Isocitrate Dehydrogenase Status Research Articles

Grade 2, 3 and Dedifferentiated Chondrosarcomas: A Comparative Study of Isocitrate Dehydrogenase-Mutant and Wild-Type Tumors with Implications for Prognosis and Therapy.

Grade 2 and 3 and dedifferentiated chondrosarcomas (CS) are frequently associated with isocitrate dehydrogenase (IDH) mutations and often exhibit a poor clinical outcome. Treatment is limited mainly to surgery. Defining IDH status (wild type (WT) and mutant) and the associated transcriptome may prove useful in determining other therapeutic options in these neoplasms. Formalin-fixed paraffin-embedded material from 69 primary and recurrent grade 2, 3 and dedifferentiated CS was obtained. DNA sequencing for IDH1 and IDH2 mutations (n = 47) and RNA sequencing via Nextseq 2000 (n = 14) were performed. Differentially expressed genes (DEGs) were identified and used to predict aberrant biological pathways with Ingenuity Pathway Analysis (IPA) software (Qiagen). Gene Set Enrichment Analyses (GSEA) using subsets C3, C5 and C7 were performed. Differentially expressed genes were validated by immunohistochemistry. Outcome analysis was performed using the Wilcoxon test. A set of 69 CS (28 females, 41 males), average age 65, distributed among femur, pelvis, humerus, and chest wall were identified from available clinical material. After further selection based on available IDH status, we evaluated 15 IDH WT and 32 IDH mutant tumors as part of this dataset. Out of 15 IDH WT tumors, 7 involved the chest wall/scapula, while 1 of 32 mutants arose in the scapula. There were far more genes overexpressed in IDH WT tumors compared to IDH mutant tumors. Furthermore, IDH WT and IDH mutant tumors were transcriptomically distinct in the IPA and GSEA, with IDH mutant tumors showing increased activity in methylation pathways and endochondral ossification, while IDH WT tumors showed more activity in normal matrix development pathways. Validation immunohistochemistry demonstrated expression of WT1 and AR in IDH WT tumors, but not in IDH mutants. SATB2 was expressed in IDH mutant tumors and not in WT tumors. Outcome analysis revealed differences in overall survival between mutant and WT tumors (p = 0.04), dedifferentiated mutant and higher-grade (2, 3) mutant tumors (p = 0.03), and dedifferentiated mutant and higher-grade (2, 3) WT tumors (p = 0.03). The longest survival times were observed in patients with higher-grade WT tumors, while patients with dedifferentiated mutant tumors showed the lowest survival. Generally, patients with IDH WT tumors displayed longer survival in both the higher-grade and dedifferentiated groups. Grade 2, 3 and dedifferentiated chondrosarcomas are further characterized by IDH status, which in turn informs transcriptomic phenotype and overall survival. The transcriptome is distinct depending on IDH status, and implies different treatment targets.

The interplay mechanism between IDH mutation, MGMT-promoter methylation, and PRMT5 activity in the progression of grade 4 astrocytoma: unraveling the complex triad theory.

The dysregulation of Isocitrate dehydrogenase (IDH) and the subsequent production of 2-Hydroxyglutrate (2HG) may alter the expression of epigenetic proteins in Grade 4 astrocytoma. The interplay mechanism between IDH, O-6-methylguanine-DNA methyltransferase (MGMT)-promoter methylation, and protein methyltransferase proteins-5 (PRMT5) activity, with tumor progression has never been described. A retrospective cohort of 34 patients with G4 astrocytoma is classified into IDH-mutant and IDH-wildtype tumors. Both groups were tested for MGMT-promoter methylation and PRMT5 through methylation-specific and gene expression PCR analysis. Inter-cohort statistical significance was evaluated. Both IDH-mutant WHO grade 4 astrocytomas (n = 22, 64.7%) and IDH-wildtype glioblastomas (n = 12, 35.3%) had upregulated PRMT5 gene expression except in one case. Out of the 22 IDH-mutant tumors, 10 (45.5%) tumors showed MGMT-promoter methylation and 12 (54.5%) tumors had unmethylated MGMT. All IDH-wildtype tumors had unmethylated MGMT. There was a statistically significant relationship between MGMT-promoter methylation and IDH in G4 astrocytoma (p-value = 0.006). Statistically significant differences in progression-free survival (PFS) were also observed among all G4 astrocytomas that expressed PRMT5 and received either temozolomide (TMZ) or TMZ plus other chemotherapies, regardless of their IDH or MGMT-methylation status (p-value=0.0014). Specifically, IDH-mutant tumors that had upregulated PRMT5 activity and MGMT-promoter methylation, who received only TMZ, have exhibited longer PFS. The relationship between PRMT5, MGMT-promoter, and IDH is not tri-directional. However, accumulation of D2-hydroxyglutarate (2-HG), which partially activates 2-OG-dependent deoxygenase, may not affect their activities. In IDH-wildtype glioblastomas, the 2HG-2OG pathway is typically inactive, leading to PRMT5 upregulation. TMZ alone, compared to TMZ-plus, can increase PFS in upregulated PRMT5 tumors. Thus, using a PRMT5 inhibitor in G4 astrocytomas may help in tumor regression.

Evaluation of Immunohistochemical Expression of ALK-1 in Gliomas, WHO Grade 4 and Its Correlation with IDH1-R132H Mutation Status.

Glioblastoma (GB), a grade 4 glioma is the most common primary malignant brain tumor in adults. Recently, the mutation status of isocitrate dehydrogenase (IDH) has been crucial in the treatment of GB. IDH mutant cases display a more favorable prognosis than IDH-wild type ones. The anaplastic lymphoma kinase (ALK) is expressed as a receptor tyrosine kinase in both the developing central and peripheral nervous systems. Increasing lines of evidence suggest that ALK is over-expressed in GB and represents a potential therapeutic target. The goal of the current study was to investigate ALK-1 immunohistochemical expression in gliomas, grade 4, besides its correlation with IDH1-R132H mutation status and the clinicopathological parameters of the tumors. Seventy cases of gliomas, grade 4 were tested for immunohistochemical expression of ALK-1 & IDH1-R132H in the tumor cells. ALK-1 immunoexpression was detected in 22.9% of our cases and IDH1-R132H mutation was detected in 12.9% of them. ALK-1 expression (100%) was only detected in the more aggressive IDH R132H-negative GBs. ALK-1 expression was also noted in the larger-sized tumors, more in males and patients older than the mean age. Conclusion: Our results suggest that mutations in ALK-1 may predict a more dismal prognosis since ALK expression was only noted in IDH-R132H negative GBs known to have a considerably poorer outcome compared to IDH-R132H mutant cases. GBs with detectable ALK-protein expression could potentially experience substantial clinical advantages through the utilization of newly introduced ALK inhibitors allowing personalized treatment to a subset of patients. Hence, future studies targeting ALK in IDH wildtype Glioblastomas including clinical trials on larger scales are recommended.

Multi-level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas from MRI.

Isocitrate dehydrogenase (IDH) is one of the most important genotypes in patients with glioma because it can affect treatment planning. Machine learning-based methods have been widely used for prediction of IDH status (denoted as IDH prediction). However, learning discriminative features for IDH prediction remains challenging because gliomas are highly heterogeneous in MRI. In this paper, we propose a multi-level feature exploration and fusion network (MFEFnet) to comprehensively explore discriminative IDH-related features and fuse different features at multiple levels for accurate IDH prediction in MRI. First, a segmentation-guided module is established by incorporating a segmentation task and is used to guide the network in exploiting features that are highly related to tumors. Second, an asymmetry magnification module is used to detect T2-FLAIR mismatch sign from image and feature levels. The T2-FLAIR mismatch-related features can be magnified from different levels to increase the power of feature representations. Finally, a dual-attention feature fusion module is introduced to fuse and exploit the relationships of different features from intra- and inter-slice feature fusion levels. The proposed MFEFnet is evaluated on a multi-center dataset and shows promising performance in an independent clinical dataset. The interpretability of the different modules is also evaluated to illustrate the effectiveness and credibility of the method. Overall, MFEFnet shows great potential for IDH prediction.

Glioma monitoring and assessment: Utility of amide proton transfer MRI

AbstractMagnetic resonance imaging (MRI) plays a key role in the planning of glioma biopsy as well as postoperative monitoring for recurrence or progression. This review describes amide proton transfer (APT) MRI and its application to glioma imaging, including the use of APT MRI to address unmet clinical needs, common APT sequence parameter choices, and post‐processing methods. APT is a chemical exchange saturation transfer MRI technique that is sensitive to exchanging protons between water molecules and proteins and peptides containing amide groups in tissue. APT has shown promise in several areas including: differentiating glioma grading, correlation to cellular proliferation, association with molecular markers, and distinguishing pseudo‐progression from true progression. Multiple studies have shown that APTw values are affected by tumor markers including isocitrate dehydrogenase status and O6‐methylguanine‐DNA‐methyltransferase promoter status, demonstrating the potential for APT MRI. APT has also been shown to correlate with markers of cellular proliferation, including Ki‐67 index and low apparent diffusion coefficient on diffusion‐weighted imaging. Finally, this review also examines the potential limitations of APT MRI in clinical glioma imaging including the lack of standardization of acquisition and processing as well as the impact of 𝑇1, nuclear overhauser effect and magnetization transfer on the APT signal.

Diffusion along the perivascular space as a potential biomarker for glioma grading and isocitrate dehydrogenase 1 mutation status prediction.

The diffusion tensor image analysis along the perivascular space (DTI-ALPS) may have the potential to reflect glymphatic dysfunction in patients with glioma. The study aimed to determine the correlation of DTI-ALPS with glioma grade and isocitrate dehydrogenase 1 (IDH1) genotype and to then compare the ALPS index with other diffusion metrics. In this study, 81 patients with glioma and 31 healthy controls underwent magnetic resonance imaging (MRI) examination. The ALPS-index, fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) were calculated. Comparisons were made between the left and right hemispheres and between patients and controls. IDH1 status was compared after age adjustment. The diagnostic performance of each metric was assessed via receiver operating characteristic (ROC) analysis. In patients with glioma, the ALPS-index of the hemisphere ipsilateral to glioma was significantly lower than that of the hemisphere contralateral to glioma (1.417±0.177 vs. 1.478±0.165; P=0.002), and the bilateral ALPS-index values in patients were significantly decreased compared with those in healthy controls. The ALPS-index was significantly higher in lower-grade gliomas (LrGGs) than that in glioblastomas (GBMs) (1.495±0.151 vs. 1.320±0.159; P<0.001) and was significantly lower in IDH1-wild-type LrGGs than in IDH1-mutant LrGGs (1.400±0.185 vs. 1.530±0.123; P=0.036). FA, MD, and MK also showed significant differences between LrGGs and GBMs and between IDH1-mutant and IDH1-wild-type LrGGs (P<0.05). Furthermore, the combination of the ALPS-index with FA, MD, or MK, exhibited superior discrimination ability compared to each metric used alone. The ALPS-index combined with MD had the highest area under the curve (AUC) of 0.854 as compared to that of 0.614-0.807 for a single metric in glioma grading, while for IDH1 mutation prediction, this combination had the highest AUC of 0.861 as compared to that of 0.707-0.778 for a single metric. The reduced ALPS-index may reflect tumor-induced glymphatic system impairment, and the ALPS-index may be able to complement conventional diffusion metrics in glioma grading and IDH1 genotyping.

An Intra- and Inter-Modality Fusion Model with Invariant- and Specific-Constraints Using MR Images for Prediction of Glioma Isocitrate Dehydrogenase Mutation Status

In the 2021 World Health Organization classification of gliomas, it is proposed that Isocitrate Dehydrogenase (IDH) plays a key role. The prognosis of glioma is largely affected by IDH mutation status. Therefore, IDH mutation status needs to be predicted in advance before surgery. In the past decade, with the development of machine learning, more and more machine learning methods, especially deep learning methods, have been applied to the development of computer-aided diagnosis systems. At present, in this field, many deep learning and radiomics based methods have been proposed for IDH prediction using multimodal Magnetic Resonance Imaging (MRI). In this study, we proposed an intra- and inter-modality fusion model with invariant- and specific- constraints to improve the performance of IDH status prediction. First, MRI-based radiomics features were fused with deep learning features in each modality (intra-modality fusion) and then the features extracted from each modality of brain MRI were fused by using an inter-modality fusion model with invariant and specific constraints. We experimented our proposed method on the dataset provided by the Affiliated Hospital of Zhengzhou University in Zhengzhou, China and demonstrated the effectiveness of the proposed method. In our study, we propose two inter-modality fusion models, and our experimental results show that our best proposed method outperformed state-of-the-art methods with an accuracy of 0.79, precision of 0.80, recall of 0.75, and F1 score of 0.78. Thus, we predicted the IDH mutation status for glioma treatment with a 2% increase in accuracy and 4% increase in precision to predict the IDH mutation status for glioma treatment.

Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis

ObjectiveTo explain adult-type diffuse gliomas heterogeneity through diffusion kurtosis imaging-based habitat characteristics and develop and validate a comprehensive model for predicting isocitrate dehydrogenase (IDH) status.Materials and methodsIn this prospective secondary analysis, 103 participants (mean age, 52 years; range, 21-77; 54 [52%] male) pathologically diagnosed with adult-type diffuse gliomas were enrolled between June 2018 and February 2022. The Otsu method was used to generate habitat maps with mean diffusivity (MD) and mean kurtosis (MK) for a total of 4 subhabitats containing 16 habitat features. Habitat heatmaps were created based on the Pearson correlation coefficient. The Habitat imAging aNd clinicraD INtegrated prEdiction SyStem (HANDINESS) was created by combining clinical features, conventional MRI morphological features, and habitat image features. ROC, calibration curve, and decision curve analyses were used to select the optimal model after 32 pipelines for model training and validation.ResultsIn the restricted diffusion and high-density subhabitat, MK was highly correlated with MD (R2 = 0.999), volume (0.608) and percentage of volume (0.663), and this region had the highest MK value (P&amp;lt;.001). The unrestricted diffusion and low-density subhabitat had the highest MD value (P&amp;lt;.001). When MK was less than the Otsu threshold, there was still a difference between restricted diffusion and low-density and unrestricted diffusion and low-density subhabitats (P&amp;lt;.01). The HANDINESS enabled more accurate prediction of the IDH status in the training (AUC=0.951 [0.902-0.987]) and internal validation cohorts (0.938 [0.881-0.949]). AUC values for single-modality models and independent factors ranged from 0.593 to 0.916. Calibration and decision curve analyses showed that the HANDINESS demonstrated a high level of clinical applicability and predictive consistency.ConclusionDiffusion kurtosis imaging-based habitat analysis provides additional important information on microscopic tumor spatial heterogeneity. The HANDINESS has higher diagnostic performance and robustness than single-modality models.

PATH-39. INTERPRETABLE IDH CLASSIFICATION FROM H&amp;E-STAINED HISTOLOGY SLIDES

Abstract BACKGROUND Infiltrating gliomas are the most common primary adult brain tumors. Isocitrate dehydrogenase (IDH) mutation is a key driver of gliomagenesis in 25–30% of infiltrating gliomas and correlates with favorable prognosis when compared with the histologically-similar but biologically-distinct IDH-wildtype glioblastoma. We sought an interpretable computational pipeline to predict IDH status from digitized histopathology glioma sections (Whole Slide Images - WSI) and identify associated cell-level morphology. Materials and METHODS We identify 799/1122 TCGA-GBM and TCGA-LGG tumors (1534 WSIs) with available IDH status. 379 of these tumors (756 WSI) are IDH-wildtype and 420 tumors (778 WSI) are IDH-mutant. Each H&amp;E-stained WSI undergoes comprehensive curation to remove artifactual content before classification. Our interpretability mechanism is based on weakly-supervised attention-based multiple-instance-learning. IDH-associated morphology in each WSI is evaluated by a HoverNet model from the 50 highest-attention tiles. Classification performance was assessed using 10-fold cross-validation, with training (80%), validation (10%), and test (10%) sets stratified on the patient/tumor level, without controlling for grade. An additional out-of-sample hold-out test set of 82 IDH-wildtype and 32 IDH-mutant tumors from the University of Pennsylvania Health System was further used for independent evaluation. RESULTS Quantitative performance evaluation revealed AUC=0.92 and Balanced Accuracy=0.84 on the TCGA test set, and AUC=0.88 and Balanced Accuracy=0.80 on the independent hold-out UPenn set. When comparing identified IDH-wildtype with IDH-mutant tumors (and while excluding glioblastoma with lower-grade histology), the former shows significantly (p&amp;lt; 0.00005) higher distribution of neoplastic and necrotic cells and significantly (p&amp;lt; 0.00003) lower distribution of inflammatory and connective cell type tissue. CONCLUSION Histological prediction of IDH status represented an ideal prototype to interrogate WSI towards identifying clinically-relevant tumor biomarkers. Detailed interpretation of results reveals computational predictions being driven by human-identifiable features at the patch level. This study robustly identifies morpho-IDH correlates and could enable further prognostic refinement of patients.

Deep wavelet scattering orthogonal fusion network for glioma IDH mutation status prediction

Accurately predicting the isocitrate dehydrogenase (IDH) mutation status of gliomas is greatly significant for formulating appropriate treatment plans and evaluating the prognoses of gliomas. Although existing studies can accurately predict the IDH mutation status of gliomas based on multimodal magnetic resonance (MR) images and machine learning methods, most of these methods cannot fully explore multimodal information and effectively predict IDH status for datasets acquired from multiple centers. To address this issue, a novel wavelet scattering (WS)-based orthogonal fusion network (WSOFNet) was proposed in this work to predict the IDH mutation status of gliomas from multiple centers. First, transformation-invariant features were extracted from multimodal MR images with a WS network, and then the multimodal WS features were used instead of the original images as the inputs of WSOFNet and were fully fused through an adaptive multimodal feature fusion module (AMF2M) and an orthogonal projection module (OPM). Finally, the fused features were input into a fully connected classifier to predict IDH mutation status. In addition, to achieve improved prediction accuracy, four auxiliary losses were also used in the feature extraction modules. The comparison results showed that the prediction area under the curve (AUC) of WSOFNet on a single-center dataset was 0.9966 and that on a multicenter dataset was approximately 0.9655, which was at least 3.9% higher than that of state-of-the-art methods. Moreover, the ablation experimental results also proved that the adaptive multimodal feature fusion strategy based on orthogonal projection could effectively improve the prediction performance of the model, especially for an external validation dataset.

P19.08.B INCREASED CORTICAL EXCITABILITY TO TRANSCRANIAL MAGNETIC STIMULATION CONTRIBUTE TO EPILEPTOGENESIS IN IDH1-MUTANT GLIOMAS

Abstract BACKGROUND In-vitro studies indicate that gliomas with a mutation of the isocitrate dehydrogenase (IDH) increase neuronal excitability of the peritumoral cortex contributing to epileptogenesis in these patients. However, in-vivo evidence is missing. This study evaluates the electric characteristics of the brain-tumor-interface (BTI) relative to the IDH mutation status. MATERIAL AND METHODS To investigate peritumoral cortical excitability (CE), we applied 5258 pulses of transcranial magnetic stimulation (TMS) at the BTI of IDH-mutant (IDH-mt) and IDH-wildtype (IDH-wt) glial tumors in 39 patients. CE was assessed by the resting motor threshold (RMT) and the synchronized electromyographic (EMG) activity (i.e., event-related spectral perturbation, ERSP) after TMS. ERSP values were related to the IDH status, tumor grading, antiepileptic drug intake (AED) and the spatial relationship to the tumor borders. RESULTS There was not significant group difference in RMT. TMS to the BTI triggered an EMG synchronization decreasing linearly with the distance to the functional hotspot. In contrast, IDH-mt gliomas demonstrated an increased cortical output of the peritumoral brain tissue compared to IDH-wt gliomas. This effect was not attributable to AED intake or other histological and molecular characteristics. Notably, cortical hyperexcitability was detectable well beyond the tumor border. CONCLUSION s: This study provides in-vivo evidence of cortical hyperexcitability at the BTI of IDH-mt gliomas which could contribute to the epileptogenesis in these patients.

S100A6 is a potential diagnostic and prognostic biomarker for human glioma.

S100 calcium-binding protein A6 (S100A6) is a protein that belongs to the S100 family. The present study aimed to investigate the function of S100A6 in the diagnosis and survival prediction of glioma and elucidated the potential processes affecting glioma development. The Cancer Genome Atlas database was searched to identify the relationship among S100A6 expression, immune cell infiltration, clinicopathological parameters and glioma prognosis. Several clinical cases were used to verify these findings. S100A6 gene expression was high in glioma tissues, suggesting its diagnostic significance. In particular, S100A6 upregulation in glioma tissues exhibited a significant and positive correlation with the World Health Organization (WHO) grade, histological type, age, sex, primary treatment outcomes, 1p/19q codeletion, isocitrate dehydrogenase (IDH) status, overall survival (OS), progression-free interval and disease-specific survival. Kaplan-Meier and Cox regression analyses revealed that S100A6 gene expression can independently function as a risk factor affecting the prognosis of patients with glioma. Furthermore, Gene Ontology functional enrichment analysis revealed that S100A6 is implicated in immune responses and that the expression profiles of S100A6 are linked to the immune microenvironment. Furthermore, immunohistochemistry revealed that increased S100A6 protein levels are correlated with age, 1p/19q codeletion, IDH status, WHO grade and OS. The present findings suggest that increased S100A6 expression is an indicator of the dismal prognosis of patients with glioma and that it can be used as a potential diagnostic biomarker for this condition.

The Correlation of In Vivo MR Spectroscopy and Ex Vivo 2-Hydroxyglutarate Concentration for the Prediction of Isocitrate Dehydrogenase Mutation Status in Diffuse Glioma.

Isocitrate dehydrogenase (IDH) mutation status is an important biomarker in the glioma-defining subtype and corresponding prognosis. This study proposes a straightforward method for 2-hydroxyglutarate (2-HG) quantification by MR spectroscopy for IDH mutation status detection and directly compares in vivo 2-HG MR spectroscopy with ex vivo 2-HG concentration measured in resected tumor tissue. Eleven patients with suspected lower-grade glioma (ten IDH1; one IDHwt) were prospectively included. Preoperatively, 3T point-resolved spectroscopy (PRESS) was acquired; 2-HG was measured as the percentage elevation of Glx3 (the sum of 2-HG and Glx) compared to Glx4. IDH mutation status was assessed by immunochemistry or direct sequencing. The ex vivo 2-HG concentration was determined in surgically obtained tissue specimens using gas chromatography-mass spectrometry. Pearson correlation was used for assessing the correlation between in vivo MR spectroscopy and ex vivo 2-HG concentration. MR spectroscopy was positive for 2-HG in eight patients, all of whom had IDH1 tumors. A strong correlation (r = 0.80, p = 0.003) between 2-HG MR spectroscopy and the ex vivo 2-HG concentration was found. This study shows in vivo 2-HG MR spectroscopy can non-invasively determine IDH status in glioma and demonstrates a strong correlation with ex vivo 2-HG concentration in patients with lower-grade glioma.

Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis.

Mutations in isocitrate dehydrogenase (IDH) represent an independent predictor of better survival in patients with gliomas. We aimed to assess grade and IDH mutation status in patients with untreated gliomas, by evaluating the respective value of 18F-FET PET/CT via dynamic and texture analyses. A total of 73 patients (male: 48, median age: 47) who underwent an 18F-FET PET/CT for initial glioma evaluation were retrospectively included. IDH status was available in 61 patients (20 patients with WHO grade 2 gliomas, 41 with grade 3-4 gliomas). Time-activity curve type and 20 parameters obtained from static analysis using LIFEx© v6.30 software were recorded. Respective performance was assessed using receiver operating characteristic curve analysis and stepwise multivariate regression analysis adjusted for patients' age and sex. The time-activity curve type and texture parameters derived from the static parameters showed satisfactory-to-good performance in predicting glioma grade and IDH status. Both time-activity curve type (stepwise OR: 101.6 (95% CI: 5.76-1791), p = 0.002) and NGLDM coarseness (stepwise OR: 2.08 × 1043 (95% CI: 2.76 × 1012-1.57 × 1074), p = 0.006) were independent predictors of glioma grade. No independent predictor of IDH status was found. Dynamic and texture analyses of 18F-FET PET/CT have limited predictive value for IDH status when adjusted for confounding factors. However, they both help predict glioma grade.

2-Hydroxyglutarate magnetic resonance spectroscopy in adult brainstem glioma.

Adult brainstem gliomas (BSGs) are rare tumors of the CNS that are poorly understood. Upregulation of the oncometabolite 2-hydroxyglutarate (2HG) in the tumor indicates the mutation of isocitrate dehydrogenase (IDH), which can be detected by magnetic resonance spectroscopy (MRS). Although histological examination is required for the definitive diagnosis of BSG, 2HG-optimized MRS (2HG-MRS) may be useful, considering the difficult nature of brainstem lesion biopsy. The aim of this study was to evaluate the utility of 2HG-MRS for diagnosing IDH-mutant adult BSG. Patients with a radiographically confirmed brainstem tumor underwent 3T MRS. A single voxel was set in the lesion with reference to the T2 or fluid-attenuated inversion recovery image and analyzed according to the 2HG-tailored MRS protocol (point-resolved spectroscopic sequence; echo time 35 msec). All patients underwent intraoperative navigation-guided or CT-guided stereotactic biopsy for histopathological diagnosis. The status of IDH and H3K27M mutations was confirmed by immunohistochemistry and direct DNA sequencing. In addition, the authors examined the relationship between patients' 2HG concentrations and survival time. Ten patients (7 men, 3 women; median age 33.5 years) underwent 2HG-MRS and biopsy. Four patients had an H3K27M mutation and 4 had an IDH1 mutation (1 R132H canonical IDH mutation, 2 R132S and 1 R132G noncanonical IDH mutations). Two had neither H3K27M nor IDH mutations. The H3K27M and IDH mutations were mutually exclusive. Most tumors were located in the pons. There was no significant radiological difference between mutant H3K27M and IDH on a conventional MRI sequence. A 2HG concentration ≥ 1.8 mM on MRS demonstrated 100% (95% CI 28%-100%) sensitivity and 100% (95% CI 42%-100%) specificity for IDH-mutant BSG (p = 0.0048). The median overall survival was 10 months in IDH-wild-type BSG patients (n = 6) and could not be estimated in IDH-mutant BSG patients (n = 4) due to the small number of deaths (p = 0.008). 2HG-MRS demonstrated high sensitivity and specificity for the prediction of IDH-mutant BSG. In addition, 2HG-MRS may be useful for predicting the prognosis of adult BSG patients.

Multi-task Model for Glioma Segmentation and Isocitrate Dehydrogenase Status Prediction Using Global and Local Features.

According to the 2021 World Health Organization IDH status prediction scheme for gliomas, isocitrate dehydrogenase (IDH) is a particularly important basis for glioma diagnosis. In general, 3D multimodal brain MRI is an effective diagnostic tool. However, only using brain MRI data is difficult for experienced doctors to predict the IDH status. Surgery is necessary to be performed for confirming the IDH. Previous studies have shown that brain MRI images of glioma areas contain a lot of useful information for diagnosis. These studies usually need to mark the glioma area in advance to complete the prediction of IDH status, which takes a long time and has high computational cost. The tumor segmentation task model can automatically segment and locate the tumor region, which is exactly the information needed for the IDH prediction task. In this study, we proposed a multi-task deep learning model using 3D multimodal brain MRI images to achieve glioma segmentation and IDH status prediction simultaneously, which improved the accuracy of both tasks effectively. Firstly, we used a segmentation model to segment the tumor region. Also, the whole MRI image and the segmented glioma region features as the global and local features were used to predict IDH status. The effectiveness of the proposed method was validated via a public glioma dataset from the BraTS2020. Our experimental results show that our proposed method outperformed state-of-the-art methods with a prediction accuracy of 88.5% and average dice of 79.8%. The improvements in prediction and segmentation are 3% and 1% compared with the state-of-the-art method, respectively.

Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences.

The present study aimed to establish a robust predictive model based on a machine learning (ML) algorithm providing preoperative noninvasive diagnosis and to further explore the contribution of each magnetic resonance imaging (MRI) sequence to the classification to help select images for future model development. This was a retrospective cross-sectional study, and consecutive patients with histologically confirmed diffuse gliomas in our hospital from November 2015 to October 2019 were recruited. The participants were grouped into a training and testing set based on a ratio of 8:2. Five MRI sequences were employed to develop the support vector machine (SVM) classification model. An advanced contrast analysis of single-sequence-based classifiers was performed, according to which different sequence combinations were tested, and the best one was selected to form an ultimate classifier. Patients whose MRIs were acquired with other types of scanners formed an additional, independent validation set. A total of 150 patients with gliomas were used in the present study. Contrast analysis revealed that the contribution of the apparent diffusion coefficient (ADC) was the most significant [accuracies were as follows: histological phenotype, 0.640; isocitrate dehydrogenase (IDH) status, 0.656; and Ki-67 expression, 0.699] and that of T1 weighted imaging was limited (accuracies were as follows: histological phenotype, 0.521; IDH status, 0.492; and Ki-67 expression, 0.556). The ultimate classifiers for IDH status, histological phenotype, and Ki-67 expression achieved promising performances with area under the curve (AUC) values of 0.88, 0.93, and 0.93, respectively. The classifiers for the histological phenotype, IDH status, and Ki-67 expression correctly predicted 3 of 5 subjects, 6 of 7 subjects, and 9 of 13 subjects in the additional validation set, respectively. The present study showed satisfactory performance in predicting the IDH genotype, histological phenotype, and Ki-67 expression level. The contrast analysis revealed the contribution of different MRI sequences and suggested that the combination of all the acquired sequences was not the optimal strategy to build the radiogenomics-based classifier.

Convolutional neural network to predict IDH mutation status in glioma from chemical exchange saturation transfer imaging at 7 Tesla.

Noninvasive prediction of isocitrate dehydrogenase (IDH) mutation status in glioma guides surgical strategies and individualized management. We explored the capability on preoperatively identifying IDH status of combining a convolutional neural network (CNN) and a novel imaging modality, ultra-high field 7.0 Tesla (T) chemical exchange saturation transfer (CEST) imaging. We enrolled 84 glioma patients of different tumor grades in this retrospective study. Amide proton transfer CEST and structural Magnetic Resonance (MR) imaging at 7T were performed preoperatively, and the tumor regions are manually segmented, leading to the "annotation" maps that offers the location and shape information of the tumors. The tumor region slices in CEST and T1 images were further cropped out as samples and combined with the annotation maps, which were inputted to a 2D CNN model for generating IDH predictions. Further comparison analysis to radiomics-based prediction methods was performed to demonstrate the crucial role of CNN for predicting IDH based on CEST and T1 images. A fivefold cross-validation was performed on the 84 patients and 4090 slices. We observed a model based on only CEST achieved accuracy of 74.01% ± 1.15%, and the area under the curve (AUC) of 0.8022 ± 0.0147. When using T1 image only, the prediction performances dropped to accuracy of 72.52% ± 1.12% and AUC of 0.7904 ± 0.0214, which indicates no superiority of CEST over T1. However, when we combined CEST and T1 together with the annotation maps, the performances of the CNN model were further boosted to accuracy of 82.94% ± 1.23% and AUC of 0.8868 ± 0.0055, suggesting the importance of a joint analysis of CEST and T1. Finally, using the same inputs, the CNN-based predictions achieved significantly improved performances above those from radiomics-based predictions (logistic regression and support vector machine) by 10% to 20% in all metrics. 7T CEST and structural MRI jointly offer improved sensitivity and specificity of preoperative non-invasive imaging for the diagnosis of IDH mutation status. As the first study of CNN model on imaging acquired at ultra-high field MR, our results could demonstrate the potential of combining ultra-high-field CEST and CNN for facilitating decision-making in clinical practice. However, due to the limited cases and B1 inhomogeneities, the accuracy of this model will be improved in our further study.

Decreased SPTBN2 expression regulated by the ceRNA network is associated with poor prognosis and immune infiltration in low‑grade glioma.

The majority of low-grade gliomas (LGGs) in adults invariably progress to glioblastoma over time. Spectrin β non-erythrocytic 2 (SPTBN2) is detected in numerous tumors and is involved in tumor occurrence and metastasis. However, the specific roles and detailed mechanisms of SPTBN2 in LGG are largely unknown. The present study performed pan-cancer analysis for the expression and prognosis of SPTBN2 in LGG using The Cancer Genome Atlas and The Genotype-Tissue Expression. Western blotting was used to detect the amount of SPTBN2 between glioma tissues and normal brain tissues. Subsequently, based on expression, prognosis, correlation and immune infiltration, non-coding RNAs (ncRNAs) were identified that regulated SPTBN2 expression. Finally, tumor immune infiltrates associated with SPTBN2 and prognosis were performed. Lower expression of SPTBN2 was correlated with an unfavorable outcome in LGG. A significant correlation between the low SPTBN2 mRNA expression and poor clinicopathological features was observed, including wild-type isocitrate dehydrogenase status (P<0.001), 1p/19q non-codeletion (P<0.001) and elders (P=0.019). The western blotting results revealed that, compared with normal brain tissues, the amount of SPTBN2 was significantly lower in LGG tissues (P=0.0266). Higher expression of five microRNAs (miRs/miRNAs), including hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-34c-5p and hsa-miR-424-5p, correlated with poor prognosis by targeting SPTBN2 in LGG. Subsequently, four long ncRNAs (lncRNAs) [ARMCX5-GPRASP2, BASP1-antisense RNA 1 (AS1), EPB41L4A-AS1 and LINC00641] were observed in the regulation of SPTBN2 via five miRNAs. Moreover, the expression of SPTBN2 was significantly correlated with tumor immune infiltration, immune checkpoint expression and biomarkers of immune cells. In conclusion, SPTBN2 was lowly expressed and correlated with an unfavorable prognosis in LGG. A total of six miRNAs and four lncRNAs were identified as being able to modulate SPTBN2 in a lncRNA-miRNA-mRNA network of LGG. Furthermore, the current findings also indicated that SPTBN2 possessed anti-tumor roles by regulating tumor immune infiltration and immune checkpoint expression.

Differentiation of astrocytoma between grades II and III using a combination of methionine positron emission tomography and magnetic resonance spectroscopy

ObjectiveThis study aimed to establish a method for differentiating between grades II and III astrocytomas using preoperative imaging. MethodsWe retrospectively analyzed astrocytic tumors, including 18 grade II astrocytomas (isocitrate dehydrogenase (IDH)-mutant: IDH-wildtype = 8:10) and 56 grade III anaplastic astrocytomas (37:19). We recorded the maximum methionine (MET) uptake ratios (tumor-to-normal: T/N) on positron emission tomography (PET) and three MRS peak ratios: choline (Cho)/creatine (Cr), N-acetyl aspartate (NAA)/Cr, and Cho/NAA, between June 2015 and June 2020. We then evaluated the cut-off values to differentiate between grades II and III. We compared the grading results between contrast enhancement effects on MR and combinational diagnostic methods (CDM) on a scatter chart using the cutoff values of the T/N ratio and MRS parameters. ResultsThe IDH-mutant group showed significant differences in the Cho/NAA ratio between grades II and III using univariate analysis; however, multiple regression analysis results negated this. The IDH-wildtype group showed no significant differences between the groups. Contrast enhancement effects also showed no significant differences in IDH status. Accordingly, regardless of the IDH status, no statistically independent factors differentiated between grades II and III. However, CDMs showed higher sensitivity and negative predictive value in distinguishing them than MRI contrast examinations for both IDH statuses. We demonstrated a significantly higher diagnostic rate of grade III than of grade II with CDM, which was more striking in the IDH-mutant group than in the wild-type group. ConclusionsCDM could be valuable in differentiating between grade II and III astrocytic tumors.