Quantification of infarct core volume in patients with acute ischemic stroke using cerebral metabolic rate of oxygen (CMRO2) in CT perfusion.

BACKGROUND AND PURPOSE: The cerebral metabolic rate of oxygen (CMRO2) is considered a robust marker of the infarct core in 15°-tracer- based positron emission tomography. We aimed to delineate the infarct core in patients with acute ischemic stroke using commonly used relative cerebral blood flow (rCBF) < 30% and oxygen metabolism parameter of CMRO2 on CT perfusion in comparison with pre-treatment diffusion- weighted imaging (DWI)-derived infarct core volume. MATERIALS AND METHODS: Patients with acute ischemic stroke who met the inclusion criteria were recruited. The CMRO2 and CBF maps in CT perfusion were automatically generated using post-processing software. The infarct core volume was quantified with relative (r) CMRO2 < 20% - 30% and rCBF < 30%. The optimal threshold was defined as those that demonstrated the smallest mean absolute error, lowest mean infarct core volume difference, narrowest 95% limit of agreement, and largest intraclass correlation coefficient (ICC) against the DWI. RESULTS: This study included 76 patients (mean age ± standard deviation, 69.97 ± 12.15 years, 43 males). The optimal thresholds of rCMRO2 < 26% resulted in the lowest mean infarct core volume difference, narrowest 95% limit of agreement, and largest ICC among different thresholds. Bland-Altman analysis demonstrated a volumetric bias of 1.96 mL between DWI and rCMRO2 < 26%, whereas in cases of DWI and rCBF < 30%, the bias was notably larger at 14.10 mL. The highest correlation was observed for rCMRO2 < 26% (ICC=0.936), whereas rCBF < 30% showed a slightly lower ICC of 0.934. CONCLUSIONS: CT perfusion-derived CMRO2 is a promising parameter for estimating the infarct core volume in patients with acute ischemic stroke. ABBREVIATIONS: CMRO2 = cerebral metabolic rate of oxygen.

Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging.

OBJECTIVE: The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Glymphatic system dysfunction has been observed in both multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), indicating the role of neuroinflammation in the glymphatic system. However, little is known about how the two diseases differently affect the human glymphatic system. The present study aims to evaluate the diffusion MRI-based measures of the glymphatic system by contrasting MS and NMOSD. METHODS: This prospective study included 63 patients with NMOSD (n = 21) and MS (n = 42) who underwent DTI. The fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. Age and EDSS scores were adjusted. RESULTS: Using Bayesian hypothesis testing, we show that the present data substantially favor the null model of no differences between MS and NMOSD for the diffusion MRI-based measures of the glymphatic system. The inclusion Bayes factor (BF10) of model-averaged probabilities of the group (MS, NMOSD) was 0.280 for FW and 0.236 for the ALPS index. CONCLUSION: Together, these findings suggest that glymphatic alteration associated with MS and NMOSD might be similar and common as an eventual result, albeit the disease etiologies differ. PRACTITIONER POINTS: Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis.

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS: For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION: The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas. METHODS: In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score. RESULTS: The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance. CONCLUSIONS: The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Structural disconnection is associated with disability in the neuromyelitis optica spectrum disorder.

OBJECTIVES: Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune inflammatory disease of the central nervous system. Accumulating evidence suggests there is a distinct pattern of brain lesions characteristic of NMOSD, and brain MRI has potential prognostic implications. However, the question of how the brain lesions in NMOSD are associated with its distinct clinical course remains incompletely understood. Here, we aimed to investigate the association between neurological impairment and brain lesions via brain structural disconnection. METHODS: Twenty patients were diagnosed with NMOSD according to the 2015 International Panel for NMO Diagnosis criteria. The white matter lesions were manually drawn section by section. Whole-brain structural disconnection was estimated, and connectome-based predictive modeling (CPM) was used to estimate the patient's Expanded Disability Status Scale score (EDSS) from their disconnection severity matrix. Furthermore, correlational tractography was performed to assess the fractional anisotropy (FA) and axial diffusivity (AD) of white matter fibers, which negatively correlated with the EDSS score. RESULTS: CPM successfully predicted the EDSS using the disconnection severity matrix (r = 0.506, p = 0.028; q2 = 0.274). Among the important edges in the prediction process, the majority of edges connected the motor to the frontoparietal network. Correlational tractography identified a decreased FA and AD value according to EDSS scores in periependymal white matter tracts. DISCUSSION: Structural disconnection-based predictive modeling and local connectome analysis showed that frontoparietal and periependymal white matter disconnection is predictive and associated with the EDSS score of NMOSD patients.

Validation of Ultrasound and Computed Tomography-Based Risk Stratification System and Biopsy Criteria for Cervical Lymph Nodes in Preoperative Patients With Thyroid Cancer.

OBJECTIVE: This study aimed to validate the risk stratification system (RSS) and biopsy criteria for cervical lymph nodes (LNs) proposed by the Korean Society of Thyroid Radiology (KSThR). MATERIALS AND METHODS: This retrospective study included a consecutive series of preoperative patients with thyroid cancer who underwent LN biopsy, ultrasound (US), and computed tomography (CT) between December 2006 and June 2015. LNs were categorized as probably benign, indeterminate, or suspicious according to the current US- and CT-based RSS and the size thresholds for cervical LN biopsy as suggested by the KSThR. The diagnostic performance and unnecessary biopsy rates were calculated. RESULTS: A total of 277 LNs (53.1% metastatic) in 228 patients (mean age ± standard deviation, 47.4 years ± 14) were analyzed. In US, the malignancy risks were significantly different among the three categories (all P < 0.001); however, CT-detected probably benign and indeterminate LNs showed similarly low malignancy risks (P = 0.468). The combined US + CT criteria stratified the malignancy risks among the three categories (all P < 0.001) and reduced the proportion of indeterminate LNs (from 20.6% to 14.4%) and the malignancy risk in the indeterminate LNs (from 31.6% to 12.5%) compared with US alone. In all image-based classifications, nodal size did not affect the malignancy risks (short diameter [SD] ≤ 5 mm LNs vs. SD > 5 mm LNs, P ≥ 0.177). The criteria covering only suspicious LNs showed higher specificity and lower unnecessary biopsy rates than the current criteria, while maintaining sensitivity in all imaging modalities. CONCLUSION: Integrative evaluation of US and CT helps in reducing the proportion of indeterminate LNs and the malignancy risk among them. Nodal size did not affect the malignancy risk of LNs, and the addition of indeterminate LNs to biopsy candidates did not have an advantage in detecting LN metastases in all imaging modalities.

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Progressive reduction in basal ganglia explains and predicts cerebral structural alteration in type 2 diabetes.

Type 2 diabetes is consistently reported to be associated with reduced gray matter, mainly in the cortical-striatal-limbic networks. However, little is known about how the progression of diabetes affects cerebral gray matter. To investigate, we collected 543 age- and sex-matched participants of nondiabetes, prediabetes, and diabetes. Voxel-based morphometry using a linear trend model was performed to reveal brain regions associated with disease progression. The Granger causal network of structural covariance was used to assess the causal relationships of brain structural alterations according to disease progression. Multivariate pattern analysis was applied for the stage-specific predictions of hyperglycemia. We detected a linear trend of gray matter volume reduction in the basal ganglia with disease progression (P < 0.05, FWER corrected), which caused a reduction in bilateral temporal gyri, frontal pole, parahippocampus, and bilateral posterior cingulate/precuneus volumes. In addition, the gray matter pattern of the basal ganglia could predict patients with diabetes (accuracy 60.12%, p = 0.002). In conclusion, the basal ganglia is the brain area with progressive gray matter reduction as diabetes progress. The reduced volume in the basal ganglia causes widespread gray matter reductions throughout diabetes progression. These findings indicate that the basal ganglia play a key role in diabetes by affecting the cortical-striatal-limbic network.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS: Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS: The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION: DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT: The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS: • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

Quantifying infarct core volume in ischemic stroke: What is the optimal threshold and parameters of computed tomography perfusion?

OBJECTIVE: Although computed tomography perfusion (CTP) is used to select and guide decision-making processes in patients with acute ischemic stroke, there is no clear standardization of the optimal threshold to predict ischemic core volume accurately. The infarct core volume with a relative cerebral blood flow(rCBF) threshold of < 30% is commonly used. We aimed to assess the volumetric agreement of the infarct core volume with different CTP parameters and thresholds using CTP software (RAPID, VITREA) and the infarct volume on diffusion-weighted imaging (DWI), with a short interval time (within 60 min) between CTP and follow-up DWI. MATERIALS AND METHODS: This retrospective study included 42 acute ischemic stroke patients with occlusion of the large artery in the anterior circulation between April 2017-November 2020. RAPID identified infarct core as tissue rCBF < 20-38%. VITREA defined the infarct core as cerebral blood volume (CBV) < 26-56%. Olea Sphere was used to measure infarct core volume on DWI. The CTP-infarct core volume with different thresholds of perfusion parameters (CBF threshold vs CBV threshold) were compared with DWI-infarct core volumes. RESULTS: The median time between CTP and DWI was 37.5min. The commonly used threshold of CBV< 41% (4.3 mL) resulted in lower median infarct core volume difference compared to the commonly used thresholds of rCBF < 30% (8.2mL). On the other hand, the optimal thresholds of CBV < 26% (-1.0mL; 95% CI, -53.9 to 58.1 mL; 0.945) resulted in the lowest median infarct core volume difference, narrowest limits of agreement, and largest interclass correlation coefficient compared with the optimal thresholds of rCBF < 38% (4.9 mL; 95% CI, -36.4 to 62.9 mL; 0.939). CONCLUSION: Our study found that the both optimal and commonly used thresholds of CBV provided a more accurate prediction of the infarct core volume in patients with AIS than rCBF.

Malignancy risk stratification of thyroid nodules according to echotexture and degree of hypoechogenicity: a retrospective multicenter validation study.

Various risk stratification systems show discrepancies in the ultrasound lexicon of nodule echotexture and hypoechogenicity. This study aimed to determine the malignancy risk of thyroid nodules according to their echotexture and degree of hypoechogenicity. From June to September 2015, we retrospectively evaluated 5601 thyroid nodules with final diagnoses from 26 institutions. Nodules were stratified according to the echotexture (homogeneous vs. heterogeneous) and degree of hypoechogenicity (mild, moderate, or marked). We calculated the malignancy risk according to composition and suspicious features. Heterogeneous hypoechoic nodules showed a significantly higher malignancy risk than heterogeneous isoechoic nodules (P ≤ 0.017), except in partially cystic nodules. Malignancy risks were not significantly different between homogeneous versus heterogeneous nodules in both hypoechoic (P ≥ 0.086) and iso- hyperechoic nodules (P ≥ 0.05). Heterogeneous iso-hyperechoic nodules without suspicious features showed a low malignancy risk. The malignancy risks of markedly and moderately hypoechoic nodules were not significantly different in all subgroups (P ≥ 0.48). Marked or moderately hypoechoic nodules showed a significantly higher risk than mild hypoechoic (P ≤ 0.016) nodules. The predominant echogenicity effectively stratifies the malignancy risk of nodules with heterogeneous echotexture. The degree of hypoechogenicity could be stratified as mild versus moderate to marked hypoechogenicity.

An exploratory study on functional connectivity after mild traumatic brain injury: Preserved global but altered local organization.

INTRODUCTION: This study aimed to investigate alterations in whole-brain functional connectivity after a concussion using graph-theory analysis from global and local perspectives and explore the association between changes in the functional network properties and cognitive performance. METHODS: Individuals with mild traumatic brain injury (mTBI, n = 29) within a month after injury, and age- and sex-matched healthy controls (n = 29) were included. Graph-theory measures on functional connectivity assessed using resting state functional magnetic resonance imaging data were acquired from each participant. These included betweenness centrality, strength, clustering coefficient, local efficiency, and global efficiency. Multi-domain cognitive functions were correlated with the graph-theory measures. RESULTS: In comparison to the controls, the mTBI group showed preserved network characteristics at a global level. However, in the local network, we observed decreased betweenness centrality, clustering coefficient, and local efficiency in several brain areas, including the fronto-parietal attention network. Network strength at the local level showed mixed-results in different areas. The betweenness centrality of the right parahippocampus showed a significant positive correlation with the cognitive scores of the verbal learning test only in the mTBI group. CONCLUSION: The intrinsic functional connectivity after mTBI is preserved globally, but is suboptimally organized locally in several areas. This possibly reflects the neurophysiological sequelae of a concussion. The present results may imply that the network property could be used as a potential indicator for clinical outcomes after mTBI.

Proliferative Vasculopathy Associated With Antiphospholipid Antibodies in Patients With Neurological Symptoms.

Background: Proliferative vasculopathy (PV) associated antiphospholipid syndrome (APS) in the central nervous system is a rare un(der)recognized form of extra-criteria manifestations of APS. This study investigated the angiographic characteristics of cerebral and cervical arteries in patients with PV associated with antiphospholipid antibodies (aPLs). Methods: Patients with aPLs, neurologic symptoms and diffuse luminal narrowing on brain or neck magnetic resonance angiography were selected from electronic medical records. Vascular wall and intraluminal pathology were examined by high-resolution vessel wall MR imaging (VW-MRI). Results: A total of 11 patients (six men and five women) with PV-aPL, of median (interquartile range) age 42 (34-61) years, were included. Median anticardiolipin antibodies IgG titer was 78.9 (28.2-134.0) units and anti-beta 2 glycoprotein I antibodies (aB2GPIs) IgG titer was 227.2 (0.0-1012.1) units. All patients had diffuse luminal narrowing in the carotid basilar and/or cerebral arteries, five in the internal carotid artery (ICA); two each in the middle cerebral artery (MCA) and vertebral artery; and one each in the basilar artery (BA) and posterior cerebral artery. On VW-MRI, four patients showed concentric thickening of the vascular walls of the ICA and/or MCA and two showed mild eccentric wall thickening of the ICA or BA. All patients received antithrombotic treatment. In two patients with extremely high aB2GPIs titer, diffuse narrowing progressed despite treatment with antithrombotic agents on follow-up imaging. Conclusions: This study suggests that PV-aPL might be a distinct extra-criteria manifestation of APS that can manifest as long-segmental diffuse stenosis of cerebral and cervical arteries. It should be considered in relatively young patients with neurologic symptoms and aPLs.

Validation of Ultrasound Risk Stratification Systems for Cervical Lymph Node Metastasis in Patients with Thyroid Cancer.

A malignancy risk stratification system (RSS) for cervical lymph nodes (LNs) has not been fully established. This study aimed to validate the current RSS for the diagnosis of cervical LN metastasis in thyroid cancer. In total, 346 LNs from 282 consecutive patients between December 2006 and June 2015 were included. We determined the malignancy risk of each ultrasound (US) feature and performed univariable and multivariable logistic regression analyses. Each risk category from the Korean Society of Thyroid Radiology (KSThR) and the European Thyroid Association (ETA) was applied to calculate malignancy risks. The effects of size, number of suspicious features, and primary tumor characteristics were analyzed to refine the current RSS. Suspicious features including echogenic foci, cystic change, hyperechogenicity, and abnormal vascularity were independently predictive of malignancy (p ≤ 0.045). The malignancy risks of probably benign, indeterminate, and suspicious categories were 2.2-2.5%, 26.8-29.0%, and 85.8-87.4%, respectively, according to the KSThR and ETA criteria. According to the ETA criteria, 15.1% of LNs were unclassifiable. In indeterminate LNs, multiplicity of the primary tumor was significantly associated with malignancy (odds ratio, 6.53; p = 0.004). We refined the KSThR system and proposed a US RSS for LNs in patients with thyroid cancer.

Free-water diffusion tensor imaging detects occult periependymal abnormality in the AQP4-IgG-seropositive neuromyelitis optica spectrum disorder.

To compare free-water corrected diffusion tensor imaging (DTI) measures in the normal-appearing periependymal area between AQP4-IgG-seropositive NMOSD and multiple sclerosis (MS) to investigate occult pathophysiology. This prospective study included 44 patients (mean age, 39.52 ± 11.90 years; 14 men) with AQP4-IgG-seropositive NMOSD (n = 20) and MS (n = 24) who underwent DTI between April 2014 and April 2020. Based on free-water corrected DTI measures obtained from normal-appearing periependymal voxels of (1) lateral ventricles and (2) the 3rd and 4th ventricles as dependent variables, MANCOVA was conducted to compare the two groups, using clinical variables as covariates. A significant difference was found between AQP4-IgG-seropositive NMOSD and MS in the 3rd and 4th periependymal voxels (λ = 0.462, P = 0.001). Fractional anisotropy, axial diffusivity was significantly decreased and radial diffusivity was increased in AQP4-IgG-seropositive NMOSD in post-hoc analysis, compared with MS (F = 27.616, P < 0.001, F = 7.336, P = 0.011, and F = 5.800, P = 0.022, respectively). Free-water corrected DTI measures differ in the periependymal area surrounding the diencephalon and brain stem/cerebellum between MS and NMOSD, which may suggest occult white matter injury in areas with distribution of AQP-4 in NMOSD.

Myelin Content in Mild Traumatic Brain Injury Patients with Post-Concussion Syndrome: Quantitative Assessment with a Multidynamic Multiecho Sequence.

OBJECTIVE: This study aimed to explore the myelin volume change in patients with mild traumatic brain injury (mTBI) with post-concussion syndrome (PCS) using a multidynamic multiecho (MDME) sequence and automatic whole-brain segmentation. MATERIALS AND METHODS: Forty-one consecutive mTBI patients with PCS and 29 controls, who had undergone MRI including the MDME sequence between October 2016 and April 2018, were included. Myelin volume fraction (MVF) maps were derived from the MDME sequence. After three dimensional T1-based brain segmentation, the average MVF was analyzed at the bilateral cerebral white matter (WM), bilateral cerebral gray matter (GM), corpus callosum, and brainstem. The Mann-Whitney U-test was performed to compare MVF and myelin volume between patients with mTBI and controls. Myelin volume was correlated with neuropsychological test scores using the Spearman rank correlation test. RESULTS: The average MVF at the bilateral cerebral WM was lower in mTBI patients with PCS (median [interquartile range], 25.2% [22.6%-26.4%]) than that in controls (26.8% [25.6%-27.8%]) (p = 0.004). The region-of-interest myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (1.87 cm3 [1.70-2.05 cm3] vs. 2.21 cm3 [1.86-3.46 cm3]; p = 0.003) and brainstem (9.98 cm3 [9.45-11.00 cm3] vs. 11.05 cm3 [10.10-11.53 cm3]; p = 0.015). The total myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (0.45 cm3 [0.39-0.48 cm3] vs. 0.48 cm3 [0.45-0.54 cm3]; p = 0.004) and brainstem (1.45 cm3 [1.28-1.59 cm3] vs. 1.54 cm3 [1.42-1.67 cm3]; p = 0.042). No significant correlation was observed between myelin volume parameters and neuropsychological test scores, except for the total myelin volume at the bilateral cerebral WM and verbal learning test (delayed recall) (r = 0.425; p = 0.048). CONCLUSION: MVF quantified from the MDME sequence was decreased at the bilateral cerebral WM in mTBI patients with PCS. The total myelin volumes at the corpus callosum and brainstem were decreased in mTBI patients with PCS due to atrophic changes.

Prediction of hemorrhagic complications after ultrasound-guided biopsy of the thyroid and neck.

OBJECTIVES: Hemorrhage occasionally occurs after ultrasound (US)-guided biopsy of the thyroid and neck and sometimes leads to serious complications. We aimed to identify predictors of hemorrhagic complications after US-guided biopsy of the thyroid and neck. MATERIAL AND METHODS: In this retrospective study, we analyzed consecutive patients who underwent US-guided biopsy from April 2020 to November 2020. Procedure characteristics, US features, and peri- and post-procedural patient symptoms and signs were compared between patients with and without post-biopsy hemorrhage. Associations between clinical and imaging variables and post-biopsy hemorrhage were analyzed using univariate and multivariate regression analyses. RESULTS: A total of 305 patients who underwent US-guided biopsy of the thyroid and neck were included (219 women, 86 men; age range, 20-89 years). Seventeen (5.7%) cases of post-biopsy hemorrhage were detected 30 min after biopsy and manual compression. Among them, 10 developed hemorrhage at 30 min without immediate hemorrhage. In the multivariate analysis, a high tenderness score on the visual analog scale (VAS) at 30 min after biopsy (odds ratio [OR] 5.05, p < .001) was identified as an independent predictor of post-biopsy hemorrhage. In patients with hemorrhage at 30 min, tenderness scores significantly increased over 30 min of observation. CONCLUSIONS: High tenderness scores at 30 min after biopsy and manual compression were independent predictors of hemorrhage after US-guided biopsy of the thyroid and neck. The tenderness score could serve as a valuable marker to triage patients who require further observation and management after a US-guided biopsy of the thyroid and neck. KEY POINTS: • High tenderness scores at 30 min after compression were associated with the presence of delayed post-biopsy hemorrhage at 30 min. • Patients with hemorrhage at 30 min demonstrated a significant increase in tenderness scores over time. • High tenderness scores after biopsy site compression predicted the presence of delayed post-biopsy hemorrhage in the thyroid and neck.

Response prediction of vestibular schwannoma after gamma-knife radiosurgery using pretreatment dynamic contrast-enhanced MRI: a prospective study.

OBJECTIVES: There are few known predictive factors for response to gamma-knife radiosurgery (GKRS) in vestibular schwannoma (VS). We investigated the predictive role of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters regarding the tumor response after GKRS in sporadic VS. METHODS: This single-center prospective study enrolled participants between April 2017 and February 2019. We performed a volumetric measurement of DCE-MRI-derived parameters before GKRS. The tumor volume was measured in a follow-up MRI. The pharmacokinetic parameters were compared between responders and nonresponders according to 20% or more tumor volume reduction. Stepwise multivariable logistic regression analyses were performed, and the diagnostic performance of DCE-MRI parameters for the prediction of tumor response was evaluated by receiver operating characteristic curve analysis. RESULTS: Ultimately, 35 participants (21 women, 52 ± 12 years) were included. There were 22 (62.9%) responders with a mean follow-up interval of 30.2 ± 5.7 months. Ktrans (0.036 min-1 vs. 0.057 min-1, p = .008) and initial area under the time-concentration curve within 90 s (IAUC90) (84.4 vs. 143.6, p = .003) showed significant differences between responders and nonresponders. Ktrans (OR = 0.96, p = .021) and IAUC90 (OR = 0.97, p = .004) were significant differentiating variables in each multivariable model with clinical variables for tumor response prediction. Ktrans showed a sensitivity of 81.8% and a specificity of 69.2%, and IAUC90 showed a sensitivity of 100% and a specificity of 53.8% for tumor response prediction. CONCLUSION: DCE-MRI (particularly Ktrans and IAUC90) has the potential to be a predictive factor for tumor response in VS after GKRS. KEY POINTS: •Pretreatment prediction of gamma-knife radiosurgery response in vestibular schwannoma is still challenging. •Dynamic contrast-enhanced MRI could have predictive value for the response of vestibular schwannoma after gamma-knife radiosurgery.