technique.

PURPOSE: The study aims to evaluate the mid-term effects of carotid endarterectomy (CEA) on cognition and resting-state functional magnetic resonance imaging (rs-fMRI) using the Amplitude of Low Frequency Fluctuations (ALFF) technique. METHODS: In this observational study, patients eligible for CEA were prospectively included. On the same day, within 1 week of the CEA procedure performed and 12 months after the CEA procedure, all patients underwent (i) an MRI examination for rs-fMRI analysis and (ii) a cognitive evaluation using the Italian version of the Mini-Mental State Examination (MMSE) corrected for age and schooling. Pre-CEA and post-CEA MMSE scores were evaluated using paired sample t-tests, adopting a p-value < 0.05 as statistical threshold. The ALFF technique was used for analyzing the differences between pre-CEA and post-CEA rs-fMRI scans in terms of regional neural activation. This was accomplished by applying non-parametric statistics based on randomization/permutation for cluster-level inferences, adopting a cluster-mass p-value corrected for false discovery < 0.05 for cluster threshold, and a p-uncorrected < 0.01 for the voxel threshold. RESULTS: Twenty asymptomatic patients were enrolled. The mean MMSE score resulted improved following CEA procedure (p-value = 0.001). The ALFF analysis identified a single cluster of 6260 voxels of increased regional neural activity following CEA, and no cluster of reduced activity. The majority of voxels covered the right precentral gyrus, the right middle frontal gyrus, and the anterior division of the cingulate gyrus. CONCLUSION: Mid-term cognitive improvements observed after CEA are associated to increased regional neural activity of several cerebral regions.

Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm.

BACKGROUND: The Mortality Probability Model (MPM) is used in research and quality improvement to adjust for severity of illness and can also inform triage decisions. However, a limitation for its automated use or application is that it includes the variable "intracranial mass effect" (IME), which requires human engagement with the electronic health record (EHR). We developed and tested a natural language processing (NLP) algorithm to identify IME from CT head reports. METHODS: We obtained initial CT head reports from adult patients who were admitted to the ICU from our ED between 10/2013 and 9/2016. Each head CT head report was labeled yes/no IME by at least two of five independent labelers. The reports were then randomly divided 80/20 into training and test sets. All reports were preprocessed to remove linguistic and style variability, and a dictionary was created to map similar common terms. We tested three vectorization strategies: Term Frequency-Inverse Document frequency (TF-IDF), Word2Vec, and Universal Sentence Encoder to convert the report text to a numerical vector. This vector served as the input to a classification-tree-based ensemble machine learning algorithm (XGBoost). After training, model performance was assessed in the test set using the area under the receiver operating characteristic curve (AUROC). We also divided the continuous range of scores into positive/inconclusive/negative categories for IME. RESULTS: Of the 1202 CT reports in the training set, 308 (25.6%) reports were manually labeled as "yes" for IME. Of the 355 reports in the test set, 108 (30.4%) were labeled as "yes" for IME. The TF-IDF vectorization strategy as an input for the XGBoost model had the best AUROC:-- 0.9625 (95% CI 0.9443-0.9807). TF-IDF score categories were defined and had the following likelihood ratios: "positive" (TF-IDF score > 0.5) LR = 24.59; "inconclusive" (TF-IDF 0.05-0.5) LR = 0.99; and "negative" (TF-IDF < 0.05) LR = 0.05. 82% of reports were classified as either "positive" or "negative". In the test set, only 4 of 199 (2.0%) reports with a "negative" classification were false negatives and only 8 of 93 (8.6%) reports classified as "positive" were false positives. CONCLUSION: NLP can accurately identify IME from free-text reports of head CTs in approximately 80% of records, adequate to allow automatic calculation of MPM based on EHR data for many applications.

Diffuse Axonal Injury Grade on Early MRI is Associated with Worse Outcome in Children with Moderate-Severe Traumatic Brain Injury.

BACKGROUND: Traumatic brain injury (TBI) is the leading cause of death and disability in children, but effective tools for predicting outcome remain elusive. Although many pediatric patients receive early magnetic resonance imaging (MRI), data on its utility in prognostication are lacking. Diffuse axonal injury (DAI) is a hallmark of TBI detected on early MRI and was shown previously to improve prognostication in adult patients with TBI. In this exploratory study, we investigated whether DAI grade correlates with functional outcome and improves prognostic accuracy when combined with core clinical variables and computed tomography (CT) biomarkers in pediatric patients with moderate-severe TBI (msTBI). METHODS: Pediatric patients (≤ 19 years) who were admitted to two regional level one trauma centers with a diagnosis of msTBI (Glasgow Coma Scale [GCS] score < 13) between 2011 and 2019 were identified through retrospective chart review. Patients who underwent brain MRI within 30 days of injury and had documented clinical follow-up after discharge were included. Age, pupil reactivity, and initial motor GCS score were collected as part of the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. Imaging was reviewed to calculate the Rotterdam score (CT) and DAI grade (MRI) and to evaluate for presence of hypoxic-ischemic injury (MRI). The primary outcome measure was the Pediatric Cerebral Performance Category Scale (PCPCS) score at 6 months after TBI, with favorable outcome defined as PCPCS scores 1-3 and unfavorable outcome defined as PCPCS scores 4-6. The secondary outcome measure was discharge disposition to home versus to an inpatient rehabilitation facility. RESULT: Of 55 patients included in the study, 45 (82%) had severe TBI. The most common mechanism of injury was motor vehicle collision (71%). Initial head CT scans showed acute hemorrhage in 84% of patients. MRI was acquired a median of 5 days after injury, and hemorrhagic DAI lesions were detected in 87% of patients. Each 1-point increase in DAI grade increased the odds of unfavorable functional outcome by 2.4-fold. When controlling for core IMPACT clinical variables, neither the DAI grade nor the Rotterdam score was independently correlated with outcome and neither significantly improved outcome prediction over the IMPACT model alone. CONCLUSIONS: A higher DAI grade on early MRI is associated with worse 6-month functional outcome and with discharge to inpatient rehabilitation in children with acute msTBI in a univariate analysis but does not independently correlate with outcome when controlling for the GCS score. Addition of the DAI grade to the core IMPACT model does not significantly improve prediction of poor neurological outcome. Further study is needed to elucidate the utility of early MRI in children with msTBI.