VEGFR2 blockade inhibits glioblastoma cell proliferation by enhancing mitochondrial biogenesis.

BACKGROUND: Glioblastoma is an aggressive brain tumor linked to significant angiogenesis and poor prognosis. Anti-angiogenic therapies with vascular endothelial growth factor receptor 2 (VEGFR2) inhibition have been investigated as an alternative glioblastoma treatment. However, little is known about the effect of VEGFR2 blockade on glioblastoma cells per se. METHODS: VEGFR2 expression data in glioma patients were retrieved from the public database TCGA. VEGFR2 intervention was implemented by using its selective inhibitor Ki8751 or shRNA. Mitochondrial biogenesis of glioblastoma cells was assessed by immunofluorescence imaging, mass spectrometry, and western blot analysis. RESULTS: VEGFR2 expression was higher in glioma patients with higher malignancy (grade III and IV). VEGFR2 inhibition hampered glioblastoma cell proliferation and induced cell apoptosis. Mass spectrometry and immunofluorescence imaging showed that the anti-glioblastoma effects of VEGFR2 blockade involved mitochondrial biogenesis, as evidenced by the increases of mitochondrial protein expression, mitochondria mass, mitochondrial oxidative phosphorylation (OXPHOS), and reactive oxygen species (ROS) production, all of which play important roles in tumor cell apoptosis, growth inhibition, cell cycle arrest and cell senescence. Furthermore, VEGFR2 inhibition exaggerated mitochondrial biogenesis by decreased phosphorylation of AKT and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α), which mobilized PGC1α into the nucleus, increased mitochondrial transcription factor A (TFAM) expression, and subsequently enhanced mitochondrial biogenesis. CONCLUSIONS: VEGFR2 blockade inhibits glioblastoma progression via AKT-PGC1α-TFAM-mitochondria biogenesis signaling cascade, suggesting that VEGFR2 intervention might bring additive therapeutic values to anti-glioblastoma therapy.

Distinct virtual histology of grey matter atrophy in four neuroinflammatory diseases.

Gray matter (GM) atrophies were observed in multiple sclerosis, neuromyelitis optica spectrum disorders (both anti-aquaporin-4 antibody-positive [AQP4+], and -negative [AQP4-] subtypes NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Revealing the pathogenesis of brain atrophy in these disorders would help their differential diagnosis and guide therapeutic strategies. To determine the neurobiological underpinnings of GM atrophies in multiple sclerosis, AQP4+ NMOSD, AQP4- NMOSD, and MOGAD, we conducted a virtual histology analysis that links T1-weighted image derived GM atrophy and gene expression using a multicenter cohort of 324 patients with multiple sclerosis, 197 patients with AQP4+ NMOSD, 75 patients with AQP4- NMOSD, 47 patients with MOGAD, and 2,169 healthy controls (HCs). First, interregional GM atrophy profiles across the cortical and subcortical regions were determined by Cohen's d between patients with multiple sclerosis, AQP4+ NMOSD, AQP4- NMOSD, MOGAD and HCs. Then, the GM atrophy profiles were spatially correlated with the gene expressions extracted from the Allen Human Brain Atlas, respectively. Finally, we explored the virtual histology of clinical feature relevant GM atrophy by subgroup analysis that stratified by physical disability, disease duration, number of relapses, lesion burden, and cognitive function. Multiple sclerosis showed severe widespread GM atrophy pattern, mainly involving subcortical nuclei and brainstem. AQP4+ NMOSD showed obvious widespread GM atrophy pattern, predominately located in occipital cortex as well as cerebellum. AQP4- NMOSD showed mild widespread GM atrophy pattern, mainly located in frontal and parietal cortices. MOGAD showed GM atrophy mainly involving the frontal and temporal cortices. High expression of genes specific to microglia, astrocytes, oligodendrocytes, and endothelial cells in multiple sclerosis, S1 pyramidal cells in AQP4+ NMOSD, as well as S1 and CA1 pyramidal cells in MOGAD had spatial correlations with GM atrophy profiles were observed, while no atrophy profile related gene expression was found in AQP4- NMOSD. Virtual histology of clinical feature relevant GM atrophy mainly pointed to the shared neuronal and endothelial cells among the four neuroinflammatory diseases. The unique underlying virtual histology patterns were microglia, astrocytes, and oligodendrocytes for multiple sclerosis; astrocytes for AQP4+ NMOSD; and oligodendrocytes for MOGAD. Neuronal and endothelial cells were shared potential targets across these neuroinflammatory diseases. These findings might help their differential diagnosis and optimal therapeutic strategies.

Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis.

BACKGROUND: Spinal cord and brain atrophy are common in neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (RRMS) but harbor distinct patterns accounting for disability and cognitive impairment. METHODS: This study included 209 NMOSD and 304 RRMS patients and 436 healthy controls. Non-negative matrix factorization was used to parse differences in spinal cord and brain atrophy at subject level into distinct patterns based on structural MRI. The weights of patterns were obtained using a linear regression model and associated with Expanded Disability Status Scale (EDSS) and cognitive scores. Additionally, patients were divided into cognitive impairment (CI) and cognitive preservation (CP) groups. RESULTS: Three patterns were observed in NMOSD: (1) Spinal Cord-Deep Grey Matter (SC-DGM) pattern was associated with high EDSS scores and decline of visuospatial memory function; (2) Frontal-Temporal pattern was associated with decline of language learning function; and (3) Cerebellum-Brainstem pattern had no observed association. Patients with CI had higher weights of SC-DGM pattern than CP group. Three patterns were observed in RRMS: (1) DGM pattern was associated with high EDSS scores, decreased information processing speed, and decreased language learning and visuospatial memory functions; (2) Frontal-Temporal pattern was associated with overall cognitive decline; and (3) Occipital pattern had no observed association. Patients with CI trended to have higher weights of DGM and Frontal-Temporal patterns than CP group. CONCLUSION: This study estimated the heterogeneity of spinal cord and brain atrophy patterns in NMOSD and RRMS patients at individual level, and evaluated the clinical relevance of these patterns, which may contribute to stratifying participants for targeted therapy.

Central vein sign and trigeminal lesions of multiple sclerosis visualised by 7T MRI.

BACKGROUND: Although trigeminal nerve involvement is a characteristic of multiple sclerosis (MS), its prevalence across studies varies greatly due to MRI resolution and cohort selection bias. The mechanism behind the site specificity of trigeminal nerve injury is still unclear. We aim to determine the prevalence of trigeminal nerve involvement in patients with MS in a consecutive 7T brain MRI cohort. METHODS: This observational cohort originates from an ongoing China National Registry of Neuro-Inflammatory Diseases. Inclusion criteria were the following: age 18 years or older, diagnosis of MS according to the 2017 McDonald criteria and no clinical relapse within the preceding 3 months. Each participant underwent 7T MAGNETOM Terra scanner (Siemens, Erlangen, Germany), using a 32-channel phased array coil at Beijing Tiantan Hospital. T1-weighted magnetisation-prepared rapid acquisition gradient echoes, fluid-attenuated inversion recovery (FLAIR) and fluid and white matter suppression images were used to identify lesions. FLAIR* and T2* weighted images were used to identify central vein sign (CVS) within the trigeminal lesions. RESULTS: 120 patients underwent 7T MRI scans between December 2021 and May 2023. 19/120 (15.8%) patients had a total of 45 trigeminal lesions, of which 11/19 (57.9%) were bilateral. The linear lesions extended along the trigeminal nerve, from the root entry zone (REZ) (57.8%, 26/45) to the pontine-medullary nucleus (42.2%, 19/45). 26.9% (7/26) of the lesions in REZ showed a typical central venous sign. CONCLUSION: In this 7T MRI cohort, the prevalence of trigeminal nerve involvement was 15.8%. Characteristic CVS was detected in 26.9% of lesions in REZ. This suggests an inflammatory demyelination mechanism of trigeminal nerve involvement in MS.

Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators.

RATIONALE AND OBJECTIVES: To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model. MATERIALS AND METHODS: 145 and 50 RRMS patients with structural MRI and at least 1-year follow-up Expanded Disability Status Scale (EDSS) results were retrospectively enrolled and placed in the discovery and external test cohorts, respectively. Six clinical and radiomics feature-based machine learning classifiers were trained and tested to predict disability progression in the discovery cohort and validated in the external test set. Partial dependence plot (PDP) analysis and a Shiny web application were conducted to enhance the interpretability and intuitiveness. RESULTS: In the discovery cohort, 98 patients had disability stability, and 47 patients were classified as having disability progression. In the external test set, 35 patients were disability stable, and 15 patients had disability progression. Models trained with both clinical and radiomics features (area under the curve (AUC), 0.725-0.950) outperformed those trained with clinical (AUC, 0.600-0.740) or radiomics features only (AUC, 0.615-0.945). Among clinical+ radiomics feature models, the logistic regression (LR) classifier-based model performed best, with an AUC of 0.950. Only the radiomics feature-only models were applied in the external test set due to the data collection problem and showed fair performance, with AUCs ranging from 0.617 to 0.753. PDP analysis showed that female patients and those with lower volume, surface area, and symbol digit modalities test (SDMT) scores; greater mean curvature and age; and no disease modifying therapy (DMT) had increased probabilities of disease progression. Finally, a Shiny web application (https://lauralin1104.shinyapps.io/LRshiny/) was developed to calculate the risk of disability progression. CONCLUSION: Interpretable and intuitive machine learning approaches based on clinical and GM atrophy indicators can help physicians predict disability progression in RRMS patients for clinical decision-making and patient management.

Effects of the Fasting-Postprandial State on Arterial Spin Labeling MRI-Based Cerebral Perfusion Quantification in Alzheimer's Disease.

BACKGROUND: The fasting-postprandial state remains an underrecognized confounding factor for quantifying cerebral blood flow (CBF) in the cognitive assessment and differential diagnosis of Alzheimer's disease (AD). PURPOSE: To investigate the effects of fasting-postprandial state on arterial spin labeling (ASL)-based CBF in AD patients. STUDY TYPE: Prospective. SUBJECTS: Ninety-two subjects (mean age = 62.5 ± 6.4 years; females 29.3%), including 30 with AD, 32 with mild cognitive impairment (MCI), and 30 healthy controls (HCs). Differential diagnostic models were developed with a 4:1 training to testing set ratio. FIELD STRENGTH/SEQUENCE: 3-T, T1-weighted imaging using gradient echo and pseudocontinuous ASL imaging using turbo spin echo. ASSESSMENT: Two ASL scans were acquired to quantify fasting state and postprandial state regional CBFs based on an automated anatomical labeling atlas. Two-way ANOVA was used to assess the effects of fasting/postprandial state and disease state (AD, MCI, and HC) on regional CBF. Pearson's correlation analysis was conducted between regional CBF and cognitive scores (Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA]). The diagnostic performances of the fasting state, postprandial state, and mixed state (random mixing of the fasting and postprandial state CBF) in differential diagnosis of AD were conducted using support vector machine and logistic regression models. STATISTICAL TESTS: Two-way ANOVA, Pearson's correlation, and area under the curve (AUC) of diagnostic model were performed. P values <0.05 indicated statistical significance. RESULTS: Fasting-state CBF was correlated with cognitive scores in more brain regions (17 vs. 4 [MMSE] and 15 vs. 9 [MoCA]) and had higher absolute correlation coefficients than postprandial-state CBF. In the differential diagnosis of AD patients from MCI patients and HCs, fasting-state CBF outperformed mixed-state CBF, which itself outperformed postprandial-state CBF. DATA CONCLUSION: Compared with postprandial CBF, fasting-state CBF performed better in terms of cognitive score correlations and in differentiating AD patients from MCI patients and HCs. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Susceptibility-weighted image features in AQP4-negative-NMOSD versus MS.

OBJECTIVE: To characterize the susceptibility-weighted image (SWI) features including paramagnetic rim and nodular lesions with signal intensity changes and central vein sign (CVS) associated with aquaporin 4 (AQP4)-immunoglobulin G (IgG)-negative neuromyelitis optica spectrum disorder (NMOSD), and explore whether they can be used as potential imaging biomarkers for differentiating multiple sclerosis (MS) from this disorder. METHODS: We prospectively recruited NMOSD with AQP4-IgG-negative (AQP4- NMOSD) and IgG-positive (AQP4+ NMOSD), and MS subjects from the Clinical and Imaging Patterns of Neuroinflammation Diseases in China (CLUE) project (NCT0410683) between 2019 and 2021. The SWI features including paramagnetic rim and nodular lesions with signal intensity changes and CVS were analyzed and compared among groups, and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for distinguishing MS from AQP4- NMOSD. RESULTS: We enrolled a total of 160 consecutive patients (22 AQP4- NMOSD, 65 AQP4+ NMOSD, and 73 MS). We observed paramagnetic rim lesion (0/120 lesions, 0 %) and nodular (1/120, 1 %) lesions with hypointense signals on SWI in the AQP4- NMOSD group. These characteristics were similar to those recorded from AQP4+ NMOSD patients (rim: 0/369 lesions, 0 %, P = 1.000; nodular: 10/369 lesions, 2.7 %, P = 1.000), but differed significantly from those observed in the MS group (rim: 162/1665 lesions, 9.7 %, P<0.001; nodular: 392/1665 lesions, 23.5 %, P < 0.001). AQP4- NMOSD patients had fewer average CVS+ rate (12 %) than MS patients (46 %, p<0.001), similar to AQP4+ NMOSD (13 %, p = 1.000). The SWI imaging features denoting lesions with paramagnetic rim or nodular hypointense SWI signals showed 90.4 % sensitivity, 95.5 % specificity, 98.5 % PPV, and 75 % NPV, and the criteria with≥3 CVS lesions showed sensitivity of 91.8 %, specificity of 90.9 %%, PPV of 97.1 %, and NPV of 76.9 % in distinguishing MS from AQP4- NMOSD. DISCUSSION: The SWI imaging features including lesions with paramagnetic rim or nodular hypointense SWI signals and 3 CVS lesions carries useful information in distinguishing MS from AQP4- NMOSD.

Association of Mean Upper Cervical Spinal Cord Cross-Sectional Area With Cerebral Small Vessel Disease: A Community-Based Cohort Study.

BACKGROUND: The purpose of this study was to investigate the association between the mean upper cervical spinal cord cross-sectional area (MUCCA) and the risk and severity of cerebral small vessel disease (CSVD). METHODS: Community-dwelling residents in Lishui City, China, from the cross-sectional survey in the PRECISE cohort study (Polyvascular Evaluation for Cognitive Impairment and Vascular Events) conducted from 2017 to 2019. We included 1644 of 3067 community-dwelling adults in the PRECISE study after excluding those with incorrect, incomplete, insufficient, or missing clinical or imaging data. Total and modified total CSVD scores, as well as magnetic resonance imaging features, including white matter hyperintensity, lacunes, cerebral microbleeds, enlarged perivascular spaces, and brain atrophy, were assessed at the baseline. The Spinal Cord Toolbox was used to measure the upper cervical spinal cord cross-sectional area of the C1 to C3 segments of the spinal cord and its average value was taken as MUCCA. Participants were divided into 4 groups according to quartiles of MUCCA. Associations were analyzed using linear regression models adjusted for age, sex, current smoking and drinking, medical history, intracranial volume, and total cortical volume. RESULTS: The means±SD age of the participants was 61.4±6.5 years, and 635 of 1644 participants (38.6%) were men. The MUCCA was smaller in patients with CSVD than those without CSVD. Using the total CSVD score as a criterion, the MUCCA was 61.78±6.12 cm2 in 504 of 1644 participants with CSVD and 62.74±5.94 cm2 in 1140 of 1644 participants without CSVD. Using the modified total CSVD score, the MUCCA was 61.81±6.04 cm2 in 699 of 1644 participants with CSVD and 62.91±5.94 cm2 in 945 of 1644 without CSVD. There were statistical differences between the 2 groups after adjusting for covariates in 3 models. The MUCCA was negatively associated with the total and modified total CSVD scores (adjusted ß value, -0.009 [95% CI, -0.01 to -0.003] and -0.007 [95% CI, -0.01 to -0.0006]) after adjustment for covariates. Furthermore, the MUCCA was negatively associated with the white matter hyperintensity burden (adjusted ß value, -0.01 [95% CI, -0.02 to -0.003]), enlarged perivascular spaces in the basal ganglia (adjusted ß value, -0.005 [95% CI, -0.009 to -0.001]), lacunes (adjusted ß value, -0.004 [95% CI, -0.007 to -0.0007]), and brain atrophy (adjusted ß value, -0.009 [95% CI, -0.01 to -0.004]). CONCLUSIONS: The MUCCA and CSVD were correlated. Spinal cord atrophy may serve as an imaging marker for CSVD; thus, small vessel disease may involve the spinal cord in addition to being intracranial.

Cerebral glucose hypometabolism and hypoperfusion of cingulate gyrus: an imaging biomarker of autoimmune encephalitis with psychiatric symptoms.

BACKGROUND: About 60% of autoimmune encephalitis (AE) patients present psychiatric symptoms, but the underlying mechanism remains unknown. This study examined the role of the cingulate cortex in such patients to identify predictive poor psychiatric factors. METHODS: In this study, 49 AE patients and 39 healthy controls were enrolled. AE patients were further divided into two groups based on the presence/absence of psychiatric symptoms. The ratio of the standardized uptake value (SUVR) and relative cerebral blood flow (rCBF) in different regions of the cingulate cortex were calculated through positron emission tomography-computed tomography (PET/CT) and arterial spin labeling (ASL) MRI, and the results were compared among the three groups. In addition, we followed-up on the psychiatric outcomes and identified the risk factors for poor psychiatric prognosis, focusing on the cingulate cortex. RESULTS: More than half of the AE patients (27/49) exhibited psychiatric symptoms. Agitation and thought blocking were typical psychiatric phenotypes, except for anti-glutamic acid decarboxylase 65 (GAD65) encephalitis, which mainly presented with catatonia and a depressed mood. AE patients with psychiatric symptoms experienced reduced metabolism and perfusion of the anterior cingulate cortex (ACC), midcingulate cortex (MCC), and posterior cingulate cortex (PCC). The SUVR of ACC can be used as an independent risk factor of poor psychiatric outcomes, which had an area under the ROC curve (AUC) of 0.865. CONCLUSION: Impaired cingulate cortex function in AE may be the potential mechanism of psychiatric symptoms. Hypometabolism of ACC is an independent prognostic factor predicting an unfavorable psychiatric prognosis in AE.

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.

Independent and reproducible hippocampal radiomics biomarkers for multisite multiple sclerosis and neuromyelitis optica spectrum disorders.

OBJECTIVE: To investigate the abnormal radiomics features of the hippocampus in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) and to explore the clinical implications of these features. METHODS: 752 participants were recruited in this retrospective multicenter study (7 centers), which included 236 MS, 236 NMOSD, and 280 normal controls (NC). Radiomics features of each side of the hippocampus were extracted, including intensity, shape, texture, and wavelet features (N = 431). To identify the variations in these features, two-sample t-tests were performed between the NMOSD vs. NC, MS vs. NC, and NMOSD vs. MS groups at each site. The statistical results from each site were then integrated through meta-analysis. To investigate the clinical significance of the hippocampal radiomics features, we conducted further analysis to examine the correlations between these features and clinical measures such as Expanded Disability Status Scale (EDSS), Brief Visuospatial Memory Test (BVMT), California Verbal Learning Test (CVLT), and Paced Auditory Serial Addition Task (PASAT). RESULTS: Compared with NC, patients with MS exhibited significant differences in 78 radiomics features (P < 0.05/862), with the majority of these being texture features. Patients with NMOSD showed significant differences in 137 radiomics features (P < 0.05/862), most of which were intensity features. The difference between MS and NMOSD patients was observed in 47 radiomics features (P < 0.05/862), mainly texture features. In patients with MS and NMOSD, the most significant features related to the EDSS were intensity and textural features, and the most significant features related to the PASAT were intensity features. Meanwhile, both disease groups observed a weak correlation between radiomics data and BVMT. CONCLUSION: Variations in the microstructure of the hippocampus can be detected through radiomics, offering a new approach to investigating the abnormal pattern of the hippocampus in MS and NMOSD.

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.

PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. EXPERIMENTAL DESIGN: We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS: For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS: A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.

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.

Automated Determination of the H3 K27-Altered Status in Spinal Cord Diffuse Midline Glioma by Radiomics Based on T2-Weighted MR Images.

BACKGROUND AND PURPOSE: Conventional MR imaging is not sufficient to discern the H3 K27-altered status of spinal cord diffuse midline glioma. This study aimed to develop a radiomics-based model based on preoperative T2WI to determine the H3 K27-altered status of spinal cord diffuse midline glioma. MATERIALS AND METHODS: Ninety-seven patients with confirmed spinal cord diffuse midline gliomas were retrospectively recruited and randomly assigned to the training (n = 67) and test (n = 30) sets. One hundred seven radiomics features were initially extracted from automatically-segmented tumors on T2WI, then 11 features selected by the Pearson correlation coefficient and the Kruskal-Wallis test were used to train and test a logistic regression model for predicting the H3 K27-altered status. Sensitivity analysis was performed using additional random splits of the training and test sets, as well as applying other classifiers for comparison. The performance of the model was evaluated through its accuracy, sensitivity, specificity, and area under the curve. Finally, a prospective set including 28 patients with spinal cord diffuse midline gliomas was used to validate the logistic regression model independently. RESULTS: The logistic regression model accurately predicted the H3 K27-altered status with accuracies of 0.833 and 0.786, sensitivities of 0.813 and 0.750, specificities of 0.857 and 0.833, and areas under the curve of 0.839 and 0.818 in the test and prospective sets, respectively. Sensitivity analysis confirmed the robustness of the model, with predictive accuracies of 0.767-0.833. CONCLUSIONS: Radiomics signatures based on preoperative T2WI could accurately predict the H3 K27-altered status of spinal cord diffuse midline glioma, providing potential benefits for clinical management.

Development and validation of a nutrition-related genetic-clinical-radiological nomogram associated with behavioral and psychological symptoms in Alzheimer's disease.

BACKGROUND: Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer's disease (AD). This study aimed to develop and validate a novel genetic-clinical-radiological nomogram for evaluating BPSD in patients with AD and explore its underlying nutritional mechanism. METHODS: This retrospective study included 165 patients with AD from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort between June 1, 2021, and March 31, 2022. Data on demoimagedatas, neuropsychological assessments, single-nucleotide polymorphisms of AD risk genes, and regional brain volumes were collected. A multivariate logistic regression model identified BPSD-associated factors, for subsequently constructing a diagnostic nomogram. This nomogram was internally validated through 1000-bootstrap resampling and externally validated using a time-series split based on the CIBL cohort data between June 1, 2022, and February 1, 2023. Area under receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical applicability of the nomogram. RESULTS: Factors independently associated with BPSD were: CETP rs1800775 (odds ratio [OR] = 4.137, 95% confidence interval [CI]: 1.276-13.415, P = 0.018), decreased Mini Nutritional Assessment score (OR = 0.187, 95% CI: 0.086-0.405, P <0.001), increased caregiver burden inventory score (OR = 8.993, 95% CI: 3.830-21.119, P <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001-0.191, P = 0.004). These variables were incorporated into the nomogram. The area under the ROC curve was 0.925 (95% CI: 0.884-0.967, P <0.001) in the internal validation and 0.791 (95% CI: 0.686-0.895, P <0.001) in the external validation. The calibration plots showed favorable consistency between the prediction of nomogram and actual observations, and the DCA showed that the model was clinically useful in both validations. CONCLUSION: A novel nomogram was established and validated based on lipid metabolism-related genes, nutritional status, and brain stem volumes, which may allow patients with AD to benefit from early triage and more intensive monitoring of BPSD. REGISTRATION: Chictr.org.cn, ChiCTR2100049131.

Amide proton transfer-weighted imaging and derived radiomics in the classification of adult-type diffuse gliomas.

OBJECTIVES: To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma. MATERIALS AND METHODS: In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted. RESULTS: CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models. CONCLUSION: APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma. CLINICAL RELEVANCE STATEMENT: Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies. KEY POINTS: • This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma.

"Soap bubble" sign as an imaging marker for posterior fossa ependymoma Group B.

PURPOSE: To investigate the predictive value of the "soap bubble" sign on molecular subtypes (Group A [PFA] and Group B [PFB]) of posterior fossa ependymomas (PF-EPNs). METHODS: MRI scans of 227 PF-EPNs (internal retrospective discovery set) were evaluated by two independent neuroradiologists to assess the "soap bubble" sign, which was defined as clusters of cysts of various sizes that look like "soap bubbles" on T2-weighted images. Two independent cohorts (external validation set [n = 31] and prospective validation set [n = 27]) were collected to validate the "soap bubble" sign. RESULTS: Across three datasets, the "soap bubble" sign was observed in 21 PFB cases (7.4% [21/285] of PF-EPNs and 12.9% [21/163] of PFB); none in PFA. Analysis of the internal retrospective discovery set demonstrated substantial interrater agreement (1st Rating: κ = 0.71 [0.53-0.90], 2nd Rating: κ = 0.83 [0.68-0.98]) and intrarater agreement (Rater 1: κ = 0.73 [0.55-0.91], Rater 2: κ = 0.74 [0.55-0.92]) for the "soap bubble" sign; all 13 cases positive for the "soap bubble" sign were PFB (p = 0.002; positive predictive value [PPV] = 100%, negative predictive value [NPV] = 44%, sensitivity = 10%, specificity = 100%). The findings from the external validation set and the prospective validation set were similar, all cases positive for the "soap bubble" sign were PFB (p < 0.001; PPV = 100%). CONCLUSION: The "soap bubble" sign represents a highly specific imaging marker for the PFB molecular subtype of PF-EPNs.

Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics.

BACKGROUND: Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status. PURPOSE: To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG. STUDY TYPE: Prospective. POPULATION: Eighty-four BSG patients (median age 10.5 years [IQR 6.8-30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients. FIELD STRENGTH/SEQUENCE: 3 T, multi-shell diffusion imaging. ASSESSMENT: Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients. STATISTICAL TESTS: Chi-square test, Mann-Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant. RESULTS: 82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75). DATA CONCLUSION: Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration.

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs.