Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study.

RATIONALE AND OBJECTIVES: The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs). MATERIALS AND METHODS: We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed. RESULTS: A total of 475 patients (DGs: n = 338, CAGs: n = 137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, seven features were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959. CONCLUSION: Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.

Cortical myelin and thickness mapping provide insights into whole-brain tumor burden in diffuse midline glioma.

Systemic infiltration is a hallmark of diffuse midline glioma pathogenesis, which can trigger distant disturbances in cortical structure. However, the existence and effects of these changes have been underexamined. This study aimed to investigate whole-brain cortical myelin and thickness alternations induced by diffuse midline glioma. High-resolution T1- and T2-weighted images were acquired from 90 patients with diffuse midline glioma with H3 K27-altered and 64 patients with wild-type and 86 healthy controls. Cortical thickness and myelin content was calculated using Human Connectome Project pipeline. Significant differences in cortical thickness and myelin content were detected among groups. Short-term survival prediction model was constructed using automated machine learning. Compared with healthy controls, diffuse midline glioma with H3 K27-altered patients showed significantly reduced cortical myelin in bilateral precentral gyrus, postcentral gyrus, insular, parahippocampal gyrus, fusiform gyrus, and cingulate gyrus, whereas diffuse midline glioma with H3 K27 wild-type patients exhibited well-preserved myelin content. Furtherly, when comparing diffuse midline glioma with H3 K27-altered and diffuse midline glioma with H3 K27 wild-type, the decreased cortical thickness in parietal and occipital regions along with demyelination in medial orbitofrontal cortex was observed in diffuse midline glioma with H3 K27-altered. Notably, a combination of cortical features and tumor radiomics allowed short-term survival prediction with accuracy 0.80 and AUC 0.84. These findings may aid clinicians in tailoring therapeutic approaches based on cortical characteristics, potentially enhancing the efficacy of current and future treatment modalities.

Metabolic heterogeneity in different subtypes of malformations of cortical development causing epilepsy: a proton magnetic resonance spectroscopy study.

Background: The most common subtypes of malformations of cortical development (MCDs) are gray matter heterotopia (GMH), focal cortical dysplasia (FCD), and polymicrogyria (PMG). This study aimed to characterize the possible neurometabolic abnormalities and heterogeneity in different MCDs subtypes using proton magnetic resonance spectroscopy (1H-MRS). Methods: In this prospective cross-sectional study, we recruited 29 patients with MCDs and epilepsy, including ten with GMH, ten with FCD, and nine with PMG, as well as 25 age- and sex-matched healthy controls (HC) from the Epilepsy Center of West China Hospital of Sichuan University between August 2018 and November 2021. Inclusion criteria for the patients were based upon typical magnetic resonance imaging (MRI) findings of MCDs and full clinical assessment for epilepsy. Single-voxel point-resolved spectroscopy was used to acquire data from both the lesion and the normal-appearing contralateral side (NACS) in patients and from the frontal lobe in HC. Metabolite measures, including N-acetyl aspartate (NAA), myoinositol (Ins), choline (Cho), creatine (Cr), and glutamate + glutamine (Glx) concentrations, were quantitatively estimated with linear combination model (LCModel) software and corrected for the partial volume effect of cerebrospinal fluid (CSF). Results: The NAA concentration was lower and the Ins concentration was higher in the MCDs lesions than in the NACS and in HC (P=0.002-0.007), and the Cho and Cr concentrations were higher in MCDs lesions than in HC (P=0.001-0.016). Moreover, the Cho concentration was higher in NACS than in HC (P=0.015). In the GMH lesions, the only metabolic alteration was an NAA reduction (GMH_lesion vs. HC: P=0.001). In the FCD lesions, there were more metabolite abnormalities than in the other two subtypes, particularly a lower NAA and a higher Ins than in HC and NACS (P=0.012-0.042). In the PMG lesions, Cr (lesion vs. HC or NACS: P=0.017-0.021) and Glx (lesion vs. NACS: P=0.043) were increased, while NAA was normal. Correlation analysis revealed that the Cr concentration in MCDs lesions was positively correlated with seizure frequency (r=0.411; P=0.027). Conclusions: Based upon 1H-MRS, our study demonstrated that different MCDs subtypes exhibited variable metabolic features, which may be associated with distinct functional and cytoarchitectural properties.

Multiparametric mapping of white matter reorganizations in patients with frontal glioma-related epilepsy.

AIMS: Epilepsy is a common symptom in diffuse lower-grade glioma (DLGG). The specific role of white matter (WM) alteration in patients with glioma-related epilepsy (GRE) is largely unknown. This study aims to investigate the reorganization of WM tracts and changes in structural networks related to GRE. METHODS: Diffusion-weighted images were collected from 70 patients with left frontal DLGG (GRE = 33, non-GRE = 37) and 41 healthy controls (HC). Tractometry with TractSeg was applied to segment tracts and quantify fractional anisotropy (FA) along each tract. Structural network was constructed using constrained spherical deconvolution and probabilistic tractography. FA and network properties were compared among three groups. RESULTS: Compared with HC, both GRE and non-GRE showed decreased FA in contralateral inferior fronto-occipital fasciculus, superior longitudinal fasciculus II and arcuate fasciculus, increased nodal efficiency in contralateral nodes of frontal-parietal and limbic networks, whereas decreased degree centrality and betweenness centrality in nodes of dorsal temporal lobe and rostral middle frontal gyrus (rMFG). Additionally, when compared GRE with non-GRE, increased FA in contralateral corticospinal tract (CST) and lower betweenness centrality in paracentral lobule (PCL) in GRE (all p < 0.05 after Bonferroni correction). CONCLUSION: This study indicates that patients with left frontal DLGG exhibit complex WM reorganization, and the altered regions mainly concentrated in the language, frontal-parietal and limbic networks. Moreover, the preserved integrity in contralateral CST and server decreased nodal betweenness in PCL may be potential neuroimaging markers underlying the occurrence of presurgical seizures of GRE.

Progressive structural damage in sleep-related hypermotor epilepsy.

This study aimed to explore the alterations in gray matter volume (GMV) based on high-resolution structural data and the temporal precedence of structural alterations in patients with sleep-related hypermotor epilepsy (SHE). After preprocessing of T1 structural images, the voxel-based morphometry and source-based morphometry (SBM) methods were applied in 60 SHE patients and 56 healthy controls to analyze the gray matter volumetric alterations. Furthermore, a causal network of structural covariance (CaSCN) was constructed using Granger causality analysis based on structural data of illness duration ordering to assess the causal impact of structural changes in abnormal gray matter regions. The GMVs of SHE patients were widely reduced, mainly in the bilateral cerebellums, fusiform gyri, the right angular gyrus, the right postcentral gyrus, and the left parahippocampal gyrus. In addition to those regions, the results of the SBM analysis also found decreased GMV in the bilateral frontal lobes, precuneus, and supramarginal gyri. The analysis of CaSCN showed that along with disease progression, the cerebellum was the prominent node that tended to affect other brain regions in SHE patients, while the frontal lobe was the transition node and the supramarginal gyrus was the prominent node that may be easily affected by other brain regions. Our study found widely affected regions of decreased GMVs in SHE patients; these regions underlie the morphological basis of epileptic networks, and there is a temporal precedence relationship between them.

Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart.

OBJECTIVE: To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas. METHODS: Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma. RESULTS: There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618). CONCLUSIONS: Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.

CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study.

OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. METHODS: Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOItumor), VOIperitumor, and VOIintra-plus peritumor. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The results showed that the radiomics models based on features from VOIintra-plus peritumor achieved higher AUCs compared to models based on features from VOItumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.

An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma.

BACKGROUND: The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear. PURPOSE: To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG). STUDY TYPE: Retrospective. SUBJECTS: Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53). FIELD STRENGTH/SEQUENCE: 3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence. ASSESSMENT: In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software. STATISTICAL TESTS: General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant. RESULTS: For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively). DATA CONCLUSION: Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Abnormal brain functional network dynamics in sleep-related hypermotor epilepsy.

AIMS: This study aimed to use resting-state functional magnetic resonance imaging (rs-fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep-related hypermotor epilepsy (SHE). METHODS: High-resolution T1 and rs-fMRI scanning were performed on all the subjects. We used a sliding-window approach to construct a dynamic functional connectivity (dFC) network. The k-means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network-based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed. RESULTS: After k-means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE. CONCLUSION: The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.

Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer.

The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients.

Two Patterns of White Matter Connection in Multiple Gliomas: Evidence from Probabilistic Fiber Tracking.

Background: Multiple lesions are uncommon in brain gliomas, and their pathophysiology is poorly understood. Invasive growth along white matter tracts is an important clinicopathological characteristic of gliomas, and a major factor in a poor therapeutic outcome. Here, we used probabilistic fiber tracking and cluster analysis to investigate the inter-focal connectivity relationships of multiple gliomas, in order to seek inferential evidence of common origin. Methods: MRI scans of 46 patients with multiple gliomas were retrospectively analyzed. Before surgery, all patients underwent multimodal functional MR imaging, including diffusion tensor imaging, enhanced 3D T1-weighted imaging, diffusion-weighted imaging, 1H MR spectroscopy, and dynamic susceptibility contrast perfusion-weighted imaging. Probabilistic fiber tracking was used to quantify white matter connectivity between neoplastic foci. Hierarchical cluster analysis was performed to identify patterns of white matter connection. Results: Cluster analysis reveals two patterns of connectivity, one with smaller, and one with greater, connectivity (2675 ± 1098 versus 30432 ± 22707, p < 0.0001). The two subgroups show significant differences in relative cerebral blood volume (2.31 ± 0.95 versus 1.73 ± 0.48, p = 0.002) and lipid/creatine ratio (0.32 ± 0.22 versus 0.060 ± 0.051, p = 0.006). Conclusion: Two distinct patterns of white matter connection exist in multiple gliomas. Those with lower connectivity tend to have independent origins, and can be termed true multicentric glioma, whereas those with greater connectivity tend to share common origin, and spread along white matter tracts. True multicentric gliomas have higher vascularity and more intratumoral necrosis. These findings may help to develop personalized therapeutic strategies for multiple gliomas.

1H-MRS reveals metabolic alterations in generalized tonic-clonic seizures before and after treatment.

AIMS: To explore the possible metabolic alterations of bilateral dorsolateral prefrontal cortices (DLPFC) of generalized tonic-clonic seizures (GTCS) patients before and after antiepileptic drugs treatment as compared with healthy controls (HCs) using proton magnetic resonance spectroscopy (1H-MRS). METHODS: We included 23 newly diagnosed and unmedicated GTCS patients and 23 sex- and age-matched HCs. Metabolites including N-acetyl aspartate (NAA), myo-inositol (Ins), choline (Cho), creatine (Cr), and glutamate + glutamine (Glu + Gln, Glx) concentrations were quantified by using LCModel software and then corrected for the partial volume effect of cerebrospinal fluid. RESULTS: The results demonstrated that metabolite concentrations were not equal between the left and the right DLPFC. Compared with HC, NAA of the left DLPFC and Cr of the right DLPFC were significantly lower in pre-treatment patients. Self-controlled study revealed that the patients' NAA of the left DLPFC increased while their Cr of the right DLPFC decreased after treatment. Correlation analysis showed a negative correlation between the duration of medication and the pre- and post-treatment difference of Cr. CONCLUSION: These findings may shed a light on the metabolic mechanism of GTCS and the neurobiochemical mechanisms of AEDs.

Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation.

BACKGROUND: Conventional MR imaging has limited value in identifying H3 K27M mutations. We aimed to investigate the capacity of quantitative MR imaging variables in identifying the H3 K27M mutation status of diffuse midline glioma. MATERIALS AND METHODS: Twenty-three patients with H3 K27M mutation and thirty-two wild-type patients were recruited in this retrospective study, all of whom underwent multimodal MR imaging. Clinical data and quantitative MR imaging variables were explored by subgroup analysis stratified by age (juveniles and adults). Then, a logistic model for all patients was constructed to identify potential variables for predicting K27M mutation status. Besides, a retrospective validation set including 13 patients was recruited. The C-index and F1 score were used to evaluate the performance of the prediction model. RESULTS: It turned out that patients with H3 K27M mutation were younger in the adult subgroup. In the mutation group, some relative apparent diffusion coefficient (rADC) histogram parameters and myo-inositol/creatine plus phosphocreatine (Ins/tCr) ratio were lower than in the wild-type group of both juveniles and adults (p < 0.05). After nested cross-validation and LASSO algorithm, the age, Ins/tCr, and rADC_15th were selected as potential predictors for H3 K27M mutation in the model. The nomogram model showed good diagnostic power with a validated C-index of 0.884. In addition, the area under the curve (AUC) was 0.898 (0.976 in validation set) and the F1 score was 0.732. CONCLUSIONS: In conclusion, age, rADC_15th, and Ins/tCr values were helpful in identifying H3 K27M mutations in midline gliomas.

Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics -Based Nomogram.

PURPOSE: To build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation. METHOD: This study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models. RESULT: The radiomics model performed much better than the clinical model in both training (AUCs 0.925 vs. 0.786, accuracies 87.5% vs. 70.5%, sensitivities 83.6% vs. 41.7% and specificities 90.9% vs. 94.7%) and testing (AUCs 0.911 vs. 0.706, accuracies 84.0% vs. 71.3%, sensitivities 81.1% vs. 43.4% and specificities 86.4% vs. 95.0%). The combined model based on the radiomics signature and tumor stage, tumor infiltration depth and tumor pathology yielded the best performance (training cohort, AUC = 0.943, accuracies 89.5%, sensitivities 85.4% and specificities 92.9%; testing cohort, AUC = 0.923, accuracies 84.6%, sensitivities 84.0% and specificities 85.1%). CONCLUSION: Radiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.

Metabolic alterations of the dorsolateral prefrontal cortex in sleep-related hypermotor epilepsy: A proton magnetic resonance spectroscopy study.

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy whose neurobiological underpinnings remain poorly understood. The present study aimed to identify possible neurochemical alterations in the dorsolateral prefrontal cortex (DLPFC) in participants with SHE using proton magnetic resonance spectroscopy (1 H MRS). Thirty-nine participants with SHE (mean age, 30.7 years ± 11.3 [standard deviation], 24 men) and 59 controls (mean age, 29.4 years ± 10.4, 29 men) were consecutively and prospectively recruited and underwent brain magnetic resonance imaging and 1 H MRS in the bilateral DLPFCs. Brain concentrations of metabolites, including N-acetyl aspartate (NAA), myo-inositol (mI), choline, creatine, the sum of glutamate and glutamine, glutathione (GSH) and γ-aminobutyric acid, were estimated with LCModel and corrected for the partial volume effect of cerebrospinal fluid using tissue segmentation. ANCOVA analyses revealed lower concentration of NAA in the left DLPFC in participants with SHE compared with controls. A significant difference of NAA concentration between DLPFC in the two hemispheres (left > right) was observed only in the control group. We further confirmed a higher GSH concentration in men than in women in SHE participants, which probably indicates that men are more susceptible to this disease. The mI concentration in the right DLPFC was negatively correlated with epilepsy duration. This study demonstrates that DLPFC is an important brain region involved in the pathophysiology of SHE, in which both neurons and astrocytes appear impaired, and the elevated GSH level may suggest an abnormality related to oxidative stress.

Gray matter abnormalities in Tourette Syndrome: a meta-analysis of voxel-based morphometry studies.

Tourette syndrome (TS) is a neurobehavioral disorder for which the neurological mechanism has not been elucidated. Voxel-based morphometry (VBM) studies have revealed abnormalities in gray matter volume (GMV) in patients with TS; however, consistent results have not been obtained. The current study attempted to provide a voxel wise meta-analysis of gray matter changes using seed-based d mapping (SDM). We identified ten relevant studies that investigated gray matter alterations in TS patients and performed a meta-analysis using the SDM method to quantitatively estimate regional gray matter abnormalities. Next, we examined the relationships between GMV abnormalities and demographic and clinical characteristics. Our results demonstrated that TS patients had smaller GMV in the bilateral inferior frontal gyri and greater GMV in the cerebellum, right striatum (putamen), and bilateral thalami (pulvinar nucleus) than healthy controls. A meta-regression analysis did not identify correlations between GMV changes and demographic or clinical variables. This meta-analysis confirmed significant and consistent GMV changes in several brain regions of TS patients, primarily in the cortico-striato-thalamo-cortical network.

Quantitative 1H-MRS reveals metabolic difference between subcategories of malformations of cortical development.

PURPOSE: To figure out the spectra features of malformations of cortical development (MCDs) and the differences between MCDs subcategories. METHODS: Twenty patients and 18 controls were studied. The patients included two subcategories: disorders of migration (DOM) and postmigration (DOPM). Spectra of patients were acquired from both the lesion and the normal-appearing contralateral side (NACS), and they were compared to those of the controls obtained from the frontal lobe. RESULTS: Compared to the controls, a decreased NAA (P = 0.002) was identified in MCDs. After dividing the MCDs into the DOM and DOPM, we found that NAA reduction was only notable in the DOM (P = 0.007). Moreover, Ins and Cr of the DOPM were higher than those of the controls (P = 0.017 and 0.013) and the DOM (P = 0.027 and 0.001). Compared to the NACS, a decreased NAA (P = 0.042) and an increased Ins (P = 0.039) were identified in the lesion of MCDs. After dividing the MCDs into the DOM and DOPM, we found no significant differences in the DOM, but Ins, Cr, and Glx of the lesion were higher than those of the NACS (P = 0.007, 0.005 and 0.047) in the DOPM. In addition, we found that Cr and Glx correlated positively to the seizure frequency (P = 0.003 and 0.016). CONCLUSION: Decreased NAA was the prominent abnormality confirmed in MCDs. Spectra of different MCDs subcategories were different: the DOM was characterized by decreased NAA, while the DOPM was characterized by increased Ins.

Effective method to reduce the normal brain dose in single-isocenter hypofractionated stereotactic radiotherapy for multiple brain metastases.

BACKGROUND AND PURPOSE: Island blocking and dose leakage problems will lead to unnecessary irradiation to normal brain tissue (NBT) in hypofractionated stereotactic radiotherapy (HSRT) for multiple brain metastases (BM) with single-isocenter volumetric modulated arc therapy (VMAT). The present study aimed at investigating whether reducing the number of metastases irradiated by each arc beam could minimize these two problems. MATERIALS AND METHODS: A total of 32 non-small-cell lung cancer (NSCLC) patients with multiple BM received HSRT (24-36 Gy/3 fractions) with single-isocenter VMAT, where each arc beam only irradiated partial metastases (pm-VMAT), were enrolled in this retrospective study. Conventional single-isocenter VMAT plans, where each arc beam irradiated whole metastases (wm-VMAT), was regenerated and compared with pm-VMAT plans. Furthermore, the clinical efficacy and toxicities were evaluated. RESULTS: Pm-VMAT achieved similar target coverage as that with wm-VMAT, with better dose fall-off (P < 0.001) and NBT sparing (P < 0.001). However, pm-VMAT resulted in more monitor units (MU) and longer beam-on time (P < 0.001). The intracranial objective response rate and disease control rate for all patients were 75% and 100%, respectively. The local control rates at 1 year and 2 year were 96.2% and 60.2%, respectively. The median progression-free survival and overall survival were 10.3 months (95% confidence interval [CI] 6.8-13.2) and 18.5 months (95% CI 15.9-20.1), respectively. All treatment-related adverse events were grade 1 or 2, and 3 lesions (2.31%) from 2 patients (6.25%) demonstrated radiation necrosis after HSRT. CONCLUSION: HSRT with pm-VMAT is effective and has limited toxicities for NSCLC patients with multiple BM. Pm-VMAT could provide better NBT sparing while maintaining target dose coverage.

Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O6 -methylguanine-DNA methyltransferase promoter methylation in patients with gliomas.

Combining isocitrate dehydrogenase mutation (IDHmut) with O6 -methylguanine-DNA methyltransferase promoter methylation (MGMTmet) has been identified as a critical prognostic molecular marker for gliomas. The aim of this study was to determine the ability of glioma radiomics features from magnetic resonance imaging (MRI) to predict the co-occurrence of IDHmut and MGMTmet by applying the tree-based pipeline optimization tool (TPOT), an automated machine learning (autoML) approach. This was a retrospective study, in which 162 patients with gliomas were evaluated, including 58 patients with co-occurrence of IDHmut and MGMTmet and 104 patients with other status comprising: IDH wildtype and MGMT unmethylated (n = 67), IDH wildtype and MGMTmet (n = 36), and IDHmut and MGMT unmethylated (n = 1). Three-dimensional (3D) T1-weighted images, gadolinium-enhanced 3D T1-weighted images (Gd-3DT1WI), T2-weighted images, and fluid-attenuated inversion recovery (FLAIR) images acquired at 3.0 T were used. Radiomics features were extracted from FLAIR and Gd-3DT1WI images. The TPOT was employed to generate the best machine learning pipeline, which contains both feature selector and classifier, based on input feature sets. A 4-fold cross-validation was used to evaluate the performance of automatically generated models. For each iteration, the training set included 121 subjects, while the test set included 41 subjects. Student's t-test or a chi-square test was applied on different clinical characteristics between two groups. Sensitivity, specificity, accuracy, kappa score, and AUC were used to evaluate the performance of TPOT-generated models. Finally, we compared the above metrics of TPOT-generated models to identify the best-performing model. Patients' ages and grades between two groups were significantly different (p = 0.002 and p = 0.000, respectively). The 4-fold cross-validation showed that gradient boosting classifier trained on shape and textual features from the Laplacian-of-Gaussian-filtered Gd-3DT1 achieved the best performance (average sensitivity = 81.1%, average specificity = 94%, average accuracy = 89.4%, average kappa score = 0.76, average AUC = 0.951). Using autoML based on radiomics features from MRI, a high discriminatory accuracy was achieved for predicting co-occurrence of IDHmut and MGMTmet in gliomas. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.