Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) often exhibit overlapping appearances on routine MRI, complicating pre-treatment diagnosis. In 1,109 patients from five centers, we constructed standard-space tumor probabilistic maps and derived atlas-anchored spatial features to augment conventional radiomics. The spatial radiomics classifier outperformed radiomics alone (external test area under the ROC curve [AUC], 0.98) with acceptable calibration and decision curve benefit, and SHapley Additive exPlanations (SHAP)-enabled anatomy-grounded interpretation. Aligning tumor localization with the Allen Human Brain Atlas and a normative functional connectome linked GBM-enriched territories to developmental-oncogenic programs and network hubness, whereas PCNSL-enriched territories showed immune-inflammatory/proliferative programs, and associations with network hubness did not survive spatial-autocorrelation correction. These results provide shareable reference maps and an interpretable, multicenter-generalizing tool for GBM-PCNSL differentiation, while offering biological context for diagnosis-specific location susceptibility.

El tumor glioneuronal mixoide representa una entidad recientemente reconocida en la clasificación de 2021 de la Organización Mundial de la Salud de tumores del sistema nervioso central. Como una de las 14 neoplasias neuronales y glioneuronales definidas, se caracteriza molecularmente por la mutación PDGFRA p.K385. En este artículo, ilustramos las características de imagen de tomografía computarizada, resonancia magnética convencional y funcional, y tomografía por emisión de positrones/computarizada en 2 casos de tumores glioneuronales mixoides, presentando las características imagenológicas novedosas.

Myxoid glioneuronal tumor represents a newly recognized entity in the 2021 World Health Organization classification of central nervous system tumors. As one of the 14 defined neuronal and glioneuronal tumors, it is molecularly characterized by PDGFRA p.K385 mutation. In this paper, we illustrate computed tomography, conventional and functional magnetic resonance imaging, and positron emission tomography-computed tomography imaging features of 2 myxoid glioneuronal tumor cases, introducing novel imaging characteristics.

H3K27-altered diffuse midline gliomas (H3K27-altered DMGs) are classified as WHO grade 4 tumors despite their histopathological characteristics. However, histopathological grades are known to have an important effect on prognosis. This study aims to investigate the prognostic impact of histopathological grades on H3K27-altered DMGs and to predict the grades using multiparametric MRI. We conducted a retrospective study on H3K27-altered DMG patients diagnosed between December 2016 and February 2023. Tumors were classified as histologically lower- or high-grade DMGs by experienced pathologists. The influence of histopathological grades on prognosis was analyzed by Kaplan-Meier curve and Cox regression. Propensity score matching minimized baseline bias. Clinical and multimodal MRI variables were analyzed through age-stratified subgroup analysis. Logistic regression model was constructed for grade prediction and evaluated using the C-index and F1 score. 90 (53 lower-grade, 37 high-grade) had survival data and 44 (26 lower-grade, 18 high-grade) had complete multiparametric MRI data. Lower-grade patients exhibited significantly longer median overall survival than high-grade (10.5 months vs. 4 months, p < 0.05). Histopathological grade was confirmed as an independent prognostic factor. Qualitative MR imaging features revealed that high-grade DMGs in juveniles showed significantly larger enhancement quality and edema proportion (p < 0.05). Quantitative perfusion parameters like relative cerebral blood flow (rCBF) and relative cerebral blood volume (rCBV) differed significantly between high- and lower-grade groups in both juveniles and adults (p < 0.05). rCBF_99th, enhancement quality, and edema proportion, selected from variables with intergroup differences, formed a nomogram model with a C-index of 0.8596, AUC of 0.885 (0.833 validation), and F1 score of 0.8648. Histological grades could not be ignored when assessing the prognosis of H3K27-altered DMGs. Multimodal MR imaging signatures may play an important role in the prediction of histological grades and thus contribute to a layered risk management of DMGs.

BACKGROUND:Different from typical primary central nervous system lymphoma (PCNSL), early-stage atypical PCNSL usually presents as patchy signal abnormalities without evident mass effect or significant contrast enhancement and is prone to confusion with low-grade glioma (LGG). This study aims to develop a magnetic resonance imaging (MRI)-based radiomics model to differentiate early-stage atypical PCNSL from LGG. METHODS:Two cohorts consisting of early-stage atypical PCNSL patients, as well as LGG patients with similar radiological manifestations, were retrospectively recruited from West China Hospital of Sichuan University (PCNSL = 75; LGG = 138) and Chengdu Shangjin Nanfu Hospital (PCNSL = 35; LGG = 72) to serve as the training set and external validation set, respectively. Within the training set, there were additional early-stage atypical lesions from 19 typical or advanced-stage PCNSL patients included as a supplement. MRI-based radiomics models were developed and validated based on these 2 cohorts. RESULTS:Nine radiomic features were selected as significant features, most of which are wavelet radiomic features. The best radiomics model achieved an area under the curve of 0.929 (0.901-0.957) and an accuracy of 91.6% on the independent external validation set. The inclusion of 19 additional PCNSL patients improved the model's performance. CONCLUSIONS:The MRI-based radiomics model can accurately differentiate early-stage atypical PCNSL from LGG with similar radiological manifestations, allowing early-stage atypical PCNSL patients to receive timely and appropriate radiotherapy or chemotherapy while avoiding unnecessary surgical resection.

Myxoid glioneuronal tumor represents a newly recognized entity in the 2021 World Health Organization classification of central nervous system tumors. As one of the 14 defined neuronal and glioneuronal tumors, it is molecularly characterized by PDGFRA p.K385 mutation. In this paper, we illustrate computed tomography, conventional and functional magnetic resonance imaging, and positron emission tomography-computed tomography imaging features of two myxoid glioneuronal tumor cases, introducing novel imaging characteristics.

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.

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.

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 (VOI tumor ), VOI peritumor , and VOI intra-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 VOI intra-plus peritumor achieved higher AUCs compared to models based on features from VOI tumor . 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.

Abstract An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1‐weighted images and T2 fluid‐attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression‐free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1‐, 2‐, and 3‐year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.

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.

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.

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

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.

Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M mutant DMG.

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.

- OBJECTIVE: To explore the predictive value of quantitative features extracted from conventional magnetic resowild-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 tency 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 status in ependymoma.-RESULTS: There was a good interevaluator agreement on the imaging features (kappa value range 0.601e1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive per-formance 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.

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.