Predictive machine learning models based on VASARI features for WHO grading, isocitrate dehydrogenase mutation, and 1p19q co-deletion status: a multicenter study.

The objective of our study was to develop predictive models using Visually Accessible Rembrandt Images (VASARI) magnetic resonance imaging (MRI) features combined with machine learning techniques to predict the World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation status, and 1p19q co-deletion status of high-grade gliomas. To achieve this, we retrospectively included 485 patients with high-grade glioma from the First Affiliated Hospital of Xinjiang Medical University, of which 312 patients were randomly divided into a training set (n=218) and a test set (n=94) in a 7:3 ratio. Twenty-five VASARI MRI features were selected from an initial set of 30, and three machine learning models - Multilayer Perceptron (MP), Bernoulli Naive Bayes (BNB), and Logistic Regression (LR) - were trained using the training set. The most informative features were identified using recursive feature elimination. Model performance was assessed using the test set and an independent validation set of 173 patients from Beijing Tiantan Hospital. The results indicated that the MP model exhibited the highest predictive accuracy on the training set, achieving an area under the curve (AUC) close to 1, indicating perfect discrimination. However, its performance decreased in the test and validation sets; particularly for predicting the 1p19q co-deletion status, the AUC was only 0.703, suggesting potential overfitting. On the other hand, the BNB model demonstrated robust generalization on the test and validation sets, with AUC values of 0.8292 and 0.8106, respectively, for predicting IDH mutation status and 1p19q co-deletion status, indicating high accuracy, sensitivity, and specificity. The LR model also showed good performance with AUCs of 0.7845 and 0.8674 on the test and validation sets, respectively, for predicting IDH mutation status, although it was slightly inferior to the BNB model for the 1p19q co-deletion status. In conclusion, integrating VASARI MRI features with machine learning techniques shows promise for the non-invasive prediction of glioma molecular markers, which could guide treatment strategies and improve prognosis in glioma patients. Nonetheless, further model optimization and validation are necessary to enhance its clinical utility.

Therapeutic inhibition of isocitrate dehydrogenase mutations in glioma and cholangiocarcinoma: new insights and promises-a narrative review.

BACKGROUND AND OBJECTIVE: The identification of mutation hot spots in the isocitrate dehydrogenase (IDH) genes is one of the most important cancer genome-wide sequencing discoveries with relevant impact in the treatment of some orphan tumors. These genes were mostly found mutated in lower-grade gliomas (LGGs), acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPNs) and in cholangiocarcinoma. This aberrant genomic condition represents a therapeutic target of great interest in cancer research, especially in AML, given the limitations of currently approved therapies in this field. In this review, we investigate the role of IDH mutation and the mutant IDH (mIDH)- targeted therapies for cholangiocarcinoma and glioma. METHODS: Here, we provide an overview of the IDH mutation role and discuss its role in tumorigenesis and progression of some solid cancers, in which the therapeutic strategy can be completely changed thanks to these brand-new therapeutic options. KEY CONTENT AND FINDINGS: The encouraging early clinical data demonstrated to be a proof of concept for investigational mIDH1/2 inhibitors in tumors with a paucity of therapeutic possibilities. CONCLUSIONS: Moreover, we list the most important randomised clinical trials still active with their preliminary results.

AB066. From imaging to molecular diagnosis: VASARI magnetic resonance imaging features to predict isocitrate dehydrogenase mutation status in glioma.

BACKGROUND: Glioma, the most common brain tumor, poses significant challenges in patient care and economic burden. Clinicians often struggle with management strategies, especially under the 2021 World Health Organization (WHO) central nervous system (CNS) classification emphasizing molecular diagnosis. Isocitrate dehydrogenase (IDH) mutation status is crucial in glioma management. However, many facilities lack the capability for comprehensive molecular tests, and not all patients are candidates for invasive biopsies. MRI offers a non-invasive method to evaluate glioma characteristics. The Visually Accessible Rembrandt Images (VASARI) MRI feature set provides a systematic approach to analyzing brain glioma. This study examines the association of VASARI features with IDH mutation status and their predictive capability. METHODS: This study included 105 glioma patients treated between 2017 and 2022 who had not undergone surgery, chemotherapy, or radiotherapy. Brain MRIs were assessed using VASARI MRI features by two blinded radiologists. Pathological and molecular examinations were conducted per the 2021 WHO CNS tumor classification. IDH mutations were assessed using polymerase chain reaction (PCR) followed by DNA sequencing. Chi-squared analysis identified VASARI features significantly associated with IDH mutation status. A random forest model predicted IDH mutation status using these features. RESULTS: Brain MRI assessments using VASARI terminology showed good inter-observer agreement (kappa =0.714-0.831) and excellent intra-observer agreement (kappa =0.910). Thirteen VASARI features were significantly associated with IDH mutation status. The prediction model based on VASARI MRI features achieved an area under the curve (AUC) of 0.97, with 93.75% sensitivity, 75% specificity, and 84.38% accuracy on test data. CONCLUSIONS: The VASARI MRI feature set is a reliable method for evaluating glioma patients and is feasible for routine radiological practice. Several VASARI features significantly associate with IDH mutation status, aiding glioma patient management. The IDH mutation prediction model based on VASARI features performs excellently and warrants further validation before routine implementation.

Gene expression profiling and the isocitrate dehydrogenase mutational landscape of temozolomide­resistant glioblastoma.

Glioblastoma multiforme (GBM) is an aggressive brain cancer that occurs more frequently than other brain tumors. The present study aimed to reveal a novel mechanism of temozolomide resistance in GBM using bioinformatics and wet lab analyses, including meta-Z analysis, Kaplan-Meier survival analysis, protein-protein interaction (PPI) network establishment, cluster analysis of co-expressed gene networks, and hierarchical clustering of upregulated and downregulated genes. Next-generation sequencing and quantitative PCR analyses revealed downregulated [tyrosine kinase with immunoglobulin and epidermal growth factor homology domains 1 (TIE1), calcium voltage-gated channel auxiliary subunit α2Δ1 (CACNA2D1), calpain 6 (CAPN6) and a disintegrin and metalloproteinase with thrombospondin motifs 6 (ADAMTS6)] and upregulated [serum amyloid (SA)A1, SAA2, growth differentiation factor 15 (GDF15) and ubiquitin specific peptidase 26 (USP26)] genes. Different statistical models were developed for these genes using the Z-score for P-value conversion, and Kaplan-Meier plots were constructed using several patient cohorts with brain tumors. The highest number of nodes was observed in the PPI network was for ADAMTS6 and TIE1. The PPI network model for all genes contained 35 nodes and 241 edges. Immunohistochemical staining was performed using isocitrate dehydrogenase (IDH)-wild-type or IDH-mutant GBM samples from patients and a significant upregulation of TIE1 (P<0.001) and CAPN6 (P<0.05) protein expression was demonstrated in IDH-mutant GBM in comparison with IDH-wild-type GBM. Structural analysis revealed an IDH-mutant model demonstrating the mutant residues (R132, R140 and R172). The findings of the present study will help the future development of novel biomarkers and therapeutics for brain tumors.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

Predicting Isocitrate Dehydrogenase Status in Non-Contrast-Enhanced Adult-Type Astrocytic Tumors Using Diffusion Tensor Imaging and 11C-Methionine, 11C-Choline, and 18F-Fluorodeoxyglucose PET.

We aimed to differentiate the isocitrate dehydrogenase (IDH) status among non-enhanced astrocytic tumors using preoperative MRI and PET. We analyzed 82 patients with non-contrast-enhanced, diffuse, supratentorial astrocytic tumors (IDH mutant [IDH-mut], 55 patients; IDH-wildtype [IDH-wt], 27 patients) who underwent MRI and PET between May 2012 and December 2022. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) values using diffusion tensor imaging. We evaluated the tumor/normal brain uptake (T/N) ratios using 11C-methionine, 11C-choline, and 18F-fluorodeoxyglucose PET; extracted the parameters with significant differences in distinguishing the IDH status; and verified their diagnostic accuracy. Patients with astrocytomas were significantly younger than those with glioblastomas. The following MRI findings were significant predictors of IDH-wt instead of IDH-mut: thalamus invasion, contralateral cerebral hemisphere invasion, location adjacent to the ventricular walls, higher FA value, and lower MD value. The T/N ratio for all tracers was significantly higher for IDH-wt than for IDH-mut. In a composite diagnosis based on nine parameters, including age, 84.4% of cases with 0-4 points were of IDH-mut; conversely, 100% of cases with 6-9 points were of IDH-wt. Composite diagnosis using all parameters, including MRI and PET findings with significant differences, may help guide treatment decisions for early-stage gliomas.

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.

PURPOSE: 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. METHODS: 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. RESULTS: 64 patients (female, 21; male, 43; age, 47.0 ± 13.7 years) 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). CONCLUSIONS: The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Methylation Profiling Identifies Stability of Isocitrate Dehydrogenase Mutation Over Time.

OBJECTIVE: Isocitrate dehydrogenase (IDH) mutation status is a key diagnostic and prognostic feature of gliomas. It is thought to occur early in glioma tumorigenesis and remain stable over time. However, there are reports documenting a loss of IDH mutation status in a subset of patients with glioma recurrence. Here, we identified patients with a documented loss of IDH mutation status longitudinally and performed multi-platform analysis in order to determine if IDH mutations are stable throughout glioma evolution. METHODS: We retrospectively identified patients from our institution from 2009 to 2018 with immunohistochemistry (IHC)-recorded IDH mutation status changes longitudinally. Archived formalin-fixed paraffin-embedded and frozen tissue samples from these patients were collected from our institution's tumour bank. Samples were analysed using methylation profiling, copy number variation, Sanger sequencing, droplet digital PCR (ddPCR) and IHC. RESULTS: We reviewed 1491 archived glioma samples including 78 patients with multiple IDH mutant tumour samples collected longitudinally. In all instances of documented loss of IDH mutation status, multi-platform profiling identified a mixture of low tumour cell content and non-neoplastic tissue including perilesional, reactive or inflammatory cells. CONCLUSIONS: All patients with a documented loss of IDH mutation status longitudinally were resolved through multi-platform analysis. These findings support the hypothesis that IDH mutations occur early in gliomagenesis and in the absence of copy number changes at the IDH loci and are stable throughout tumour treatment and evolution. Our study highlights the importance of accurate surgical sampling and the role of DNA methylome profiling in diagnostically uncertain cases for integrated pathological and molecular diagnosis.

IDH Mutations in Chondrosarcoma: Case Closed or Not?

Chondrosarcomas are malignant cartilage-producing tumours that frequently harbour isocitrate dehydrogenase 1 and -2 (IDH) gene mutations. Several studies have confirmed that these mutations are key players in the early stages of cartilage tumour development, but their role in later stages remains ambiguous. The prognostic value of IDH mutations remains unclear and preclinical studies have not identified effective treatment modalities (in)directly targeting these mutations. In contrast, the IDH mutation status is a prognostic factor in other cancers, and IDH mutant inhibitors as well as therapeutic strategies targeting the underlying vulnerabilities induced by IDH mutations seem effective in these tumour types. This discrepancy in findings might be ascribed to a difference in tumour type, elevated D-2-hydroxyglutarate levels, and the type of in vitro model (endogenous vs. genetically modified) used in preclinical studies. Moreover, recent studies suggest that the (epi)genetic landscape in which the IDH mutation functions is an important factor to consider when investigating potential therapeutic strategies or patient outcomes. These findings imply that the dichotomy between IDH wildtype and mutant is too simplistic and additional subgroups indeed exist within chondrosarcoma. Future studies should focus on the identification, characterisation, and tailoring of treatments towards these biological subgroups within IDH wildtype and mutant chondrosarcoma.

Isocitrate Dehydrogenase 1 and 2 Mutations in Pediatric Neuroblastoma Patients.

Objective: Neuroblastoma is one of the common tumors of childhood. The demonstration of new factors such as isocitrate dehydrogenase 1 (IDH1) and isocitrate dehydrogenase 2 (IDH2) mutations will be important in the diagnosis and treatment. IDH1 and IDH2 mutations have been found in many types of cancer, such as malignant gliomas, acute myeloid leukemias, chondrosarcoma, and thyroid carcinoma. This study aimed to investigate the presence of IDH1 or IDH2 mutations in patients with neuroblastoma and to determine whether these mutations were different in terms of age, clinical findings, and response to treatment. Methods: Biopsy specimens of 25 patients with pediatric neuroblastoma patients were evaluated for IDH mutations. The clinical and laboratory features of the patients with/without mutation were retrospectively analyzed from a hospital database. Results: A total of 25 patients for whom genetic analysis could be performed were included in the study (60% male, n=15). The mean age was 32.2±25.9 months (3 days-96 months). IDH1 mutation was detected in 8 (32%) and IDH2 mutations in 5 (20%) patients. These mutations showed no statistically significant relationship with age, tumor localization, laboratory results, stage, and prognosis. However, in the case of IDH mutation, patients were diagnosed at the advanced stage. Conclusions: This study demonstrated the relationship between neuroblastoma and IDH mutation for the first time. Because to the fact that the mutation is very heterogeneous, it would be appropriate to conduct a larger series of patients in terms of the impact of the clinical significance of each mutation on the diagnosis and prognosis.

The Antiepileptic Drug Oxcarbazepine Inhibits the Growth of Patient-Derived Isocitrate Dehydrogenase Mutant Glioma Stem-like Cells.

Patients diagnosed with isocitrate dehydrogenase mutant (IDHmut) gliomas suffer frequently from seizures. Although the clinical course is less aggressive than that of its IDH wildtype counterpart, recent discoveries have shown that epileptic activity can promote tumor proliferation. However, it is not known if antiepileptic drugs confer additional value by inhibiting tumor growth. In this study, the antineoplastic properties of 20 FDA-approved antiepileptic drugs (AEDs) were tested in six patient-derived IDHmut glioma stem-like cells (GSCs). Cell proliferation was assessed using the CellTiterGlo-3D assay. Two of the screened drugs (oxcarbazepine and perampanel) demonstrated an antiproliferative effect. A subsequent eight-point dose-response curve proved the dose-dependent growth inhibition for both drugs, but only oxcarbazepine reached an IC50 value below 100 µM in 5/6 GSCs (mean 44.7 µM; range 17.4-98.0 µM), approximating the possible cmax for oxcarbazepine in patient serums. Furthermore, the treated GSC spheroids were 82% smaller (mean volume 1.6 nL vs. 8.7 nL; p = 0.01 (live/deadTM fluorescence staining)), and the apoptotic events increased by more than 50% (caspase-3/7 activity; p = 0.006). Taken together, this drug screen of a large series of antiepileptic drugs identified oxcarbazepine as a potent proapoptotic drug in IDHmut GSCs, which combines antiepileptic and antineoplastic properties to treat this seizure-prone patient population.

Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.

Background: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers. Methods: We retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models' performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models. Results: The LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164). Conclusions: The radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.

Predictive role of magnetic resonance imaging in the distinction of isocitrate dehydrogenase (IDH) mutant grade 4 astrocytomas versus glioblastomas.

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status is a crucial prognostic factor in high-grade glial tumors. PURPOSE: To investigate whether magnetic resonance imaging (MRI) features can display a diagnostic performance in the determination of IDH mutation in high-grade gliomas. MATERIAL AND METHODS: A total of 170 patients including 24 IDH mutant grade 4 astrocytomas and 146 glioblastomas (GBM) were retrospectively examined via contrast-enhanced (CE) MRI before surgery. Immunohistochemistry and genomic sequence analyses were performed on specimen materials for the determination of IDH mutational status. Certain morphological and diffusion-weighted imaging (DWI) parameters were utilized to see if they could play a role to be non-invasive potential imaging predictors in the discrimination of IDH mutant versus wild-type (WT) high-grade gliomas. RESULTS: On histopathological examination, IDH mutation was detected in 24 patients with high-grade glioma and 146 of the patients were found to be WT. Certain morphological criteria of tumor location and involvement, tumor margins, visual detection of diffusion restriction on DWI, and quantitative apparent diffusion coefficient (ADC) parameters consisting of ADCmean, ADCmin, and ADCr could be used as imaging predictors in the discrimination of high-grade IDH mutant versus WT tumors. CONCLUSION: Certain MRI morphologic features and visual detection of diffusion restriction on DWI and quantitative ADC parameters consisting of ADCmean, ADCmin, and ADCr can be considered non-invasive, significant independent imaging predictors in the discrimination and can obviate invasive procedures for histopathological diagnosis.

Prediction of the Molecular Subtype of IDH Mutation Combined with MGMT Promoter Methylation in Gliomas via Radiomics Based on Preoperative MRI.

BACKGROUND: The molecular subtype of IDH mut combined with MGMT meth in gliomas suggests a good prognosis and potential benefit from TMZ chemotherapy. The aim of this study was to establish a radiomics model to predict this molecular subtype. METHOD: The preoperative MR images and genetic data of 498 patients with gliomas were retrospectively collected from our institution and the TCGA/TCIA dataset. A total of 1702 radiomics features were extracted from the tumour region of interest (ROI) of CE-T1 and T2-FLAIR MR images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used for feature selection and model building. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. RESULTS: Regarding clinical variables, age and tumour grade were significantly different between the two molecular subtypes in the training, test and independent validation cohorts (p < 0.05). The areas under the curve (AUCs) of the radiomics model based on 16 selected features in the SMOTE training cohort, un-SMOTE training cohort, test set and independent TCGA/TCIA validation cohort were 0.936, 0.932, 0.916 and 0.866, respectively, and the corresponding F1-scores were 0.860, 0.797, 0.880 and 0.802. The AUC of the independent validation cohort increased to 0.930 for the combined model when integrating the clinical risk factors and radiomics signature. CONCLUSIONS: radiomics based on preoperative MRI can effectively predict the molecular subtype of IDH mut combined with MGMT meth.

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas.

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.

Development and validation of an LC-MS/MS method for D- and L-2-hydroxyglutaric acid measurement in cerebrospinal fluid, urine and plasma: application to glioma.

Aim: IDH mutations have been identified as frequent molecular lesions in several tumor types, particularly in gliomas. As a putative marker of IDH mutations, elevated D-2-HG has been reported in glioma, acute myeloid leukemia and intrahepatic cholangiocarcinoma. Excessive production of L-2-HG has also been described in renal cell carcinoma and 2-hydroxyaciduria. Materials & methods: The authors present a fully optimized stable isotope dilution multiple reaction monitoring method for quantification of D-/L-2-HG using LC-MS/MS. This is the first method validation study performed on cerebrospinal fluid, plasma and urine demonstrating clinical applicability with samples from glioma patients. Results & conclusion: This method validation study showed high accuracy and precision with low limit of detection and limit of quantification values. The authors believe that the presented approach is highly applicable for basic and clinical research on related pathologies.

Recent advances of IDH1 mutant inhibitor in cancer therapy.

Isocitrate dehydrogenase (IDH) is the key metabolic enzyme that catalyzes the conversion of isocitrate to α-ketoglutarate (α-KG). Two main types of IDH1 and IDH2 are present in humans. In recent years, mutations in IDH have been observed in several tumors, including glioma, acute myeloid leukemia, and chondrosarcoma. Among them, the frequency of IDH1 mutations is higher than IDH2. IDH1 mutations have been shown to increase the conversion of α-KG to 2-hydroxyglutarate (2-HG). IDH1 mutation-mediated accumulation of 2-HG leads to epigenetic dysregulation, altering gene expression, and impairing cell differentiation. A rapidly emerging therapeutic approach is through the development of small molecule inhibitors targeting mutant IDH1 (mIDH1), as evidenced by the recently approved of the first selective IDH1 mutant inhibitor AG-120 (ivosidenib) for the treatment of IDH1-mutated AML. This review will focus on mIDH1 as a therapeutic target and provide an update on IDH1 mutant inhibitors in development and clinical trials.

Deuterium magnetic resonance spectroscopy enables noninvasive metabolic imaging of tumor burden and response to therapy in low-grade gliomas.

BACKGROUND: The alternative lengthening of telomeres (ALT) pathway is essential for tumor proliferation in astrocytomas. The goal of this study was to identify metabolic alterations linked to the ALT pathway that can be exploited for noninvasive magnetic resonance spectroscopy (MRS)-based imaging of astrocytomas in vivo. METHODS: Genetic and pharmacological methods were used to dissect the association between the ALT pathway and glucose metabolism in genetically engineered and patient-derived astrocytoma models. 2H-MRS was used for noninvasive imaging of ALT-linked modulation of glycolytic flux in mice bearing orthotopic astrocytomas in vivo. RESULTS: The ALT pathway was associated with higher activity of the rate-limiting glycolytic enzyme phosphofructokinase-1 and concomitantly elevated flux of glucose to lactate in astrocytoma cells. Silencing the ALT pathway or treating with the poly(ADP-ribose) polymerase inhibitor niraparib that induces telomeric fusion in ALT-dependent astrocytoma cells abrogated glycolytic flux. Importantly, this metabolic reprogramming could be non-invasively visualized by 2H-MRS. Lactate production from [6,6'-2H]-glucose was higher in ALT-dependent astrocytoma tumors relative to the normal brain in vivo. Furthermore, treatment of orthotopic astrocytoma-bearing mice with niraparib reduced lactate production from [6,6'-2H]-glucose at early timepoints when alterations in tumor volume could not be detected by anatomical imaging, pointing to the ability of [6,6'-2H]-glucose to report on pseudoprogression in vivo. CONCLUSIONS: We have mechanistically linked the ALT pathway to elevated glycolytic flux and demonstrated the ability of [6,6'-2H]-glucose to non-invasively assess tumor burden and response to therapy in astrocytomas. Our findings point to a novel, clinically translatable method for metabolic imaging of astrocytoma patients.

The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma.

Objectives: To non-invasively predict the coexistence of isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas using apparent diffusion coefficient (ADC) histogram and direct ADC measurements and compare the diagnostic performances of the two methods. Materials and methods: A total of 118 patients with adult-type diffuse glioma who underwent preoperative brain magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) were included in this retrospective study. The patient group included 40 patients with coexisting IDH mutation and MGMT promoter methylation (IDHmut/MGMTmet) and 78 patients with other molecular status, including 32 patients with IDH wildtype and MGMT promoter methylation (IDHwt/MGMTmet), one patient with IDH mutation and unmethylated MGMT promoter (IDHmut/MGMTunmet), and 45 patients with IDH wildtype and unmethylated MGMT promoter (IDHwt/MGMTunmet). ADC histogram parameters of gliomas were extracted by delineating the region of interest (ROI) in solid components of tumors. The minimum and mean ADC of direct ADC measurements were calculated by placing three rounded or elliptic ROIs in solid components of gliomas. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performances of the two methods. Results: The 10th percentile, median, mean, root mean squared, 90th percentile, skewness, kurtosis, and minimum of ADC histogram analysis and minimum and mean ADC of direct measurements were significantly different between IDHmut/MGMTmet and the other glioma group (P < 0.001 to P = 0.003). In terms of single factors, 10th percentile of ADC histogram analysis had the best diagnostic efficiency (AUC = 0.860), followed by mean ADC obtained by direct measurements (AUC = 0.844). The logistic regression model combining ADC histogram parameters and direct measurements had the best diagnostic efficiency (AUC = 0.938), followed by the logistic regression model combining the ADC histogram parameters with statistically significant difference (AUC = 0.916) and the logistic regression model combining minimum ADC and mean ADC (AUC = 0.851). Conclusion: Both ADC histogram analysis and direct measurements have potential value in predicting the coexistence of IDHmut and MGMTmet in adult-type diffuse glioma. The diagnostic performance of ADC histogram analysis was better than that of direct ADC measurements. The combination of the two methods showed the best diagnostic performance.

Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI.

RATIONALE AND OBJECTIVES: We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs). MATERIALS AND METHODS: A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance. RESULTS: The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001). CONCLUSION: The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.