Survival and neurological outcomes following management of intramedullary spinal metastasis patients: a case series with comprehensive review of the literature.

Intramedullary spinal cord metastasis (ISCM), though rare, represents a potentially debilitating manifestation of systemic cancer. With emerging advances in cancer care, ISCMs are increasingly being encountered in clinical practice. Herein, we describe one of the larger retrospective single institutional case series on ISCMs, analyze survival and treatment outcomes, and review the literature. All surgically evaluated ISCMs at our institution between 2005 and 2023 were retrospectively reviewed. Demographics, tumor features, treatment, and clinical outcome characteristics were collected. Neurological function was quantified via the Frankel grade and the McCormick score (MCS). The pre- and post-operative Karnofsky performance scores (KPS) were used to assess functional status. Descriptive statistics, univariate analysis, log-rank test, and the Kaplan-Meier survival analysis were performed. A total of 9 patients were included (median age 67 years (range, 26-71); 6 were male). Thoracic and cervical spinal segments were most affected (4 patients each). Six patients (75%) underwent surgical management (1 biopsy and 5 resections), and 3 cases underwent chemoradiation only. Post-operatively, 2 patients had an improvement in their neurological exam with one patient becoming ambulatory after surgery; three patients maintained their neurological exam, and 1 had a decline. There was no statistically significant difference in the pre- and post-operative MCS and median KPS scores in surgically treated patients. Median OS after ISCM diagnosis was 7 months. Absence of brain metastasis, tumor histology (renal and melanoma), cervical/thoracic location, and post-op KPS ≥ 70 showed a trend toward improved overall survival. The incidence of ISCM is increasing, and earlier diagnosis and treatment are considered key for the preservation of neurological function. When patient characteristics are favorable, surgical resection of ISCM can be considered in patients with rapidly progressive neurological deficits. Surgical treatment was not associated with an improvement in overall survival in patients with ISCMs.

Amyloid-related Imaging Abnormalities in Alzheimer Disease Treated with Anti-Amyloid-ß Therapy.

Alzheimer disease (AD) is the most common form of dementia worldwide. Treatment of AD has mainly been focused on symptomatic treatment until recently with the advent and approval of monoclonal antibody (MAB) immunotherapy. U.S. Food and Drug Administration-approved drugs such as aducanumab, as well as upcoming newer-generation drugs, have provided an exciting new therapy focused on reducing the amyloid plaque burden in AD. Although this new frontier has shown benefits for patients, it is not without complications, which are mainly neurologic. Increased use of MABs led to the discovery of amyloid-related imaging abnormalities (ARIA). ARIA has been further classified into two categories, ARIA-E and ARIA-H, representing edema and/or effusion and hemorrhage, respectively. ARIA is thought to be caused by increased vascular permeability following an inflammatory response, leading to the extravasation of blood products and proteinaceous fluid. Patients with ARIA may present with headaches, but they are usually asymptomatic and ARIA is only diagnosable at MRI; it is essential for the radiologist to recognize and monitor ARIA. Increased incidence and investigation into this concern have led to the creation of grading scales and monitoring guidelines to diagnose and guide treatment using MABs. Cerebral amyloid angiopathy has an identical pathogenesis to that of ARIA and is its closest differential diagnosis, with imaging findings being the same for both entities and only a history of MAB administration allowing differentiation. The authors discuss the use of MABs for treating AD, expand on ARIA and its consequences, and describe how to identify and grade ARIA to guide treatment properly. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center See the invited commentary by Yu in this issue.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging.

INTRODUCTION: Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). METHODOLOGY: A retrospective, institutional review board-approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. RESULTS: For all patients combined, the clinical feature-derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. CONCLUSIONS: When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature-derived model for predicting PFS.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

PURPOSE: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL). METHODOLOGY: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC. RESULTS: Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]). CONCLUSION: Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

Multi-band- and in-plane-accelerated diffusion MRI enabled by model-based deep learning in q-space and its extension to learning in the spherical harmonic domain.

PURPOSE: To propose a new method for the recovery of combined in-plane- and multi-band (MB)-accelerated diffusion MRI data. METHODS: Combining MB acceleration with in-plane acceleration is crucial to improve the time efficiency of high (angular and spatial) resolution diffusion scans. However, as the MB factor and in-plane acceleration increase, the reconstruction becomes challenging due to the heavy aliasing. The new reconstruction utilizes an additional q-space prior to constrain the recovery, which is derived from the previously proposed qModeL framework. Specifically, the qModeL prior provides a pre-learned representation of the diffusion signal space to which the measured data belongs. We show that the pre-learned q-space prior along with a model-based iterative reconstruction that accommodate multi-band unaliasing, can efficiently reconstruct the in-plane- and MB-accelerated data. The power of joint reconstruction is maximally utilized by using an incoherent under-sampling pattern in the k-q domain. We tested the proposed method on single- and multi-shell data, with high/low angular resolution, high/low spatial resolution, healthy/abnormal tissues, and 3T/7T field strengths. Furthermore, the learning is extended to the spherical harmonic basis, to provide a rotational invariant learning framework. RESULTS: The qModeL joint reconstruction is shown to simultaneously unalias and jointly recover DWIs with reasonable accuracy in all the cases studied. The reconstruction error from 18-fold accelerated multi-shell datasets was <3%. The microstructural maps derived from the accelerated acquisitions also exhibit reasonable accuracy for both healthy and abnormal tissues. The deep learning (DL)-enabled reconstructions are comparable to those derived using traditional methods. CONCLUSION: qModeL enables the joint recovery of combined in-plane- and MB-accelerated dMRI utilizing DL.

Targeting Myeloid-Specific Integrin α9ß1 Improves Short- and Long-Term Stroke Outcomes in Murine Models With Preexisting Comorbidities by Limiting Thrombosis and Inflammation.

RATIONALE: Currently, there is no effective intervention available that can reduce brain damage following reperfusion. Clinical studies suggest a positive correlation between the increased influx of neutrophils and severity of brain injury following reperfusion. Integrin α9ß1 is highly expressed on activated neutrophils and contributes to stable adhesion, but its role in stroke outcome has not been demonstrated to date. OBJECTIVE: We sought to determine the mechanistic role of myeloid-specific α9ß1 in the progression of ischemic stroke in murine models with preexisting comorbidities. METHODS AND RESULTS: We generated novel myeloid-specific α9-deficient (α9-/-) wild type (α9fl/flLysMCre+/-), hyperlipidemic (α9fl/flLysMCre+/-Apoe-/-), and aged (bone marrow chimeric) mice to evaluate stroke outcome. Susceptibility to ischemia/reperfusion injury was evaluated at 1, 7, and 28 days following reperfusion in 2 models of experimental stroke: filament and embolic. We found that peripheral neutrophils displayed elevated α9 expression following stroke. Irrespective of sex, genetic deletion of α9 in myeloid cells improved short- and long-term stroke outcomes in the wild type, hyperlipidemic, and aged mice. Improved stroke outcome and enhanced survival in myeloid-specific α9-/- mice was because of marked decrease in cerebral thromboinflammatory response as evidenced by reduced fibrin, platelet thrombi, neutrophil, NETosis, and decreased phospho-NF-κB (nuclear factor-κB), TNF (tumor necrosis factor)-α, and IL (interleukin)-1ß levels. α9-/- mice were less susceptible to FeCl3 injury-induced carotid artery thrombosis that was concomitant with improved regional cerebral blood flow following stroke as revealed by laser speckle imaging. Mechanistically, fibronectin containing extra domain A, a ligand for integrin α9, partially contributed to α9-mediated stroke exacerbation. Infusion of a specific anti-integrin α9 inhibitor into hyperlipidemic mice following reperfusion significantly reduced infarct volume and improved short- and long-term functional outcomes up to 28 days. CONCLUSIONS: We provide genetic and pharmacological evidence for the first time that targeting myeloid-specific integrin α9ß1 improves short- and long-term functional outcomes in stroke models with preexisting comorbidities by limiting cerebral thrombosis and inflammation.

Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke.

OBJECTIVES: Automated image-level detection of large vessel occlusions (LVO) could expedite patient triage for mechanical thrombectomy. A few studies have previously attempted LVO detection using artificial intelligence (AI) on CT angiography (CTA) images. To our knowledge this is the first study to detect LVO existence and location on raw 4D-CTA/ CT perfusion (CTP) images using neural network (NN) models. MATERIALS AND METHODS: Retrospective study using data from a level-I stroke center was performed. A total of 306 (187 with LVO, and 119 without) patients were evaluated. Image pre-processing included co-registration, normalization and skull stripping. Five consecutive time-points for each patient were selected to provide variable contrast density in data. Additional data augmentation included rotation and horizonal image flipping. Our model architecture consisted of two neural networks, first for classification (based on hemispheric asymmetry), followed by second model for exact site of LVO detection. Only cases deemed positive by the classification model were routed to the detection model, thereby reducing false positives and improving specificity. The results were compared with expert annotated LVO detection. RESULTS: Using a 80:20 split for training and validation, the combination of both classification and detection model achieved a sensitivity of 86.5%, a specificity of 89.5%, and an accuracy of 87.5%. A 5-fold cross-validation using the entire data achieved a mean sensitivity of 82.7%, a specificity of 89.8%, and an accuracy of 85.5% and a mean AUC of 0.89 (95% CI: 0.85-0.93). CONCLUSION: Our findings suggest that accurate image-level LVO detection is feasible on CTP raw images.

CT Perfusion Maps Improve Detection of M2-MCA Occlusions in Acute Ischemic Stroke.

OBJECTIVES: Middle cerebral artery occlusions, particularly M2 branch occlusions are challenging to identify on CTA. We hypothesized that additional review of the CTP maps will increase large vessel occlusion (LVO) detection accuracy on CTA and reduce interpretation time. MATERIALS AND METHODS: Two readers (R1 and R2) retrospectively reviewed the CT studies in 99 patients (27 normal, 26 M1-MCA, 46 M2-MCA occlusions) who presented with suspected acute ischemic stroke (AIS). The time of interpretation and final diagnosis were recorded for the CTA images (derived from CTP data), both without and with the CTP maps. The time for analysis for all vascular occlusions was compared using McNemar tests. ROC curve analysis and McNemar tests were performed to assess changes in diagnostic performance with the addition of CTP maps. RESULTS: With the addition of the CTP maps, both readers showed increased sensitivity (p = 0.01 for R1 and p = 0.04 for R2), and accuracy (p = 0.02 for R1 and p = 0.004 for R2) for M2-MCA occlusions. There was a significant improvement in diagnostic performance for both readers for detection of M2-MCA occlusions (AUC R1 = 0.86 to 0.95, R2 = 0.84 to 0.95; p < 0.05). Both readers showed reduced interpretation time for all cases combined, as well as for normal studies (p < 0.001) when CTP images were reviewed along with CTA. Both readers also showed reduced interpretation time for M2-MCA occlusions, which was significant for one of the readers (p < 0.02). CONCLUSION: The addition of CTP maps improves accuracy and reduces interpretation time for detecting LVO and M2-MCA occlusions in AIS. Incorporation of CTP in acute stroke imaging protocols may improve detection of more distal occlusions.

Computed Tomography Perfusion-Based Prediction of Core Infarct and Tissue at Risk: Can Artificial Intelligence Help Reduce Radiation Exposure?

BACKGROUND AND PURPOSE: We explored the feasibility of automated, arterial input function independent, vendor neutral prediction of core infarct, and penumbral tissue using complete and partial computed tomographic perfusion data sets through neural networks. METHODS: Using retrospective computed tomographic perfusion data from 57 patients, split as training/validation (60%/40%), we developed and validated separate 2-dimensional U-net models for cerebral blood flow (CBF) and time to maximum (Tmax) maps calculation to predict core infarct and tissue at risk, respectively. Once trained, the full sets of 28 input images were sequentially reduced to equitemporal 14, 10, and 7 time points. The averaged structural similarity index measure between the model-derived images and ground truth perfusion maps was compared. Volumes for core infarct and Tmax were compared using the Pearson correlation coefficient. RESULTS: Both CBF and Tmax maps derived using 28 and 14 time points had similar structural similarity index measure (0.80-0.81; P>0.05) when compared with ground truth images. The Pearson correlation for the CBF and Tmax volumes derived from the model using 28-tp with ground truth volumes derived from the RAPID software was 0.69 for CBF and 0.74 for Tmax. The predicted maps were fully concordant in terms of laterality to the commercial perfusion maps. The mean Dice scores were 0.54 for the core infarct and 0.63 for the hypoperfusion maps. CONCLUSIONS: Artificial intelligence model-derived volumes show good correlation with RAPID-derived volumes for CBF and Tmax. Within the constraints of a small sample size, the perfusion map quality is similar when using 14-tp instead of 28-tp. Our findings provide proof of concept that vendor neutral artificial intelligence models for computed tomographic perfusion processing using complete or partial image data sets appear feasible. The model accuracy could be further optimized using larger data sets.

Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques.

OBJECTIVES: Despite the robust diagnostic performance of MRI-based radiomic features for differentiating between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) reported on prior studies, the best sequence or a combination of sequences and model performance across various machine learning pipelines remain undefined. Herein, we compare the diagnostic performance of multiple radiomics-based models to differentiate GBM from PCNSL. METHODS: Our retrospective study included 94 patients (34 with PCNSL and 60 with GBM). Model performance was assessed using various MRI sequences across 45 possible model and feature selection combinations for nine different sequence permutations. Predictive performance was assessed using fivefold repeated cross-validation with five repeats. The best and worst performing models were compared to assess differences in performance. RESULTS: The predictive performance, both using individual and a combination of sequences, was fairly robust across multiple top performing models (AUC: 0.961-0.977) but did show considerable variation between the best and worst performing models. The top performing individual sequences had comparable performance to multiparametric models. The best prediction model in our study used a combination of ADC, FLAIR, and T1-CE achieving the highest AUC of 0.977, while the second ranked model used T1-CE and ADC, achieving a cross-validated AUC of 0.975. CONCLUSION: Radiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences. KEY POINTS: • Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably. • ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model. • Embedded feature selection models perform better than models using a priori feature reduction.

Repeat Head CT for Neurologically Stable Patients With Mild Traumatic Subarachnoid Hemorrhage During Interfacility Transfer and Follow-Up Does Not Alter Patient Care [Formula: see text].

PURPOSE: To evaluate the impact of repeat head computed tomography (CT) during (1) interfacility transfer and (2) inpatient and/or outpatient follow-up on management, cost-effectiveness, and radiation dose in neurologically stable patients with mild traumatic subarachnoid hemorrhage (tSAH). MATERIAL AND METHODS: This is a single-center retrospective study evaluating patients with mild tSAH presenting between January 2017 and July 2019. A total of 101 and 140 patients met the eligibility criteria for the first and second subgroups, respectively. Common inclusion criteria were isolated mild tSAH, Glasgow Coma Scale between 13 and 15, and neurological stability. Additional inclusion criteria for the first subgroup were availability of brain imaging at the outside institution prior to transfer and the second subgroup was the availability of follow-up imaging. RESULTS: In the first subgroup, 76.20% of patients had stable SAH, 18.80% had reduced SAH, while 5% had an interval increase in SAH. None required any surgical intervention. Additional per-patient mean radiation exposure was 1.77 ± 0.26 mSv. In the second subgroup, all 140 patients had complete resolution of tSAH. One patient had a new tiny subdural hemorrhage, which subsequently resolved on follow-up. The additional mean radiation exposure was 2.47 ± 1.29 mSv. A total of 256 avoidable CT scans were performed resulting in excess health care costs of about US$531 696. CONCLUSION: In neurologically stable isolated tSAH patients, repeat brain imaging during interfacility transfer and inpatient and/or outpatient follow-up do not alter patient management despite increased health care costs and radiation burden.

Detection and Quantification of Symptomatic Atherosclerotic Plaques With High-Resolution Imaging in Cryptogenic Stroke.

BACKGROUND AND PURPOSE: High-resolution vessel wall imaging (HR-VWI) is a powerful tool in diagnosing intracranial vasculopathies not detected on routine imaging. We hypothesized that 7T HR-VWI may detect the presence of atherosclerotic plaques in patients with intracranial atherosclerosis disease initially misdiagnosed as cryptogenic strokes. METHODS: Patients diagnosed as cryptogenic stroke but suspected of having an intracranial arteriopathy by routine imaging were prospectively imaged with HR-VWI. If intracranial atherosclerotic plaques were identified, they were classified as culprit or nonculprit based on the likelihood of causing the index stroke. Plaque characteristics, such as contrast enhancement, degree of stenosis, and morphology, were analyzed. Contrast enhancement was determined objectively after normalization with the pituitary stalk. A cutoff value for plaque-to-pituitary stalk contrast enhancement ratio (CR) was determined for optimal prediction of the presence of a culprit plaque. A revised stroke cause was adjudicated based on clinical and HR-VWI findings. RESULTS: A total of 344 cryptogenic strokes were analyzed, and 38 eligible patients were imaged with 7T HR-VWI. Intracranial atherosclerosis disease was adjudicated as the final stroke cause in 25 patients. A total of 153 intracranial plaques in 374 arterial segments were identified. Culprit plaques (n=36) had higher CR and had concentric morphology when compared with nonculprit plaques (P≤0.001). CR ≥53 had 78% sensitivity for detecting culprit plaques and a 90% negative predictive value. CR ≥53 (P=0.008), stenosis ≥50% (P<0.001), and concentric morphology (P=0.030) were independent predictors of culprit plaques. CONCLUSIONS: 7T HR-VWI allows identification of underlying intracranial atherosclerosis disease in a subset of stroke patients with suspected underlying vasculopathy but otherwise classified as cryptogenic. Plaque analysis in this population demonstrated that culprit plaques had more contrast enhancement (CR ≥53), caused a higher degree of stenosis, and had a concentric morphology.

Differential Risk Factors and Outcomes of Ischemic Stroke due to Cervical Artery Dissection in Young Adults.

INTRODUCTION: Cervical artery dissection (CeAD) is a major cause of ischemic stroke in young adults. Our understanding of the specific risk factors and clinical course of CeAD is still evolving. In this study, we evaluated the differential risk factors and outcomes of CeAD-related strokes among young adults. METHODS: The study population consisted of young patients 15-45 years of age consecutively admitted with acute ischemic stroke to our comprehensive stroke center between January 1, 2010, and November 30, 2016. Diagnosis of CeAD was based on clinical and radiological findings. Univariate and multivariable logistic regression analyses were used to assess the risk factors and clinical outcomes associated with CeAD-related strokes. RESULTS: Of the total 333 patients with acute ischemic stroke included in the study (mean ± SD age: 36.4 ± 7.1 years; women 50.8%), CeAD was identified in 79 (23.7%) patients. As compared to stroke due to other etiologies, patients with CeAD were younger in age, more likely to have history of migraine and recent neck manipulation and were less likely to have hypertension, diabetes, and previous history of stroke. Clinical outcomes of CeAD were comparable to strokes due to other etiologies. Within the CeAD group, higher initial stroke severity and history of tobacco use were associated with higher modified Rankin Scale score at follow-up. CONCLUSIONS: While history of migraine and neck manipulation are significantly associated with CeAD, most of the traditional vascular risk factors for stroke are less prevalent in this group when compared to strokes due to other etiologies. For CeAD-related strokes, higher initial stroke severity and history of tobacco use may be associated with higher stroke-related disability, but overall, patients with CeAD have similar outcomes as compared to strokes due to other etiologies.

Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose.

PURPOSE: We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA). METHODS: Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values. RESULTS: Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51-0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001). CONCLUSION: Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure.

Neurological emergencies associated with COVID-19: stroke and beyond.

Novel coronavirus disease (COVID-19) was declared a global pandemic on March 1, 2020. Neurological manifestations are now being reported worldwide, including emergent presentation with acute neurological changes as well as a comorbidity in hospitalized patients. There is limited knowledge on the neurologic manifestations of COVID-19 at present, with a wide array of neurological complications reported, ranging from ischemic stroke to acute demyelination and encephalitis. We report five cases of COVID-19 presenting to the ER with acute neurological symptoms, over the course of 1 month. This includes two cases of ischemic stroke, one with large-vessel occlusion and one with embolic infarcts. The remainders of the cases include acute tumefactive demyelination, isolated cytotoxic edema of the corpus callosum with subarachnoid hemorrhage, and posterior reversible encephalopathy syndrome (PRES).

Neuroimaging Findings in Intracranial Sarcoid Phlebitis: A Case Report.

INTRODUCTION: Venous phlebitis in Neurosarcoidosis (NS) is rare but is often associated with intracranial hemorrhage (ICH). Imaging findings in such cases have been recently described on susceptibility weighted imaging (SWI). CASE PRESENTATION AND OUTCOME: We report a patient who presented with ICH. Magnetic resonance imaging provided evidence for parenchymal and leptomeningeal involvement while SWI and vessel wall imaging (VWI) helped confirmed NS associated intracranial phlebitis. The patient was subsequently diagnosed with systemic sarcoidosis. DISCUSSION: The emerging role of VWI and SWI in the diagnosis of this rare entity is discussed.

Computed Tomography Angiogram Derived From Computed Tomography Perfusion Done with Low Iodine Volume Protocol Preserves Diagnostic Yield for Middle Cerebral Artery-M2 Occlusions.

BACKGROUND: Computed tomography angiogram (CTA) derived from computed tomography perfusion (CTP) has been proposed to avoid addition of separate CT perfusion protocol for selection of large vessel occlusion in acute stroke patients. Previous studies have validated this technique for proximal large vessel occlusions. In this study, we test reliability for identifying M2 occlusions on CTA derived from CTP. METHODS: Through a retrospective search of the institutional thrombectomy database, we identified 28 cases with M2-MCA occlusion, of which 24 met the inclusion criteria for analysis. An additional 20 cases without M2-MCA occlusion (either normal or M1-MCA occlusion) were randomly mixed in the database to reduce observer bias. The baseline images of the CTP study in these 48 cases were then independently analyzed by 3 readers with varying level of expertise. The digital subtraction angiography (DSA) images were also independently reviewed where available. The percentage of agreement among reviewers as well as the probability of agreement of the reviewers, when compared to the DSA findings was also calculated. RESULTS: The observed agreement for the image quality amongst the 3 readers (n = 48) varied between 0.78 and 0.95 and tended to be higher for the M1 segment MCA and lower for distal M2-MCA. The observed agreements comparing 3 image reviewers versus DSA in M2 patients (n = 24) was 98% for identifying occlusion (95% CI 95%-100%), 94% for identifying proximal M2 occlusion (95% CI 88%-98%), and 91% (95% CI 84%-97%) and 90% (95% CI 83%-95%), respectively for correctly identifying inferior and superior branch of M2 occlusion. CONCLUSION: CTA data derived from CT Perfusion study preserves diagnostic yield for correctly identifying M2 occlusion.

Absence of Collaterals is Associated with Larger Infarct Volume and Worse Outcome in Patients with Large Vessel Occlusion and Mild Symptoms.

BACKGROUND: Mechanical thrombectomy is the standard of care for patients with large vessel occlusion (LVO) presenting with severe symptoms; however, little is known about the best treatment for patients with LVO and mild symptoms. The absence of good collaterals has been associated with a worse outcome in patients with LVO. In this study, we aim to assess the use of collateral score to identify patients with LVO and mild symptoms that might benefit from mechanical thrombectomy (MT). METHODS: A retrospective review of prospectively collected data on patients presenting with mild ischemic stroke (National Institute of Health Stroke Scale [NIHSS] <6) and anterior circulation LVO between September 2015 and July 2017 was performed. Collected data included baseline demographics, NIHSS on admission, Alberta Stroke Program Early CT Score (ASPECTS), location of occlusion, collateral score using Tan scoring system, final infarct volume, and 90-day modified Rankin Scale (mRS). Patients who underwent MT were excluded from this analysis. Two multivariable models were used to assess outcomes. A gamma distributed generalized linear regression model with a log link was used to examine the impact on final infarct volume. To predict the odds of a positive 90-day outcome we estimated a logistic regression. RESULTS: Forty-one patients were identified. Mean age was 67.7-years with 56.1% males. Median NIHSS on admission was 3. The most common vessels involved were the middle cerebral artery (26), internal carotid artery (14), and anterior cerebral artery (1). Twelve patients received intravenous alteplase. Median ASPECTS score was 9, median collateral score was 2.3. Median infarct volume was 10.7 mL. A good functional outcome (mRS 0-2) at 90 days was achieved in 86.4% of patients. There was a negative relationship between collateral score and final infarct volume (-.3134, P = .046). Multivariable regression results showed that with a one-point increase in NIHSS on admission there was a 25% increase in final infarct volume. Higher infarct volume was associated with lower odds of achieving good functional outcome (mRS 0-2) (odds ratio .96, P = .049 [95% confidence interval .918-.999). CONCLUSIONS: Most patients with anterior circulation LVO and low NIHSS achieve good long-term functional outcome, however, approximately 15% had significant disability. The absence of collaterals correlates with a larger final infarct volume and a worse long-term functional outcome. Collateral score might be a useful tool in identifying patients with LVO and low NIHSS who might benefit from MT.

Imaging in viral infections of the central nervous system: can images speak for an acutely ill brain?

Viral infections involving the central nervous system (CNS) may result from a wide variety of agents and have clinically overlapping manifestations. The diagnosis is often made based on a combination of the clinical exam, local epidemiology, imaging, and biochemical findings. Despite the advances in medicine and imaging, the diagnosis often remains elusive. Imaging, however, still plays a vital role in suggesting the diagnosis in typical cases, excluding potential mimics, and in evaluating changes with therapy. Herein, the authors present a review of various common and rare viral encephalitides with emphasis on the imaging literature.