Extrahippocampal Radiomics Analysis Can Potentially Identify Laterality in Patients With MRI-Negative Temporal Lobe Epilepsy

Objective: The objective of the study was to investigate whether radiomics features of extrahippocampal regions differ between patients with epilepsy and healthy controls, and whether any differences can identify patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE).

Methods: Data from 36 patients with hippocampal sclerosis (HS) and 50 healthy controls were used to construct a radiomics model. A total of 1,618 radiomics features from the affected hippocampal and extrahippocampal regions were compared with features from healthy controls and the unaffected side of patients. Using a stepwise selection method with a univariate t-test and elastic net penalization, significant predictors for identifying TLE were separately selected for the hippocampus (H+) and extrahippocampal region (H–). Each model was independently validated with an internal set of MRI-negative adult TLE patients (n = 22) and pediatric validation cohort with MRI-negative TLE (n = 20) from another tertiary center; diagnostic performance was calculated using area under the curve (AUC) of the receiver-operating-characteristic curve analysis.

Results: Forty-eight significant H+ radiomic features and 99 significant H– radiomic features were selected from the affected side of patients and used to create a hippocampus model and an extrahippocampal model, respectively. Texture features were the most frequently selected feature. Training set showed slightly higher accuracy between hippocampal (AUC = 0.99) and extrahippocampal model (AUC = 0.97). In the internal validation and external validation sets, the extrahippocampal model (AUC = 0.80 and 0.92, respectively) showed higher diagnostic performance for identifying the affected side of patients than the hippocampus model (AUC = 0.67 and 0.69).

Significance: Radiomics revealed extrahippocampal abnormality in the affected side of patients with TLE and could potentially help to identify MRI-negative TLE.

Classification of Evidence: Class IV Criteria for Rating Diagnostic Accuracy Studies.

Diagnostic performance of T2* gradient echo, susceptibility-weighted imaging, and quantitative susceptibility mapping for patients with multiple system atrophy-parkinsonian type: a systematic review and meta-analysis

OBJECTIVES

To investigate the diagnostic performance of T2*-weighted gradient echo (GRE) imaging, susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) in differentiating multiple system atrophy-parkinsonian type (MSA-P) from Parkinson's disease (PD).

METHODS

A systematic literature search through the MEDLINE and EMBASE databases was performed, starting on September 8, 2020, to identify studies evaluating the diagnostic performance of putaminal hypointensity on T2* GRE or SWI and phase shift on QSM in differentiating MSA-P from PD. The pooled sensitivity and specificity were obtained using hierarchical logistic regression modeling and hierarchical summary receiver operating characteristic (HSROC) modeling. The pooled diagnostic yields of T2* GRE, SWI, or QSM among MSA-P patients were calculated using the DerSimonian-Laird random-effects model.

RESULTS

Twelve original articles with 985 patients were finally included. SWI was performed in seven studies, T2* GRE was performed in three studies, and QSM was performed in two studies. The pooled sensitivity and specificity were 0.65 (95% CI 0.51-0.78) and 0.90 (95% CI 0.83-0.95), respectively. The area under the HSROC curve was 0.87 (95% CI 0.84-0.90). The Higgins I² statistic calculations revealed considerable heterogeneity in terms of both sensitivity (I² = 72.12%) and specificity (I² = 70.38%). The coupled forest plot revealed the threshold effect. For the nine studies in which area under the curve (AUC) was obtainable, the AUC ranged from 0.68 to 0.947, with a median of 0.819. The pooled diagnostic yield of T2* GRE, SWI, or QSM was 66% (95% CI 51-78%).

CONCLUSIONS

Putaminal hypointensity on T2* GRE or SWI and phase shift on QSM might be a promising diagnostic tool in differentiating MSA-P from PD. Further large multicenter prospective study is warranted.

KEY POINTS

• Three different index tests, definitions of positive image findings, thresholds, the way how to draw ROIs, reference standard, and MRI parameters could affect the heterogeneity of the study. • The pooled sensitivity and specificity were 0.65 (95% CI 0.51-0.78) and 0.90 (95% CI 0.83-0.95), respectively. • The pooled diagnostic yield of T2* GRE, SWI, or QSM was 66% (95% CI 51-78%).

Diagnostic Yield of Diffusion-Weighted Brain Magnetic Resonance Imaging in Patients with Transient Global Amnesia: A Systematic Review and Meta-Analysis

OBJECTIVE

To investigate the diagnostic yield of diffusion-weighted imaging (DWI) in patients with transient global amnesia (TGA) and identify significant parameters affecting diagnostic yield.

MATERIALS AND METHODS

A systematic literature search of the MEDLINE and EMBASE databases was conducted to identify studies that assessed the diagnostic yield of DWI in patients with TGA. The pooled diagnostic yield of DWI in patients with TGA was calculated using the DerSimonian-Laird random-effects model. Subgroup analyses were also performed of slice thickness, magnetic field strength, and interval between symptom onset and DWI.

RESULTS

Twenty-two original articles (1732 patients) were included. The pooled incidence of right, left, and bilateral hippocampal lesions was 37% (95% confidence interval [CI], 30-44%), 42% (95% CI, 39-46%), and 25% (95% CI, 20-30%) of all lesions, respectively. The pooled diagnostic yield of DWI in patients with TGA was 39% (95% CI, 27-52%). The Higgins I² statistic showed significant heterogeneity (I² = 95%). DWI with a slice thickness ≤ 3 mm showed a higher diagnostic yield than DWI with a slice thickness > 3 mm (pooled diagnostic yield: 63% [95% CI, 53-72%] vs. 26% [95% CI, 16-40%], p < 0.01). DWI performed at an interval between 24 and 96 hours after symptom onset showed a higher diagnostic yield (68% [95% CI, 57-78%], p < 0.01) than DWI performed within 24 hours (16% [95% CI, 7-34%]) or later than 96 hours (15% [95% CI, 8-26%]). There was no difference in the diagnostic yield between DWI performed using 3T vs. 1.5T (pooled diagnostic yield, 31% [95% CI, 25-38%] vs. 24% [95% CI, 14-37%], p = 0.31).

CONCLUSION

The pooled diagnostic yield of DWI in TGA patients was 39%. DWI obtained with a slice thickness ≤ 3 mm or an interval between symptom onset and DWI of > 24 to 96 hours could increase the diagnostic yield.

Prognostic Utility of Disproportionately Enlarged Subarachnoid Space Hydrocephalus in Idiopathic Normal Pressure Hydrocephalus Treated with Ventriculoperitoneal Shunt Surgery: A Systematic Review and Meta-analysis

BACKGROUND

Disproportionately enlarged subarachnoid space hydrocephalus is a specific radiologic marker for idiopathic normal pressure hydrocephalus. However, controversy exists regarding the prognostic utility of disproportionately enlarged subarachnoid space hydrocephalus.

PURPOSE

Our aim was to evaluate the prevalence of disproportionately enlarged subarachnoid space hydrocephalus in idiopathic normal pressure hydrocephalus and its predictive utility regarding prognosis in patients treated with ventriculoperitoneal shunt surgery.

DATA SOURCES

We used MEDLINE and EMBASE databases.

STUDY SELECTION

We searched for studies that reported the prevalence or the diagnostic performance of disproportionately enlarged subarachnoid space hydrocephalus in predicting treatment response.

DATA ANALYSIS

The pooled prevalence of disproportionately enlarged subarachnoid space hydrocephalus was obtained. Pooled sensitivity, specificity, and area under the curve of disproportionately enlarged subarachnoid space hydrocephalus to predict treatment response were obtained. Subgroup and sensitivity analyses were performed to explain heterogeneity among the studies.

DATA SYNTHESIS

Ten articles with 812 patients were included. The pooled prevalence of disproportionately enlarged subarachnoid space hydrocephalus in idiopathic normal pressure hydrocephalus was 44% (95% CI, 34%-54%). The pooled prevalence of disproportionately enlarged subarachnoid space hydrocephalus was higher in the studies using the second edition of the Japanese Guidelines for Management of Idiopathic Normal Pressure Hydrocephalus compared with the studies using the international guidelines without statistical significance (52% versus 43%, P = .38). The pooled sensitivity and specificity of disproportionately enlarged subarachnoid space hydrocephalus for prediction of treatment response were 59% (95% CI, 38%-77%) and 66% (95% CI, 57%-74%), respectively, with an area under the curve of 0.67 (95% CI, 0.63-0.71).

LIMITATIONS

The lack of an established method for assessing disproportionately enlarged subarachnoid space hydrocephalus using brain MR imaging served as an important cause of the heterogeneity.

CONCLUSIONS

Our meta-analysis demonstrated a relatively low prevalence of disproportionately enlarged subarachnoid space hydrocephalus in idiopathic normal pressure hydrocephalus and a poor diagnostic performance for treatment response.

Clinical Features and Brain MRI Findings in Korean Patients with AGel Amyloidosis

PURPOSE

AGel amyloidosis is systemic amyloidosis caused by pathogenic variants in the GSN gene. In this study, we sought to characterize the clinical and brain magnetic resonance image (MRI) features of Korean patients with AGel amyloidosis.

MATERIALS AND METHODS

We examined 13 patients with AGel amyloidosis from three unrelated families. Brain MRIs were performed in eight patients and eight age- and sex-matched healthy controls. Therein, we analyzed gray and white matter content using voxel-based morphometry (VBM), tract-based spatial statistics (TBSS), and FreeSurfer.

RESULTS

The median age at examination was 73 (interquartile range: 64-76) years. The median age at onset of cutis laxa was 20 (interquartile range: 15-30) years. All patients over that age of 60 years had dysarthria, cutis laxa, dysphagia, and facial palsy. Two patients in their 30s had only mild cutis laxa. The median age at dysarthria onset was 66 (interquartile range: 63.5-70) years. Ophthalmoparesis was observed in three patients. No patient presented with muscle weakness of the limbs. Axial fluid-attenuated inversion recovery images of the brain showed no significant differences between the patient and control groups. Also, analysis of VBM, TBSS, and FreeSurfer revealed no significant differences in cortical thickness between patients and healthy controls at the corrected significance level.

CONCLUSION

Our study outlines the clinical manifestations of prominent bulbar palsy and early-onset cutis laxa in 13 Korean patients with AGel amyloidosis and confirms that AGel amyloidosis mainly affects the peripheral nervous system rather than the central nervous system.

Utility of 7 Tesla Magnetic Resonance Imaging in Patients With Epilepsy: A Systematic Review and Meta-Analysis

Objective: 7 Tesla magnetic resonance imaging (MRI) enables high resolution imaging and potentially improves the detection of morphologic abnormalities in patients with epilepsy. However, its added value compared with conventional 1.5T and 3.0T MRI is unclear. We reviewed the evidence for the use of 7 Tesla MRI in patients with epilepsy and compared the detection rate of focal lesions with clinical MRI. Methods: Clinical retrospective case studies were identified using the indexed text terms "epilepsy" AND "magnetic resonance imaging" OR "MR imaging" AND "7T" OR "7 Tesla" OR "7T" in Medline (2002-September 1, 2020) and Embase (1999-September 1, 2020). The study setting, MRI protocols, qualitative, and quantitative assessment were systematically reviewed. The detection rate of morphologic abnormalities on MRI was reported in each study in which surgery was used as the reference standard. Meta-analyses were performed using a univariate random-effects model in diagnostic performance studies with patients that underwent both 7T MRI and conventional MRI. Results: Twenty-five articles were included (467 patients and 167 healthy controls) consisting of 10 case studies, 10 case-control studies, 4 case series, and 1 cohort study. All studies included focal epilepsy; 12 studies (12/25, 48%) specified the disease etiology and 4 studies reported focal but non-lesional (MRI-negative on 1.5/3.0T) epilepsy. 7T MRI showed superior detection and delineation of morphologic abnormalities in all studies. In nine comparative studies, 7T MRI had a superior detection rate of 65% compared with the 22% detection rate of 1.5T or 3.0T. Significance: 7T MRI is useful for delineating morphologic abnormalities with a higher detection rate compared with conventional clinical MRI. Most studies were conducted using a case series or case study; therefore, a cohort study design with clinical outcomes is necessary. Classification of Evidence: Class IV Criteria for Rating Diagnostic Accuracy Studies.

Diagnostic performance and interobserver agreement of the callosal angle and Evans' index in idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic performance and interobserver agreement of the callosal angle and Evans' index in idiopathic normal pressure hydrocephalus (iNPH).

METHODS

A systematic search of MEDLINE and EMBASE was performed to find studies assessing the diagnostic performance or interobserver agreement of the callosal angle and Evans' index in iNPH. Pooled sensitivity and specificity of the two radiologic indices were calculated. The area under the curve (AUC) was obtained based on a hierarchical summary receiver operating characteristic curve. The diagnostic performances of both radiologic indices were compared in subgroup analysis. To evaluate interobserver agreement, the pooled correlation coefficient was calculated.

RESULTS

Ten original articles (874 patients) were included. The pooled sensitivity and specificity of the callosal angle in the diagnosis of iNPH were 91% (95% CI, 86-94%) and 93% (95% CI, 89-96%), respectively. The pooled sensitivity and specificity of Evans' index were 96% (95% CI, 47-100%) and 83% (95% CI, 77-88%), respectively. Subgroup analysis demonstrated a significant higher specificity of the callosal angle than that of Evans' index (p < 0.01). The AUC of the callosal angle and Evans' index were 0.97 (95% CI, 0.95-0.98) and 0.87 (95% CI, 0.84-0.90), respectively. The pooled correlation coefficients for the callosal angle and Evans' index were 0.92 (95% CI, 0.82-0.96) and 0.92 (95% CI, 0.83-0.97), respectively.

CONCLUSIONS

Our meta-analysis demonstrated a high performance of the callosal angle in the diagnosis of iNPH. Evans' index showed reasonable diagnostic performance with high sensitivity but low specificity. Interobserver agreements were excellent in both radiologic indices.

KEY POINTS

• Callosal angle showed high diagnostic performance in idiopathic normal pressure hydrocephalus. • Evans' index showed reasonable diagnostic performance with high sensitivity but low specificity. • Interobserver agreements were excellent in both callosal angle and Evans' index.

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

Background

High-resolution medical images that include facial regions can be used to recognize the subject’s face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data. According to the Health Insurance Portability and Accountability Act (HIPAA) privacy rules, full-face photographic images and any comparable image are direct identifiers and considered as protected health information. Moreover, the General Data Protection Regulation (GDPR) categorizes facial images as biometric data and stipulates that special restrictions should be placed on the processing of biometric data.

Objective

This study aimed to develop software that can remove the header information from Digital Imaging and Communications in Medicine (DICOM) format files and facial features (eyes, nose, and ears) at the 2D sliced-image level to anonymize personal information in medical images.

Methods

A total of 240 cranial magnetic resonance (MR) images were used to train the deep learning model (144, 48, and 48 for the training, validation, and test sets, respectively, from the Alzheimer's Disease Neuroimaging Initiative [ADNI] database). To overcome the small sample size problem, we used a data augmentation technique to create 576 images per epoch. We used attention-gated U-net for the basic structure of our deep learning model. To validate the performance of the software, we adapted an external test set comprising 100 cranial MR images from the Open Access Series of Imaging Studies (OASIS) database.

Results

The facial features (eyes, nose, and ears) were successfully detected and anonymized in both test sets (48 from ADNI and 100 from OASIS). Each result was manually validated in both the 2D image plane and the 3D-rendered images. Furthermore, the ADNI test set was verified using Microsoft Azure's face recognition artificial intelligence service. By adding a user interface, we developed and distributed (via GitHub) software named “Deface program” for medical images as an open-source project.

Conclusions

We developed deep learning–based software for the anonymization of MR images that distorts the eyes, nose, and ears to prevent facial identification of the subject in reconstructed 3D images. It could be used to share medical big data for secondary research while making both data providers and recipients compliant with the relevant privacy regulations.

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images

BACKGROUND AND PURPOSE

Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.

MATERIALS AND METHODS

A deep learning-based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls.

RESULTS

In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758-0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668-0.870. XGBoost had a sensitivity of 79% and a specificity of 80%.

CONCLUSIONS

The deep learning-based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.

Comparison of Dynamic Contrast-Enhancement Parameters between Gadobutrol and Gadoterate Meglumine in Posttreatment Glioma: A Prospective Intraindividual Study

BACKGROUND AND PURPOSE

Differences in molecular properties between one-molar and half-molar gadolinium-based contrast agents are thought to affect parameters obtained from dynamic contrast-enhanced imaging. The aim of our study was to investigate differences in dynamic contrast-enhanced parameters between one-molar nonionic gadobutrol and half-molar ionic gadoterate meglumine in patients with posttreatment glioma.

MATERIALS AND METHODS

This prospective study enrolled 32 patients who underwent 2 20-minute dynamic contrast-enhanced examinations, one with gadobutrol and one with gadoterate meglumine. The model-free parameter of area under the signal intensity curve from 30 to 1100 seconds and the Tofts model-based pharmacokinetic parameters were calculated and compared intraindividually using paired t tests. Patients were further divided into progression (n = 12) and stable (n = 20) groups, which were compared using Student t tests.

RESULTS

Gadobutrol and gadoterate meglumine did not show any significant differences in the area under the signal intensity curve or pharmacokinetic parameters of K ^trans, V_e, V_p, or K_ep (all P > .05). Gadobutrol showed a significantly higher mean wash-in rate (0.83 ± 0.64 versus 0.29 ± 0.63, P = .013) and a significantly lower mean washout rate (0.001 ± 0.0001 versus 0.002 ± 0.002, P = .02) than gadoterate meglumine. Trends toward higher area under the curve, K ^trans, V_e, V_p, wash-in, and washout rates and lower K_ep were observed in the progression group in comparison with the treatment-related-change group, regardless of the contrast agent used.

CONCLUSIONS

Model-free and pharmacokinetic parameters did not show any significant differences between the 2 gadolinium-based contrast agents, except for a higher wash-in rate with gadobutrol and a higher washout rate with gadoterate meglumine, supporting the interchangeable use of gadolinium-based contrast agents for dynamic contrast-enhanced imaging in patients with posttreatment glioma.

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients

BACKGROUND

Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.

METHODS

The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).

RESULTS

Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.

CONCLUSIONS

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Disrupted Functional and Structural Connectivity in Angelman Syndrome

BACKGROUND AND PURPOSE

This work investigated alterations in functional connectivity (FC) and associated structures in patients with Angelman syndrome (AS) by using integrated quantitative imaging analysis and connectivity measures.

MATERIALS AND METHODS

We obtained 3T brain MR imaging, including resting-state functional MR imaging, diffusion tensor imaging, and 3D T1-weighted imaging from children with AS (n = 14) and age- and sex-matched controls (n = 28). The brains of patients with AS were analyzed by measuring FC, white matter microstructural analysis, cortical thickness, and brain volumes; these were compared with brains of controls.

RESULTS

Interregional FC analysis revealed significantly reduced intra- and interhemispheric FC, especially in the basal ganglia and thalamus, in patients with AS. Significant reductions in fractional anisotropy were found in the corpus callosum, cingulum, posterior limb of the internal capsules, and arcuate fasciculus in patients with AS. Quantitative structural analysis also showed gray matter volume loss of the basal ganglia and diffuse WM volume reduction in AS compared with the control group.

CONCLUSIONS

This integrated quantitative MR imaging analysis demonstrated poor functional and structural connectivity, as well as brain volume reduction, in children with AS, which may explain the motor and language dysfunction observed in this well-characterized neurobehavioral phenotype.

Altered Structural Network in Newly Onset Childhood Absence Epilepsy

BACKGROUND AND PURPOSE

Recent quantitative neuroimaging studies of childhood absence epilepsy (CAE) have identified various structural abnormalities that might be involved in the onset of absence seizure and associated cognitive and behavioral functions. However, the neuroanatomical alterations specific to CAE remain unclear, and so this study investigated the regional alterations of brain structures associated with newly diagnosed CAE.

METHODS

Surface and volumetric magnetic resonance imaging data of patients with newly diagnosed CAE (n=18) and age-matched healthy controls (n=18) were analyzed using Free-Surfer software. A group comparison using analysis of covariance was performed with significance criteria of p<0.05 and p<0.01 in global and regional analyses, respectively.

RESULTS

Compared with control subjects, the patients with CAE had smaller total and regional volumes of cortical gray-matter (GM) in the right rostral middle frontal, right lateral orbitofrontal, and left rostral middle frontal regions, as well as in the right precentral, right superior, middle, left middle, and inferior temporal gyri. The cortex in the right posterior cingulate gyrus and left medial occipital region was significantly thicker in patients with CAE than in controls.

CONCLUSIONS

Patients with CAE showed a reduced bilateral frontotemporal cortical GM volume and an increased posterior medial cortical thickness, which are associated with the default mode network. These structural changes can be suggested as the neural basis of the absence seizures and neuropsychiatric comorbidities in CAE.

Perilesional and homotopic area activation during proverb comprehension after stroke

INTRODUCTION

The mechanism of functional recovery in right hemisphere (RH) stroke patients when attempting to comprehend a proverb has not been identified. We previously reported that there is bilateral hemisphere involvement during proverb comprehension in the normal population. However, the underlying mechanisms of proverb comprehension following a right middle cerebral artery (MCA) infarction have not yet been fully elucidated.

METHODS

We here compared the brain regions activated by literal sentences and by opaque or transparent proverbs in right MCA infarction patients using functional magnetic resonance imaging (fMRI). Experimental stimuli included 18 opaque proverbs, 18 transparent proverbs, and 18 literal sentences that were presented pseudorandomly in 1 of 3 predesigned sequences.

RESULTS

Fifteen normal adults and 17 right MCA infarction patients participated in this study. The areas of the brain in the stroke patients involved in understanding a proverb compared with a literal sentence included the right middle frontal gyrus (MFG) and frontal pole, right anterior cingulate gyrus/paracingulate gyrus and left inferior frontal gyrus (IFG), middle temporal gyrus (MTG), precuneus, and supramarginal gyrus (SMG). When the proverbs were presented to these stroke patients in the comprehension tests, the left supramarginal, postcentral gyrus, and right paracingulate gyrus were activated for the opaque proverbs compared to the transparent proverbs.

CONCLUSIONS

These findings suggest that the functional recovery of language in stroke patients can be explained by perilesional activation, which is thought to arise from the regulation of the excitatory and inhibitory neurotransmitter system, and by homotopic area activation which has been characterized by decreased transcallosal inhibition and astrocyte involvement.

Early treadmill exercise increases macrophage migration inhibitory factor expression after cerebral ischemia/reperfusion

The neuroprotective function of macrophage migration inhibitory factor (MIF) in ischemic stroke was rarely evaluated. This study aimed to investigate the effects of early treadmill exercise on recovery from ischemic stroke and to determine whether these effects are associated with the expression levels of MIF and brain-derived neurotrophic factor (BDNF) in the ischemic area. A total of 40 male Sprague-Dawley rats were randomly assigned to the ischemia and exercise group [middle cerebral artery occlusion (MCAO)-Ex, n = 10), ischemia and sedentary group (MCAO-St, n = 10), sham-surgery and exercise group (Sham-Ex, n = 10), or sham-surgery and sedentary group (Sham-St, n = 10). The MCAO-Ex and MCAO-St groups were subjected to MCAO for 60 minutes, whereas the Sham-Ex and Sham-St groups were subjected to an identical operation without MCAO. Rats in the MCAO-Ex and Sham-Ex groups then ran on a treadmill for 30 minutes once a day for 5 consecutive days. After reperfusion, the hanging time tested by the wire hang test was longer and the relative fractional anisotropy determined by MRI was higher in the peri-infarct region of the MCAO-Ex group compared with the MCAO-St group. The expression levels of MIF and BDNF in the peri-infarct region were upregulated in the MCAO-Ex group. Increased MIF and BDNF levels were positively correlated with relative fractional anisotropy changes in the peri-infarct region. There was no significant difference in the levels of MIF and BDNF in the peri-infarct region between the Sham-Ex and Sham-St groups. Our study demonstrated that early exercise (initiated 48 hours after the MCAO) could improve motor and neuronal recovery after ischemic stroke. Furthermore, the increased levels of MIF and BDNF in the peri-infarct region (penumbra) may be one of the mechanisms of enhanced neurological function recovery. All experiments were approved by the Institutional Animal Care and Use Committee in Asan Medical Center in South Korea (2016-12-126).

Hyperoxia-Induced ΔR1

Background and Purpose- Acceleration of longitudinal relaxation under hyperoxic challenge (ie, hyperoxia-induced ΔR₁) indicates oxygen accumulation and reflects baseline tissue oxygenation. We evaluated the feasibility of hyperoxia-induced ΔR₁ for evaluating cerebral oxygenation status and degree of ischemic damage in stroke. Methods- In 24-hour transient stroke rat models (n=13), hyperoxia-induced ΔR₁, ischemic severity (apparent diffusion coefficient [ADC]), vasogenic edema (R₂), total and microvascular blood volume (superparamagnetic iron oxide-driven ΔR₂* and ΔR₂, respectively), and glucose metabolism activity (¹⁸F-fluorodeoxyglucose uptake on positron emission tomography) were measured. The distribution of these parameters according to hyperoxia-induced ΔR₁ was analyzed. The partial pressure of tissue oxygen change during hyperoxic challenge was measured using fiberoptic tissue oximetry. In 4-hour stroke models (n=6), ADC and hyperoxia-induced ΔR₁ was analyzed with 2,3,5-triphenyltetrazolium chloride staining being a criterion of infarction. Results- Ischemic hemisphere showed significantly higher hyperoxia-induced ΔR₁ than nonischemic brain in a pattern depending on ADC. During hyperoxic challenge, ischemic hemisphere demonstrated uncontrolled increase of partial pressure of tissue oxygen, whereas contralateral hemisphere rapidly plateaued. Ischemic hemisphere also demonstrated significant correlation between hyperoxia-induced ΔR₁ and R₂. Hyperoxia-induced ΔR₁ showed a significant negative correlation with ¹⁸F-fluorodeoxyglucose uptake. The ADC, R₂, ΔR₂, and ¹⁸F-fluorodeoxyglucose uptake showed a dichotomized distribution according to the hyperoxia-induced ΔR₁ as their slopes and values were higher at low hyperoxia-induced ΔR₁ (<50 ms^-1) than at high ΔR₁. In 4-hour stroke rats, the distribution of ADC according to the hyperoxia-induced ΔR₁ was similar with 24-hour stroke rats. The hyperoxia-induced ΔR₁ was greater in the infarct area (47±10 ms^-1) than in peri-infarct area (16±4 ms^-1; P<0.01). Conclusions- Hyperoxia-induced ΔR₁ adequately indicates cerebral oxygenation and can be a feasible biomarker to classify the degree of ischemia-induced damage in neurovascular function and metabolism in stroke brain.

Correction to: Contribution of Zinc-Dependent Delayed Calcium Influx via TRPC5 in Oxidative Neuronal Death and its Prevention by Novel TRPC Antagonist

After the publication of this work errors were noticed in Fig. 3b and 4d.

Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis

Purpose To develop and validate a radiomics-based model for staging liver fibrosis by using gadoxetic acid-enhanced hepatobiliary phase MRI. Materials and Methods In this retrospective study, 436 patients (mean age, 51 years; age range, 18-86 years; 319 men [mean age, 51 years; age range, 18-86 years]; 117 women [mean age, 50 years; age range, 18-79 years]) with pathologic analysis-proven liver fibrosis who underwent gadoxetic acid-enhanced MRI from June 2015 to December 2016 were randomized in a three-to-one ratio into development (n = 329) and test (n = 107) cohorts, respectively. In the development cohort, a model was developed to calculate radiomics fibrosis index (RFI) by using logistic regression with elastic net regularization to differentiate stage F3-F4 from stage F0-F2. Optimal RFI cutoffs to diagnose clinically significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4), and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. In the test cohort, the diagnostic performance of RFI was compared with that of normalized liver enhancement, aspartate transaminase-to-platelet ratio index (APRI), and fibrosis-4 index by using the Obuchowski index. Results In the test cohort, RFI (Obuchowski index, 0.86) significantly outperformed normalized liver enhancement (Obuchowski index, 0.77; P < .03), APRI (Obuchowski index, 0.60; P < .001), and fibrosis-4 index (Obuchowski index, 0.62; P < .001) for staging liver fibrosis. By using the cutoffs, RFI had sensitivities and specificities as follows: 81% (95% confidence interval: 71%, 89%) and 78% (95% confidence interval: 63%, 89%) for diagnosing stage F2-F4, respectively; 79% (95% confidence interval: 67%, 88%) and 82% (95% confidence interval: 69%, 91%), respectively, for diagnosing stage F3-F4; and 92% (95% confidence interval: 79%, 98%) and 75% (95% confidence interval: 62%, 83%), respectively, for diagnosing stage F4. Conclusion Radiomics analysis of gadoxetic acid-enhanced hepatobiliary phase images allows for accurate diagnosis of liver fibrosis. © RSNA, 2018 Online supplemental material is available for this article.

Reliability of fast magnetic resonance imaging for acute ischemic stroke patients using a 1.5-T scanner

OBJECTIVES

To determine whether fast scanned MRI using a 1.5-T scanner is a reliable method for the detection and characterization of acute ischemic stroke in comparison with conventional MRI.

METHODS

From May 2015 to June 2016, 862 patients (FLAIR, n = 482; GRE, n = 380; MRA, n = 190) were prospectively enrolled in the study, with informed consent and under institutional review board approval. The patients underwent both fast (EPI-FLAIR, ETL-FLAIR, TR-FLAIR, EPI-GRE, parallel-GRE, fast CE-MRA) and conventional MRI (FLAIR, GRE, time-of-flight MRA, fast CE-MRA). Two neuroradiologists independently assessed agreements in acute and chronic ischemic hyperintensity, hyperintense vessels (FLAIR), microbleeds, susceptibility vessel signs, hemorrhagic transformation (GRE), stenosis (MRA), and image quality (all MRI), between fast and conventional MRI. Agreements between fast and conventional MRI were evaluated by generalized estimating equations. Z-scores were used for comparisons of the percentage agreement among fast FLAIR sequences and fast GRE sequences and between conventional and fast MRA.

RESULTS

Agreements of more than 80% were achieved between fast and conventional MRI (ETL-FLAIR, 96%; TR-FLAIR, 97%; EPI-GRE, 96%; parallel-GRE, 98%; fast CE-MRA, 86%). ETL- and TR-FLAIR were significantly superior to EPI-FLAIR in the detection of acute ischemic hyperintensity and hyperintense vessels, while parallel-GRE was significantly superior to EPI-GRE in the detection of susceptibility vessel sign (p value < 0.05 for all). There were no significant differences in the other scores and image qualities (p value > 0.05).

CONCLUSIONS

Fast MRI at 1.5 T is a reliable method for the detection and characterization of acute ischemic stroke in comparison with conventional MRI.

KEY POINTS

• Fast MRI at 1.5 T may achieve a high intermethod reliability in the detection and characterization of acute ischemic stroke with a reduction in scan time in comparison with conventional MRI.

Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study

BACKGROUND

The presence of metastatic lymph nodes is a prognostic indicator for patients with thyroid carcinomas and is an important determinant of clinical decision making. However, evaluating neck lymph nodes requires experience and is labor- and time-intensive. Therefore, the development of a computer-aided diagnosis (CAD) system to identify and differentiate metastatic lymph nodes may be useful.

METHODS

From January 2008 to December 2016, we retrieved clinical records for 804 consecutive patients with 812 lymph nodes. The status of all lymph nodes was confirmed by fine-needle aspiration. The datasets were split into training (263 benign and 286 metastatic lymph nodes), validation (30 benign and 33 metastatic lymph nodes), and test (100 benign and 100 metastatic lymph nodes). Using the VGG-Class Activation Map model, we developed a CAD system to localize and differentiate the metastatic lymph nodes. We then evaluated the diagnostic performance of this CAD system in our test set.

RESULTS

In the test set, the accuracy, sensitivity, and specificity of our model for predicting lymph node malignancy were 83.0%, 79.5%, and 87.5%, respectively. The CAD system clearly detected the locations of the lymph nodes, which not only provided identifying data, but also demonstrated the basis of decisions.

CONCLUSION

We developed a deep learning-based CAD system for the localization and differentiation of metastatic lymph nodes from thyroid cancer on ultrasound. This CAD system is highly sensitive and may be used as a screening tool; however, as it is relatively less specific, the screening results should be validated by experienced physicians.

Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver

Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal venous phase CT images in 7461 patients with pathologically confirmed liver fibrosis. The diagnostic performance of the DLS was evaluated in separate test data sets for 891 patients. The influence of patient characteristics and CT techniques on the staging accuracy of the DLS was evaluated by logistic regression analysis. In a subset of 421 patients, the diagnostic performance of the DLS was compared with that of the radiologist's assessment, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index. Results In the test data sets, the DLS had a staging accuracy of 79.4% (707 of 891) and an AUROC of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4), respectively. At multivariable analysis, only pathologic fibrosis stage significantly affected the staging accuracy of the DLS (P = .016 and .013 for F1 and F2, respectively, compared with F4), whereas etiology of liver disease and CT technique did not. The DLS (Obuchowski index, 0.94) outperformed the radiologist's interpretation, APRI, and fibrosis-4 index (Obuchowski index range, 0.71-0.81; P ˂ .001) for staging liver fibrosis. Conclusion The deep learning system allows for accurate staging of liver fibrosis by using CT images. © RSNA, 2018 Online supplemental material is available for this article.