Isocitrate Dehydrogenase Mutation Status Research Articles

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI.

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. Included were 71 glioma patients (mean age, 50.17 ± 13.38years; 35 men). HM-MRI images were collected at five different echo times (80-200ms) with seven b-values (0-3000s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). HM-MRI can aid in non-invasive diagnosis of gliomas.

MODERN IMAGING TECHNIQUES IN THE ASSESSMENT&#x0D; OF MALIGNANCY IN GLIAL BRAIN TUMORS:&#x0D; LITERATURE REVIEW

Relevance: Gliomas represent a major group of primary central nervous system tumors, accounting for approximately 80% of all&#x0D; malignant brain neoplasms. Recent advances in molecular diagnostics, including the identification of mutations in the isocitrate dehydrogenase(IDH) gene, offer new opportunities to improve the classification of gliomas and to understand the mechanisms of their development. IDH&#x0D; mutations, which are found in a large proportion of gliomas, are associated with a better prognosis and represent an important biomarker&#x0D; for the development of targeted therapeutic strategies. Diagnostic techniques, particularly magnetic resonance imaging (MRI), also play&#x0D; an important role, providing additional ways to assess the morphological and biological characteristics of gliomas, contributing to a more&#x0D; accurate determination of tumor malignancy and molecular profile, which is key to treatment decisions.&#x0D; The purpose: To study the possibilities of magnetic resonance imaging in assessing the degree of malignancy of glial brain tumors.&#x0D; Method: A literature review was performed using the keywords: glioma, IDH mutation, IDH wild-type, MRI, magnetic resonance&#x0D; spectroscopy (MRS), apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis&#x0D; image (DKI), Susceptibility Weighted Imaging (SWI) in PubMed, BMC Medicine, and Google Scholar databases. The review includes the results&#x0D; of an analysis of 45 literature reviews on the diagnostic capabilities of molecular diagnostics and MRI to assess the malignancy of gliomas.&#x0D; Results: This review highlights the importance of current approaches to the diagnosis of gliomas, demonstrating that different grades of&#x0D; malignancy and IDH mutation status are associated with unique features on MR imaging, including differences in localization, signal, and&#x0D; enhancement patterns. DTI and DWI extend the ability of MRI to assess tissue microstructure, allowing for more precise tumor characterization.&#x0D; Studies combining MRI morphologic features, ADC and DTI parameters offer a non-invasive approach to glioma prognosis, highlighting the&#x0D; importance of integrated use of imaging modalities to improve diagnosis and therapy.&#x0D; Conclusion: The use of magnetic resonance imaging allows for more accurate morphologic and metabolic characterization of gliomas&#x0D; in assessing the degree of malignancy, allowing physicians to make more accurate prognostic assessments and select the most appropriate&#x0D; treatment strategy.&#x0D; Keywords: Glioma, IDH mutation, wild-type IDH, MRI, magnetic resonance spectroscopy, ADC, DWI, DTI, DKI, SWI

Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas.

An accurate assessment of isocitrate dehydrogenase (IDH) status in patients with glioma is crucial for treatment planning and is a key factor in predicting patient outcomes. In this study, we investigated the potential value of whole-tumor histogram metrics derived from synthetic magnetic resonance imaging (MRI) in distinguishing IDH mutation status between astrocytoma and glioblastoma. In this prospective study, 80 glioma patients were enrolled from September 2019 to June 2022. All patients underwent pre- and post-contrast synthetic MRI scan protocol. Immunohistochemistry (IHC) staining or gene sequencing were used to assess IDH mutation status in tumor tissue samples. Whole-tumor histogram metrics, including T1, T2, proton density (PD), etc., were extracted from the quantitative maps, while radiological features were assessed by synthetic contrast-weighted maps. Basic clinical features of the patients were also evaluated. Differences in clinical, radiological, and histogram metrics between IDH-mutant astrocytoma and IDH-wildtype glioblastoma were analyzed using univariate analyses. Variables with statistical significance in univariate analysis were included in multivariate logistic regression analysis to develop the combined model. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to assess the diagnostic performance of metrics and models. The histopathologic analysis revealed that of the 80 cases, 41 were classified as IDH-mutant astrocytoma and 39 as IDH-wildtype glioblastoma. Compared to IDH-wildtype glioblastoma, IDH-mutant astrocytoma showed significantly lower T1 [10th percentile (10th), mean, and median] and post-contrast PD (10th, 90th percentile, mean, median, and maximum) values as well as higher post-contrast T1 (cT1) (10th, mean, median, and minimum) values (all P<0.05). The combined model (T1-10th + cT1-10th + age) was developed by integrating the independent influencing factors of IDH-mutant astrocytoma using the multivariate logistic regression. The diagnostic performance of this model [AUC =0.872 (0.778-0.936), sensitivity =75.61%, and specificity =89.74%] was superior to the clinicoradiological model, which was constructed using age and enhancement degree (AUC =0.822 (0.870-0.898), P=0.035). The combined model constructed using histogram metrics derived from synthetic MRI could be a valuable preoperative tool to distinguish IDH mutation status between astrocytoma and glioblastoma, and subsequently, could assist in the decision-making process of pretreatment.

The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study.

Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. 64 patients (female, 21; male, 43; age, 47.0 ± 13.7years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET.

According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation

BackgroundGlioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation.MethodsPlasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations.ResultsThe derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of β-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas.ConclusionsThe plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of β-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.

Mutations in IDH1/2 Genes Predict Better Disease Outcome of Glioma Patients-A Study from Western India

Introduction: Isocitrate Dehydrogenase (IDH) plays an important role in cellular metabolism. In gliomas, the mutational status of IDH1/2 genes have paramount significance, however, study from Western India is limited. Therefore, the current study we sought to explore the clinical impact of IDH1/2 mutations for glioma patients from Western India.&#x0D; Materials and Method: A total of 50 pre-therapeutic, histopathologically confirmed patients with astrocytoma tumors were included and IDH1/2 mutations were detected using real-time PCR. IDH1/2 mutations were correlated with clinicopathological parameters and disease outcome. Data was evaluated by SPSS software.&#x0D; Results: The overall incidence of IDH1/2 mutations was noted in 24% (12/50) of glioma patients. Out of 12 patients whose tumors showed IDH mutations, 83% patients have IDH1 mutations, whereas 17% showed IDH2 mutation. Further, in IDH1 mutations, IDH1 R132H &amp; IDH1 R132C mutations were noted in, 80% and 20% of patients, respectively. When correlated with clinicopathological parameters, significant inverse correlation was found with patients age (χ2= 9.75, r = -0.476, p=0.001) and grade of tumors (χ2=17.51, r =-0.636, p=0.0001). In Kaplan-Meier survival analysis, a part from age (Log rank=5.443, p=0.020), IDH mutation status (Log rank=3.855, p=0.050), and both, IDH mutation and low grade glioma tumors (Log rank=6.492, p=0.039) remained significant parameters for predicting better 24 months PFS and OS of glioma patients. However, in multivariate survival analysis using Cox Proportional Hazard Forward Stepwise Model, only combination of low grade glioma with presence of IDH mutation emerged at step one as positive significant independent prognostic factor that predict better PFS (HR=2.92, 95% CI=1.12-7.61, p=0.028) and OS (HR=3.0, 95% CI=1.45-6.19, p=0.003).&#x0D; Conclusion: Based on this data, we concluded that for glioma patients, apart from patients age, low grade tumors with presence of IDH mutations remained significant independent positive prognosticators and would be helpful to clinicians for better management of glioma patients.

Accuracy of Radiomics in Predicting IDH Mutation Status in Diffuse Gliomas: A Bivariate Meta-Analysis.

Purpose To perform a systematic review and meta-analysis assessing the predictive accuracy of radiomics in the noninvasive determination of isocitrate dehydrogenase (IDH) status in grade 4 and lower-grade diffuse gliomas. Materials and Methods A systematic search was performed in the PubMed, Scopus, Embase, Web of Science, and Cochrane Library databases for relevant articles published between January 1, 2010, and July 7, 2021. Pooled sensitivity and specificity across studies were estimated. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2, and methods were evaluated using the radiomics quality score (RQS). Additional subgroup analyses were performed according to tumor grade, RQS, and number of sequences used (PROSPERO ID: CRD42021268958). Results Twenty-six studies that included 3280 patients were included for analysis. The pooled sensitivity and specificity of radiomics for the detection of IDH mutation were 79% (95% CI: 76, 83) and 80% (95% CI: 76, 83), respectively. Low RQS scores were found overall for the included works. Subgroup analyses showed lower false-positive rates in very low RQS studies (RQS < 6) (meta-regression, z = -1.9; P = .02) compared with adequate RQS studies. No substantial differences were found in pooled sensitivity and specificity for the pure grade 4 gliomas group compared with the all-grade gliomas group (81% and 86% vs 79% and 79%, respectively) and for studies using single versus multiple sequences (80% and 77% vs 79% and 82%, respectively). Conclusion The pooled data showed that radiomics achieved good accuracy performance in distinguishing IDH mutation status in patients with grade 4 and lower-grade diffuse gliomas. The overall methodologic quality (RQS) was low and introduced potential bias. Keywords: Neuro-Oncology, Radiomics, Integration, Application Domain, Glioblastoma, IDH Mutation, Radiomics Quality Scoring Supplemental material is available for this article. Published under a CC BY 4.0 license.

Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners.

Determination of isocitrate dehydrogenase (IDH) genotype is crucial in the stratification of diagnosis and prognostication in diffuse gliomas. We sought to build and validate radiomics models and clinical features incorporated nomogram for preoperative prediction of IDH mutation status and WHO grade of diffuse gliomas with L-[methyl-11C] methionine ([11C]MET) PET/CT imaging according to the 2016 WHO classification of tumors of the central nervous system. Consecutive 178 preoperative [11C]MET PET/CT images were retrospectively studied for radiomics analysis. One hundred six patients from PET scanner 1 were used as training dataset, and 72 patients from PET scanner 2 were used for validation dataset. [11C]MET PET and integrated CT radiomics features were extracted, respectively; three independent predictive models were built based on PET features, CT features, and combined PET/CT features, respectively. The SelectKBest method, Spearman correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning algorithms were applied for feature selection and model building. After filtering the satisfactory predictive model, key clinical features were incorporated for the nomogram establishment. The combined [11C]MET PET/CT radiomics model, which consisted of four PET features and eight integrated CT features, was significantly associated with IDH genotype (p < 0.0001 for both training and validation datasets). Nomogram based on the [11C]MET PET/CT radiomics score, patients' age, and dichotomous tumor location status showed satisfactory discrimination capacity, and the AUC was 0.880 (95% CI, 0.726-0.998) in the training dataset and 0.866 (95% CI, 0.777-0.956) in the validation dataset. In IDH stratified WHO grade prediction, the final radiomics model consists of four PET features and two CT features had reasonable and stable differential efficacy of WHO grade II and III patients from grade IV patients in IDH-wildtype patients, and the AUC was 0.820 (95% CI, 0.541-1.000) in the training dataset and 0.766 (95% CI, 0.612-0.921) in the validation dataset. [11C]MET PET radiomics features could benefit non-invasive IDH genotype prediction, and integrated CT radiomics features could enhance the efficacy. Radiomics and clinical features incorporation could establish satisfactory nomogram for clinical application. This non-invasive predictive investigation based on our consecutive cohort from two PET scanners could provide the perspective to observe the differential efficacy and the stability of radiomics-based investigation in untreated diffuse gliomas.

A biosensor for D-2-hydroxyglutarate in frozen sections and intraoperative assessment of IDH mutation status

The oncometabolite D-2-hydroxyglutarate (D-2-HG) has emerged as a valuable biomarker in tumors with isocitrate dehydrogenase (IDH) mutations. Efficient detection methods are required and rapid intraoperative determination of D-2-HG remains a huge challenge. Herein, D-2-HG dehydrogenase from Achromobacter xylosoxidans (AX-D2HGDH) was found to have high substrate specificity. AX-D2HGDH dehydrogenizes D-2-HG and reduces flavin adenine dinucleotide (FAD) bound to the enzyme. Interestingly, the dye resazurin can be taken as another substrate to restore FAD. AX-D2HGDH thus catalyzes a bisubstrate and biproduct reaction: the dehydrogenation of D-2-HG to 2-ketoglutarate and simultaneous reduction of non-fluorescent resazurin to highly fluorescent resorufin. According to steady-state analysis, a ping-pong bi-bi mechanism has been concluded. The Km values for resazurin and D-2-HG were determined as 0.56 μM and 10.93 μM, respectively, suggesting high affinity to both substrates. On the basis, taking AX-D2HGDH and resazurin as recognition and fluorescence transducing element, a D-2-HG biosensor (HGAXR) has been constructed. HGAXR exhibits high sensitivity, accuracy and specificity for D-2-HG in different biological samples. With the aid of HGAXR and the matched low-cost palm-size detecting device, D-2-HG levels in frozen sections of resected brain tumor tissues can be measured in a direct, simple and accurate manner with a fast detection (1–3 min). As the technique of frozen section is familiar to surgeons and pathologists, HGAXR and the portable device can be easily integrated into the current workflow, having potential to provide rapid intraoperative pathology for IDH mutation status and guide decision-making during surgery.

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.

Radiosynthesis and biological evaluation of [18F]AG-120 for PET imaging of the mutant isocitrate dehydrogenase 1 in glioma

Glioma are clinically challenging tumors due to their location and invasiveness nature, which often hinder complete surgical resection. The evaluation of the isocitrate dehydrogenase mutation status has become crucial for effective patient stratification. Through a transdisciplinary approach, we have developed an 18F-labeled ligand for non-invasive assessment of the IDH1R132H variant by using positron emission tomography (PET) imaging. In this study, we have successfully prepared diastereomerically pure [18F]AG-120 by copper-mediated radiofluorination of the stannyl precursor 6 on a TRACERlab FX2 N radiosynthesis module. In vitro internalization studies demonstrated significantly higher uptake of [18F]AG-120 in U251 human high-grade glioma cells with stable overexpression of mutant IDH1 (IDH1R132H) compared to their wild-type IDH1 counterpart (0.4 vs. 0.013% applied dose/µg protein at 120 min). In vivo studies conducted in mice, exhibited the excellent metabolic stability of [18F]AG-120, with parent fractions of 85% and 91% in plasma and brain at 30 min p.i., respectively. Dynamic PET studies with [18F]AG-120 in naïve mice and orthotopic glioma rat model reveal limited blood-brain barrier permeation along with a low uptake in the brain tumor. Interestingly, there was no significant difference in uptake between mutant IDH1R132H and wild-type IDH1 tumors (tumor-to-blood ratio[40−60 min]: ~1.7 vs. ~1.3). In conclusion, our preclinical evaluation demonstrated a target-specific internalization of [18F]AG-120 in vitro, a high metabolic stability in vivo in mice, and a slightly higher accumulation of activity in IDH1R132H-glioma compared to IDH1-glioma. Overall, our findings contribute to advancing the field of molecular imaging and encourage the evaluation of [18F]AG-120 to improve diagnosis and management of glioma and other IDH1R132H-related tumors.

NIMG-69. LEPTOMENINGEAL METASTASES IN IDH-WILDTYPE GLIOBLASTOMAS REVISITED: COMPREHENSIVE ANALYSIS OF INCIDENCE, RISK FACTORS, AND PROGNOSIS BASED ON POSTCONTRAST FLAIR IMAGING

Abstract BACKGROUND To comprehensively investigate the incidence, risk factors, and prognosis of leptomeningeal metastases (LM) in isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients. METHODS Total 913 IDH-wildtype glioblastoma patients with complete molecular information on IDH mutation and MGMT promoter methylation status were enrolled between 2004 and 2022. LM diagnosis was performed with MRI including postcontrast FLAIR and CSF cytology. Logistic regression analysis for LM development at initial diagnosis and recurrence was performed with clinical, surgical, imaging, and molecular data. The overall survival (OS) was compared between patients with and without LM. RESULTS The incidence of LM was 21.8% (199 patients) among patients with IDH-wildtype glioblastoma, with 11.5% (105 patients) at initial diagnosis, and 10.3% (94 patients) at recurrence. Male sex (odds ratio [OR]: 1.74, P = 0.017), MGMT promoter unmethylation (OR: 1.48, P = 0.048), nonlobar location (OR: 2.27, P &amp;lt; 0.001), and presence of necrosis (OR: 3.64, P &amp;lt; 0.001) were independent predictors of LM at initial diagnosis, whereas ventricular opening during surgery (OR: 2.96, P &amp;lt; 0.001) was the only independent predictor of LM at recurrence. The median OS was 16.2 months (interquartile range [IQR]: 9.6-30.4) in patients with LM and 19.3 months (IQR: 12.1-38.5) in patients without LM (log-rank test; P = 0.001). CONCLUSION The incidence of LM is high in patients with IDH-wildtype glioblastoma. Aggressive clinical, imaging, and molecular factors are correlated with LM at initial diagnosis, whereas ventricular entry warrant imaging surveillance for LM at recurrence. The prognosis of LM is not discouraging as expected, and promote reconsideration of focused clinical trials.

Multimodel habitats constructed by perfusion and/or diffusion MRI predict isocitrate dehydrogenase mutation status and prognosis in high-grade gliomas

To determine whether tumour vascular and cellular heterogeneity of high-grade glioma (HGG) is predictive of isocitrate dehydrogenase (IDH) mutation status and overall survival (OS) by using tumour habitat-based analysis constructed by perfusion and/or diffusion magnetic resonance imaging (MRI). Seventy-eight HGG patients that met the 2021 World Health Organization WHO Classification of Tumors of the Central Nervous System, 5th edition (WHO CNS5), were enrolled to predict IDH mutation status, of which 32 grade 4 patients with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter were enrolled for prognostic analysis. The deep-learning-based model nnU-Net and K-means clustering algorithm were applied to construct the Traditional Habitat, Vascular Habitat (VH), Cellular Density Habitat (DH), and their Combined Habitat (CH). Quantitative parameters were extracted and compared between IDH-mutant and IDH-wild-type patients, respectively, and the prediction potential was evaluated by receiver operating characteristic (ROC) curve analysis. OS was analysed using Kaplan-Meier survival analysis and the log-rank test. Compared with IDH-mutants, median relative cerebral blood volume (rCBVmedian) values in the whole enhancing tumour (WET), VH1, VH3, CH1-4 habitats were significantly increased in IDH-wild-type HGGs (all p<0.05). Additionally, the accuracy of rCBVmedian values in CH1 outperformed other habitats in identifying IDH mutation status (p<0.001) at a cut-off value of 4.83 with AUC of 0.815. Kaplan-Meier survival analysis highlighted significant differences in OS between the populations dichotomised by the median of rCBVmedian in WET, VH1, CH1-3 habitats (all p<0.05). The habitat imaging technique may improve the accuracy of predicting IDH mutation status and prognosis, and even provide a new direction for subsequent personalised precision treatment.

Proton MR spectroscopy shows improved performance to segregate high-grade astrocytoma subgroups when defined with the new 2021 World Health Organization classification of central nervous system tumors.

The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors prioritizes isocitrate dehydrogenase (IDH) mutation to define tumor types in diffuse gliomas, in contrast to the 2016 classification, which prioritized histological features. Our objective was to investigate the influence of this change in the performance of proton MR spectroscopy (1H-MRS) in segregating high-grade diffuse astrocytoma subgroups. Patients with CNS WHO grade 3 and 4 diffuse astrocytoma, known IDH mutation status, and available 1H-MRS were retrospectively retrieved and divided into 4 groups based on IDH mutation status and histological grade. Differences in 1H-MRS between groups were analyzed with the Kruskal-Wallis test. The points on the spectrum that showed the greatest differences were chosen to evaluate the performance of 1H-MRS in discriminating between grades 3 and 4 tumors (WHO 2016 defined), and between IDH-mutant and IDH-wildtype tumors (WHO 2021). ROC curves were constructed with these points, and AUC values were calculated and compared. The study included 223 patients with high-grade diffuse astrocytoma. Discrimination between IDH-mutant and IDH-wildtype tumors showed higher AUC values (highest AUC short TE, 0.943; long TE, 0.864) and more noticeable visual differences than the discrimination between grade 3 and 4 tumors (short TE, 0.885; long TE, 0.838). Our findings suggest that 1H-MRS is more applicable to classify high-grade astrocytomas defined with the 2021 criteria. Improved metabolomic robustness and more homogeneous groups yielded better tumor type discrimination by 1H-MRS with the new criteria. The 2021 World Health Organization classification of brain tumors empowers molecular criteria to improve tumor characterization. This derives in greater segregation of high-grade diffuse astrocytoma subgroups by MR spectroscopy and warrants further development of brain tumor classification tools with spectroscopy. • The new 2021 updated World Health Organization classification of central nervous system tumors maximizes the role of molecular diagnosis in the classification of brain tumors. • Proton MR spectroscopy performs better to segregate high-grade astrocytoma subgroups when defined with the new criteria. • The study provides additional evidence of improved metabolic characterization of brain tumor subgroups with the new criteria.

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.

Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI.

The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér-Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann-Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas.

MRI-Based Deep Learning Method for Classification of IDH Mutation Status.

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin-Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.

P02.18.B INTEGRATIVE OMICS ANALYSIS OF IDH1 MUTANT GLIOMA PATIENTS REVEALS ALTERATIONS IN BUTYRATE METABOLISM

Abstract BACKGROUND Mutations in isocitrate dehydrogenase (IDH) 1 or 2 define glioma classification and determine the biology of these tumors. Although IDH is an essential enzyme in the cellular metabolism, powerful genome-wide analyses focus mostly at the genetic and epigenetic levels, while differences between distinct glioma entities at the proteomics, metabolomics, and functional levels are still poorly understood. Here, we provide an integrative multi-omics analysis of glioma patients to reveal metabolomic vulnerabilities of gliomas stratified by IDH mutation. MATERIAL AND METHODS We performed a multi-omics analysis of two cohorts of glioma patients (n=28) stratified based on IDH mutation status. Molecular assessment included LC-MS analysis, EPIC DNA methylation arrays, RNA-seq/HTA array-based transcriptomics and label-free quantitative proteomics. In 13 patients 13C-glucose was injected prior to surgery and tumor and adjacent brain tissue was collected. Gut microbial profiling using 16S ribosomal RNA (rRNA) sequencing was performed in a separate cohort of 128 glioma patients and 24 healthy individuals. Additional validation was performed in isogenic glioma stem-like cultures with CRISPR knock-in of IDH mutation. RESULTS We show that several molecular layers collectively contribute to biological differences between IDH mutant and wild-type gliomas, with strongest differences observed at the DNA methylation level. IDH mutant gliomas present specific molecular features linked to tumor metabolism. We confirm previous findings on 2-HG, cysteine and de novo glutathione synthesis pathways, which are specifically altered in IDH mutant gliomas. We further identify butyrate metabolism as a novel pathway highly active in IDH mutant gliomas. Butyrate is a bacterial metabolite with multiple systemic effects that is normally synthesized in the gut. This is supported by our gut microbiota metagenomics analysis of glioma patients. Yet the detection of enzymes associated with butyrate metabolism in tumor tissue and cell lines suggests a differential/enhanced metabolic rewiring of the butyrate signaling in IDH mutant gliomas CONCLUSION Our data shows the importance of multiple molecular layers in shaping specific features of IDH mutant gliomas. We unravel an activated butyrate metabolism in IDH mutant gliomas that may underly a novel gut-brain axis in glioma patients.