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.

Evaluation of Key Molecular Markers in Adult Diffuse Gliomas Based on a Novel Combination of Diffusion and Perfusion MRI and MR Spectroscopy.

BACKGROUND: Preoperative identification of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status could help clinicians select the optimal therapy in patients with diffuse glioma. Although, the value of multimodal intersection was underutilized. PURPOSE: To evaluate the value of quantitative MRI biomarkers for the identification of IDH mutation and 1p/19q codeletion in adult patients with diffuse glioma. STUDY TYPE: Retrospective. POPULATION: Two hundred sixteen adult diffuse gliomas with known genetic test results, divided into training (N = 130), test (N = 43), and validation (N = 43) groups. SEQUENCE/FIELD STRENGTH: Diffusion/perfusion-weighted-imaging sequences and multivoxel MR spectroscopy (MRS), all 3.0 T using three different scanners. ASSESSMENT: The apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) of the core tumor were calculated to identify IDH-mutant and 1p/19q-codeleted statuses and to determine cut-off values. ADC models were built based on the 30th percentile and lower, CBV models were built based on the 75th centile and higher (both in five centile steps). The optimal tumor region was defined and the metabolite concentrations of MRS voxels that overlapped with the ADC/CBV optimal region were calculated and added to the best-performing diagnostic models. STATISTICAL TESTS: DeLong's test, diagnostic test, and decision curve analysis were performed. A P value <0.05 was considered to be statistically significant. RESULTS: Almost all ADC models achieved good performance in identifying IDH mutation status, among which ADC_15th was the most valuable parameter (threshold = 1.186; Youden index = 0.734; AUC_train = 0.896). The differential power of CBV histogram metrics for predicting 1p/19q codeletion outperformed ADC histogram metrics, and the CBV_80th-related model performed best (threshold = 1.435; Youden index = 0.458; AUC_train = 0.724). The AUCs of ADC_15th and CBV_80th models in the validation set were 0.857 and 0.733. These models tended to improve after incorporation of N-acetylaspartate/total_creatine and glutamate-plus-glutamine/total_creatine, respectively. DATA CONCLUSION: The intersection of ADC-, CBV-based histogram and MRS provide a reliable paradigm for identifying the key molecular markers in adult diffuse gliomas. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

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.

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.

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.

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.

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.

Quantitative MR spectroscopy reveals metabolic changes in the dorsolateral prefrontal cortex of patients with temporal lobe epilepsy.

OBJECTIVE: To characterize possible metabolic changes of the dorsolateral prefrontal cortex (DLPFC) in patients with temporal lobe epilepsy (TLE). METHODS: Quantitative proton magnetic resonance spectroscopy (1H-MRS) studies were performed on 24 TLE patients and 22 healthy controls. Metabolite concentrations were calculated using a linear combination model (LCModel) and corrected for cerebrospinal fluid contamination. Comparisons were performed between the TLE patients and the controls and between the left DLPFC and right DLPFC in each group. Pearson correlation coefficients were calculated between the metabolite concentrations and epilepsy duration and between the metabolite concentrations and voxel tissue composition: [gray matter (GM)/(GM+white matter (WM))]. RESULTS: Metabolic asymmetry was found in controls between the left and right DLPFC, i.e., the NAA concentration of the left DLPFC was significantly higher than that of the right. However, such metabolic asymmetry was not observed in TLE patients. Compared with the controls, TLE patients showed significantly decreased NAA and Ins, and the reductions were greater in the left DLPFC. No significant correlation was found between the metabolite concentrations and epilepsy duration or between the metabolite concentrations and voxel tissue composition [GM/(GM+WM)]. CONCLUSIONS: This study suggests that TLE can produce metabolic changes to DLPFC that is remote from the seizure focus. KEY POINTS: • Magnetic resonance spectroscopy probes the brain metabolism noninvasively. • Dorsolateral prefrontal reductions in NAA (a neuronal marker) and Ins are observed in TLE. • Temporal lobe epilepsy can result in metabolic changes remote from the seizure focus.

Expression of activated signal transducer and activator of transcription-3 as a predictive and prognostic marker in advanced esophageal squamous cell carcinoma.

BACKGROUND: Signal transducer and activator of transcription-3 (STAT3) is an oncogenic transcription factor constitutively active and aberrantly expressed in various types of malignancies, and the expression of p-STAT3 has been recognized as a predictor of poor survival. It remains unclear how variations in p-STAT3 expression influence clinical outcomes in esophageal squamous cell carcinoma (ESCC). METHODS: Between 1 January 2008 and 1 November 2013, 153 advanced esophageal squamous cell carcinoma patients (stage IV) from two cancer centers in West China were treated with paclitaxel and cisplatin. We retrospectively analyzed the clinical outcomes of patients with ESCC and examined the correlation between p-STAT3 levels and clinical outcomes in esophageal cancer patients. RESULTS: Among the 153 patients, positive p-STAT3 expression was observed in 73 of 153 (47.7 %) cases. The median PFS for patients with positive expression of p-STAT3 and negative expression of p-STAT3 was 5.0 months and 6.9 months, respectively (P < 0.001). The median overall survival was significantly higher in patients with p-STAT3 negative tumors than in those with p-STAT3 positive tumors (9.9 vs 8.9 months, P = 0.026). Kaplan-Meier survival analysis showed that p-STAT3 expression was statistically indicative of a poor prognosis for progression-free survival. CONCLUSIONS: These data showed that p-STAT3 expression was significantly associated with poor prognosis in patients with esophageal cancer and could be used as a predictive and prognostic marker in esophageal cancer.

Ultrasound-guided puncture drainage versus surgical incision drainage for deep neck space abscesses: a protocol for a systematic review with meta-analysis and trial sequential analysis.

INTRODUCTION: Deep neck space abscesses (DNAs) are serious surgical emergencies, associated with life-threatening complications. Surgical incision and drainage combined with antibiotics is the main treatment for DNAs, but drawbacks still exist. Ultrasound-guided puncture drainage is an alternative treatment for some DNAs with limited clinical evidence. Hence, the optimal drainage technique for the treatment of DNAs remains unclear. Therefore, we will perform a protocol for a systematic review and meta-analysis to identify the efficacy of ultrasound-guided puncture drainage for DNAs. METHODS AND ANALYSIS: PubMed, Ovid Medline, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang database, VIP database and trial registry databases will be searched from inception to September 2023 to identify randomised controlled trials of patients diagnosed with DNAs accepting ultrasound-guided puncture drainage. The primary outcome will be the length of hospital stay. The secondary outcomes will be the cure rate, incidence of retreatment, complications and overall cost to the healthcare system. Fixed-effects or random-effects model will be used according to the statistical heterogeneity. Mean differences or standardised mean differences with 95% CIs for continuous data and risk ratio (RR) with 95% CIs for dichotomous data. The Cochrane risk-of-bias tool 2, Grading of Recommendations Assessment, Development and Evaluation (GRADE) and trial sequential analysis will be conducted to evaluate the evidence quality and control the random errors. Funnel plots and Egger's regression test will be performed to evaluate publication bias. ETHICS AND DISSEMINATION: Ethical approval was not required for this systematic review protocol. The results will be disseminated through peer-reviewed publications. PROSPERO REGISTRATION NUMBER: CRD42023441031.

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.

MRI characteristics of H3 G34-mutant diffuse hemispheric gliomas and possible differentiation from IDH-wild-type glioblastomas in adolescents and young adults.

OBJECTIVE: H3 G34-mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase-wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them. METHODS: Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study. The authors reviewed the clinical, radiological, and molecular data of G34m-DHGs and compared their neuroimaging features with those of IDH-WT-GBMs in adolescents and young adults. Visually Accessible Rembrandt Images (VASARI) features were extracted, and the Kruskal-Wallis test was performed. A logistic regression model was constructed to evaluate the diagnostic performance for differentiating between G34m-DHG and IDH-WT-GBM. Subsequently, FeAture Explorer (FAE) was used to generate the machine learning pipeline and select important radiomics features that had been extracted with PyRadiomics. Estimates of the performance were supplied by metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC). RESULTS: The mean age of the 23 patients with G34m-DHG was 23.7 years (range 11-45 years), younger than the mean age of patients with IDH-WT-GBM (30.96 years, range 5-43 years). All tumors were hemispheric. Most cases were immunonegative for ATRX (95%) and Olig2 (100%), were immunopositive for p53 (95%), and exhibited MGMT promoter methylation (81%). The radiological presentations of G34m-DHG were different from those of IDH-WT-GBM. The majority of the G34m-DHGs were in the frontal, parietal, and temporal lobes and demonstrated no or only faint contrast enhancement (74%), while IDH-WT-GBMs were mostly seen in the frontal lobe and showed marked contrast enhancement in 83% of cases. The FAE-generated model, based on radiomics features (AUC 0.925) of conventional MR images, had better discriminatory performance between G34m-DHG and IDH-WT-GBM than VASARI feature analysis (AUC 0.843). CONCLUSIONS: G34m-DHGs most frequently occur in the frontal, parietal, and temporal lobes in adolescent and young adults and are associated with radiological characteristics distinct from those of IDH-WT-GBMs. Successful identification can be achieved by using either VASARI features or radiomics signatures, which may contribute to prognostic evaluation and assist in clinical settings.

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.

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.

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.

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.

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.

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.

Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study.

OBJECTIVES: To investigate the value of morphological feature and signal intensity ratio (SIR) derived from conventional magnetic resonance imaging (MRI) in distinguishing primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (aGBM). METHODS: Pathology-confirmed PCNSLs (n = 93) or aGBMs (n = 48) from three institutions were retrospectively enrolled and divided into training cohort (n = 98) and test cohort (n = 43). Morphological features and SIRs were compared between PCNSL and aGBM. Using linear discriminant analysis, multiple models were constructed with SIRs and morphological features alone or jointly, and the diagnostic performances were evaluated via receiver operating characteristic (ROC) analysis. Areas under the curves (AUCs) and accuracies (ACCs) of the models were compared with the radiologists' assessment. RESULTS: Incision sign, T2 pseudonecrosis sign, reef sign and peritumoral leukomalacia sign were associated with PCNSL (training and overall cohorts, P < 0.05). Increased T1 ratio, decreased T2 ratio and T2/T1 ratio were predictive of PCNSL (all P < 0.05). ROC analysis showed that combination of morphological features and SIRs achieved the best diagnostic performance for differentiation of PCNSL and aGBM with AUC/ACC of 0.899/0.929 for the training cohort, AUC/ACC of 0.794/0.837 for the test cohort and AUC/ACC of 0.869/0.901 for the overall cohort, respectively. Based on the overall cohort, two radiologists could distinguish PCNSL from aGBM with AUC/ACC of 0.732/0.724 for radiologist A and AUC/ACC of 0.811/0.829 for radiologist B. CONCLUSION: MRI morphological features can help differentiate PCNSL from aGBM. When combined with SIRs, the diagnostic performance was better than that of radiologists' assessment.

Case Report: Extra-Articular Diffuse Tenosynovial Giant Cell Tumor of the Temporomandibular Joint.

Diffuse tenosynovial giant cell tumor (D-TSGCT) is a benign but locally destructive tumor of synovium that may involve joints, tendon sheaths, and bursae. Its occurrence in the temporomandibular joint (TMJ) is extremely rare. The authors reported a case of 48-year-old man with an extra-articular D-TSGCT in the TMJ with medial cranial fossa extension. computed tomography (CT) and magnetic resonance imaging (MRI) features are described. The lesion was a cystic-solid mass centered at the temporal bone without involvement of the condylar head, and its solid component presented high-density on CT and hypointensity on MRI. No signs of recurrence or metastasis was observed during 12-months of follow-up. The present report suggested the potential characteristics of radiologic imaging of D-TSGCT in TMJ.