Noninvasive assessment of isocitrate dehydrogenase mutation status in cerebral gliomas by magnetic resonance spectroscopy in a clinical setting

7 Tesla magnetic resonance spectroscopic imaging predicting IDH status and glioma grading

INTRODUCTION

With the application of high-resolution 3D 7 Tesla Magnetic Resonance Spectroscopy Imaging (MRSI) in high-grade gliomas, we previously identified intratumoral metabolic heterogeneities. In this study, we evaluated the potential of 3D 7 T-MRSI for the preoperative noninvasive classification of glioma grade and isocitrate dehydrogenase (IDH) status. We demonstrated that IDH mutation and glioma grade are detectable by ultra-high field (UHF) MRI. This technique might potentially optimize the perioperative management of glioma patients.

METHODS

We prospectively included 36 patients with WHO 2021 grade 2-4 gliomas (20 IDH mutated, 16 IDH wildtype). Our 7 T 3D MRSI sequence provided high-resolution metabolic maps (e.g., choline, creatine, glutamine, and glycine) of these patients' brains. We employed multivariate random forest and support vector machine models to voxels within a tumor segmentation, for classification of glioma grade and IDH mutation status.

RESULTS

Random forest analysis yielded an area under the curve (AUC) of 0.86 for multivariate IDH classification based on metabolic ratios. We distinguished high- and low-grade tumors by total choline (tCho) / total N-acetyl-aspartate (tNAA) ratio difference, yielding an AUC of 0.99. Tumor categorization based on other measured metabolic ratios provided comparable accuracy.

CONCLUSIONS

We successfully classified IDH mutation status and high- versus low-grade gliomas preoperatively based on 7 T MRSI and clinical tumor segmentation. With this approach, we demonstrated imaging based tumor marker predictions at least as accurate as comparable studies, highlighting the potential application of MRSI for pre-operative tumor classifications.

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.

Differential metabolic alterations in IDH1 mutant vs. wildtype glioma cells promote epileptogenesis through distinctive mechanisms

Glioma-related epilepsy (GRE) is a hallmark clinical presentation of gliomas with significant impacts on patient quality of life. The current standard of care for seizure management is comprised of anti-seizure medications (ASMs) and surgical resection. Seizures in glioma patients are often drug-resistant and can often recur after surgery despite total tumor resection. Therefore, current research is focused on the pro-epileptic pathological changes occurring in tumor cells and the peritumoral environment. One important contribution to seizures in GRE patients is metabolic reprogramming in tumor and surrounding cells. This is most evident by the significantly heightened seizure rate in patients with isocitrate dehydrogenase mutated (IDHmut) tumors compared to patients with IDH wildtype (IDHwt) gliomas. To gain further insight into glioma metabolism in epileptogenesis, this review compares the metabolic changes inherent to IDHmut vs. IDHwt tumors and describes the pro-epileptic effects these changes have on both the tumor cells and the peritumoral environment. Understanding alterations in glioma metabolism can help to uncover novel therapeutic interventions for seizure management in GRE 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.

Predictive IDH Genotyping Based on the Evaluation of Spatial Metabolic Heterogeneity by Compartmental Uptake Characteristics in Preoperative Glioma Using 18F-FET PET

Molecular markers are of increasing importance for classifying, treating, and determining the prognosis for central nervous system tumors. Isocitrate dehydrogenase (IDH) is a critical regulator of glucose and amino acid metabolism. Our objective was to investigate metabolic reprogramming of glioma using compartmental uptake (CU) characteristics in O-(2-18F-fluoroethyl)-l-tyrosine (FET) PET and to evaluate its diagnostic potential for IDH genotyping. Methods: Between 2017 and 2022, patients with confirmed glioma were preoperatively investigated using static 18F-FET PET. Metabolic tumor volume (MTV), MTV for 60%-100% uptake (MTV60), and T2-weighted and contrast-enhancing lesion volumes were automatically segmented using U-Net neural architecture and isocontouring. Volume intersections were determined using the Dice coefficient. Uptake characteristics were determined for metabolically defined compartments (central [80%-100%] and peripheral [60%-75%] areas of 18F-FET uptake). CU ratio was defined as the fraction between the peripheral and central compartments. Mean target-to-background ratio was calculated. Comparisons were performed using parametric and nonparametric tests. Receiver-operating-characteristic curves, regression, and correlation were used for statistical analysis. Results: In total, 52 participants (male, 27, female, 25; mean age ± SD, 51 ± 16 y) were evaluated. MTV60 was greater and distinct from contrast-enhancing lesion volume (P = 0.046). IDH-mutated tumors presented a greater volumetric CU ratio and SUV CU ratio than IDH wild-type tumors (P < 0.05). Volumetric CU ratio determined IDH genotype with excellent diagnostic performance (area under the curve [AUC], 0.88; P < 0.001) at more than 5.49 (sensitivity, 86%, specificity, 90%), because IDH-mutated tumors presented a greater peripheral metabolic compartment than IDH wild-type tumors (P = 0.045). MTV60 and MTV were not suitable for IDH classification (P > 0.05). SUV CU ratio (AUC, 0.72; P = 0.005) and target-to-background ratio (AUC, 0.68; P = 0.016) achieved modest diagnostic performance-inferior to the volumetric CU ratio. Furthermore, the classification of loss of heterozygosity of chromosomes 1p and 19q (AUC, 0.75; P = 0.019), MGMT promoter methylation (AUC, 0.70; P = 0.011), and ATRX loss (AUC, 0.73; P = 0.004) by amino acid PET was evaluated. Conclusion: We proposed parametric 18F-FET PET as a noninvasive metabolic biomarker for the evaluation of CU characteristics, which differentiated IDH genotype with excellent diagnostic performance, establishing a critical association between spatial metabolic heterogeneity, mitochondrial tricarboxylic acid cycle, and genomic features with critical implications for clinical management and the diagnostic workup of patients with central nervous system cancer.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

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.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.

Spectroscopic imaging of D-2-hydroxyglutarate and other metabolites in pre-surgical patients with IDH-mutant lower-grade gliomas

Purpose

Prognostically favorable IDH-mutant gliomas are known to produce oncometabolite D-2-hydroxyglutarate (2HG). In this study, we investigated metabolite-based features of patients with grade 2 and 3 glioma using 2HG-specific in vivo MR spectroscopy, to determine their relationship with image-guided tissue pathology and predictive role in progression-free survival (PFS).

Methods

Forty-five patients received pre-operative MRIs that included 3-D spectroscopy optimized for 2HG detection. Spectral data were reconstructed and quantified to compare metabolite levels according to molecular pathology (IDH1R132H, 1p/19q, and p53); glioma grade; histological subtype; and T2 lesion versus normal-appearing white matter (NAWM) ROIs. Levels of 2HG were correlated with other metabolites and pathological parameters (cellularity, MIB-1) from image-guided tissue samples using Pearson’s correlation test. Metabolites predictive of PFS were evaluated with Cox proportional hazards models.

Results

Quantifiable levels of 2HG in 39/42 (93%) IDH+ and 1/3 (33%) IDH– patients indicated a 91.1% apparent detection accuracy. Myo-inositol/total choline (tCho) showed reduced values in astrocytic (1p/19q-wildtype), p53-mutant, and grade 3 (vs. 2) IDH-mutant gliomas (p < 0.05), all of which exhibited higher proportions of astrocytomas. Compared to NAWM, T2 lesions displayed elevated 2HG+ γ-aminobutyric acid (GABA)/total creatine (tCr) (p < 0.001); reduced glutamate/tCr (p < 0.001); increased myo-inositol/tCr (p < 0.001); and higher tCho/tCr (p < 0.001). Levels of 2HG at sampled tissue locations were significantly associated with tCho (R = 0.62; p = 0.002), total NAA (R = − 0.61; p = 0.002) and cellularity (R = 0.37; p = 0.04) but not MIB-1. Increasing levels of 2HG/tCr (p = 0.0007, HR 5.594) and thresholding (≥ 0.905, median value; p = 0.02) predicted adverse PFS.

Conclusion

In vivo 2HG detection can reasonably be achieved on clinical scanners and increased levels may signal adverse PFS.

Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics

Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Diagnostic performance of edited 2HG MR spectroscopy of central glioma in the clinical environment

OBJECTIVE

Oncometabolite D-2-hydroxyglutarate (2HG) is pooled in isocitrate dehydrogenase (IDH)-mutant glioma cells. Detecting 2HG by MR spectroscopy (MRS) has been proven viable in the last decade but has not entirely found its way into the clinical routine. This study aimed to explore the adoption of 2HG MRS while acknowledging factors that influence its performance in the clinical environment.

METHODS

Thirty-nine MR spectra were acquired and reported prospectively in patients with suspected glioma using a 3 T system with Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS) sequence utilizing averaged free induction decay (FID) signals. Postprocessing and evaluation of spectra were performed with jMRUI and LCModel. 2HG concentration estimates, 2HG/Cr ratio, together with quality measures, including Cramér-Rao lower bounds (CRLBs), full-width at half-maximum (FWHM) values, and signal-to-noise ratio (SNR) were calculated using LCModel. Immunohistochemistry and genomic analysis results used as a ground truth were available for 15 patients.

RESULTS

The threshold for test positivity was set according to the ROC curve at 1 mM. Calculated sensitivity was 57.14% (95% CI 0.20-0.88), specificity 87.5% (95% CI 0.46-0.99), positive predictive value 80%, and negative predictive value 70%. Overall diagnostic accuracy was 73.33% (95% CI 0.45-0.92). The 2HG/Cr ratio with the cutoff value 0.085 significantly improved sensitivity and overall diagnostic accuracy [85.71%, 95% CI 0.42-1.00 and 86.67%, (95% CI 0.60-0.98), respectively].

CONCLUSION

Multiple factors compromising spectral quality in the clinical adoption of edited 2HG MRS resulted in diminished sensitivity but clinically acceptable specificity. Furthermore, the 2HG/Cr ratio performs better than the sole 2HG concentration estimate in the pre-operative setting.

[Radiological biomarkers of brain gliomas]

The most important objective of modern neuroimaging is comparison of data on genotype and phenotype of brain gliomas. Radiogenomics as a new branch of science is devoted to searching for such relationships based on MRI and PET/CT parameters. The 2021 WHO classification of tumors of the central nervous system poses the most important tasks for physicians in assessment of biological behavior of tumors and their response to combined treatment. The review demonstrates the possibilities and prospects of preoperative MRI and PET/CT with amino acids in assessing the genetic profile of brain gliomas.

Spectral fitting strategy to overcome the overlap between 2-hydroxyglutarate and lipid resonances at 2.25 ppm

PURPOSE

1 H MRS provides a noninvasive tool for identifying mutations in isocitrate dehydrogenase (IDH). Quantification of the prominent 2-hydroxyglutarate (2HG) resonance at 2.25 ppm is often confounded by the lipid resonance at the same frequency in tumors with elevated lipids. We propose a new spectral fitting approach to separate these overlapped signals, therefore, improving 2HG evaluation.

METHODS

TE 97 ms PRESS was acquired at 3T from 42 glioma patients. New lipid basis sets were created, in which the small lipid 2.25-ppm signal strength was preset with reference to the lipid signal at 0.9 ppm, incorporating published fat relaxation data. LCModel fitting using the new lipid bases (Fitting method 2) was conducted along with fitting using the LCModel built-in lipid basis set (Fitting method 1), in which the lipid 2.25-ppm signal is assessed with reference to the lipid 1.3-ppm signal. In-house basis spectra of low-molecular-weight metabolites were used in both fitting methods.

RESULTS

Fitting method 2 showed marked improvement in identifying IDH mutational status compared with Fitting method 1. 2HG estimates from Fitting method 2 were overall smaller than those from Fitting method 1, which was because of differential assignment of the signal at 2.25 ppm to lipids. In receiver operating characteristic analysis, Fitting method 2 provided a complete distinction between IDH mutation and wild-type whereas Fitting method 1 did not.

CONCLUSION

The data suggest that 1 H MR spectral fitting using the new lipid basis set provides a robust fitting strategy that improves 2HG evaluation in brain tumors with elevated lipids.

D-2-Hydroxyglutarate in Glioma Biology

Isocitrate dehydrogenase (IDH) mutations are common genetic abnormalities in glioma, which result in the accumulation of an "oncometabolite", D-2-hydroxyglutarate (D-2-HG). Abnormally elevated D-2-HG levels result in a distinctive pattern in cancer biology, through competitively inhibiting α-ketoglutarate (α-KG)/Fe(II)-dependent dioxgenases (α-KGDDs). Recent studies have revealed that D-2-HG affects DNA/histone methylation, hypoxia signaling, DNA repair, and redox homeostasis, which impacts the oncogenesis of IDH-mutated cancers. In this review, we will discuss the current understanding of D-2-HG in cancer biology, as well as the emerging opportunities in therapeutics in IDH-mutated glioma.

Joint application of biochemical markers and imaging techniques in the accurate and early detection of glioblastoma

Glioblastoma is a primary brain tumor with the most metastatic effect in adults. Despite the wide range of multidimensional treatments, tumor heterogeneity is one of the main causes of tumor spread and gives great complexity to diagnostic and therapeutic methods. Therefore, featuring noble noninvasive prognostic methods that are focused on glioblastoma heterogeneity is perceived as an urgent need. Imaging neuro-oncological biomarkers including MGMT (O⁶-methylguanine-DNA methyltransferase) promoter methylation status, tumor grade along with other tumor characteristics and demographic features (e.g., age) are commonly referred to during diagnostic, therapeutic and prognostic processes. Therefore, the use of new noninvasive prognostic methods focused on glioblastoma heterogeneity is considered an urgent need. Some neuronal biomarkers, including the promoter methylation status of the promoter MGMT, the characteristics and grade of the tumor, along with the patient's demographics (such as age and sex) are involved in diagnosis, treatment, and prognosis. Among the wide array of imaging techniques, magnetic resonance imaging combined with the more physiologically detailed technique of H-magnetic resonance spectroscopy can be useful in diagnosing neurological cancer patients. In addition, intracranial tumor qualitative analysis and sometimes tumor biopsies help in accurate diagnosis. This review summarizes the evidence for biochemical biomarkers being a reliable biomarker in the early detection and disease management in GBM. Moreover, we highlight the correlation between Imaging techniques and biochemical biomarkers and ask whether they can be combined.

From Metabolism to Genetics and Vice Versa: The Rising Role of Oncometabolites in Cancer Development and Therapy

Over the last decades, the study of cancer metabolism has returned to the forefront of cancer research and challenged the role of genetics in the understanding of cancer development. One of the major impulses of this new trend came from the discovery of oncometabolites, metabolic intermediates whose abnormal cellular accumulation triggers oncogenic signalling and tumorigenesis. These findings have led to reconsideration and support for the long-forgotten hypothesis of Warburg of altered metabolism as oncogenic driver of cancer and started a novel paradigm whereby mitochondrial metabolites play a pivotal role in malignant transformation. In this review, we describe the evolution of the cancer metabolism research from a historical perspective up to the oncometabolites discovery that spawned the new vision of cancer as a metabolic disease. The oncometabolites' mechanisms of cellular transformation and their contribution to the development of new targeted cancer therapies together with their drawbacks are further reviewed and discussed.

The role of 2-hydroxyglutarate magnetic resonance spectroscopy for the determination of isocitrate dehydrogenase status in lower grade gliomas versus glioblastoma: a systematic review and meta-analysis of diagnostic test accuracy

PURPOSE

Magnetic resonance spectroscopy (MRS) provides a non-invasive means of determining isocitrate dehydrogenase (IDH) status. Determination of 2-hydroxyglutarate (2-HG) presence through MRS is a means of determining IDH status; however, differences may be seen by grade. The goal of this paper is to perform a diagnostic test accuracy (DTA) meta-analysis on 2-HG MRS for IDH status in both lower-grade glioma (LGG) and glioblastoma (GBM) in preoperative patients.

METHODS

A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy guidelines. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2. The search was up to date as of 09/02/2021. Nine English-language journal articles were included.

RESULTS

The meta-analysis found a pooled sensitivity of 93% (95% CI 58-99%) and specificity of 84% (95% CI 51-96%) for LGG (n= 181). For GBM (n= 77), the pooled sensitivity was 84% (95% CI 25.0-99%) and the specificity was 97% (95% CI 43-100%).

CONCLUSION

2-HG MRS shows promise as a non-invasive means of determining IDH status, with specificity higher for GBM and sensitivity higher for LGG. The wide confidence intervals are notable, however, in particular related to the small number of IDH-mutant GBM studied. Diagnostic heterogeneity was particularly present for LGG, and the hierarchical summary receiver operator curves showed poor predictive accuracy in both groups. For more conclusive results, diagnostic test accuracy statistics need to be quantified with larger studies and more deliberate study design.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Neuro-Oncology and Radiogenomics: Time to Integrate?

Radiogenomics aims to predict genetic markers based on imaging features. The critical importance of molecular markers in the diagnosis and management of intracranial gliomas has led to a rapid growth in radiogenomics research, with progressively increasing complexity. Despite the advances in the techniques being examined, there has been little translation into the clinical domain. This has resulted in a growing disconnect between cutting-edge research and assimilation into clinical practice, though the fundamental goal is for these techniques to improve patient care. The goal of this review, therefore, is to discuss possible clinical scenarios in which the addition of radiogenomics may aid patient management. This includes facilitating patient counseling, determining optimal patient management when complete molecular characterization is not possible, reclassifying tumors, and overcoming some of the limitations of histologic assessment. The review also discusses considerations for selecting relevant radiogenomic features based on the clinical setting.

Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer

In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign

PURPOSE

The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

METHODS

Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10^-3mm²/s (V_ADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

RESULTS

V_ADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V_ADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V_ADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

CONCLUSION

Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V_ADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. V_ADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

Comparative Value of 2-Hydroxyglutarate-to-Lipid and Lactate Ratio versus 2-Hydroxyglutarate Concentration on MR Spectroscopic Images for Predicting Isocitrate Dehydrogenase Mutation Status in Gliomas

Purpose

To compare the ability of 2-hydroxyglutarate (2HG)-to-lipid and lactate (2HG/[lipid + lactate]) ratio with the ability of 2HG concentration alone to predict the isocitrate dehydrogenase (IDH) mutation status in patients with glioma.

Materials and Methods

In this retrospective study, consecutive patients with histopathologically proven glioma were enrolled between July 2016 and February 2019. A total of 79 patients were enrolled (mean age, 44 years; 49 men). The 2HG concentration and other MR spectroscopic parameters were measured by single-voxel point-resolved spectroscopy before surgery. The diagnostic performance of the 2HG concentration and 2HG/(lipid + lactate) ratio were calculated. Internal validation was assessed by the bootstrap approach with 1000 bootstrap resamples. Differences in the predictive accuracy of 2HG/(lipid + lactate) ratio and 2HG concentration were determined by calculating the integrated discrimination improvement. The diagnostic accuracy (sensitivity, specificity, and area under the receiver operating characteristic curve [AUC]) of these measures was also compared separately in patients with glioblastomas and patients with lower-grade gliomas.

Results

Of the 79 enrolled patients, 28 had IDH mutations and 51 had wild-type IDH. The sensitivity, specificity, and AUC of 2HG concentration for predicting IDH-mutant gliomas were 89% (25 of 28), 67% (34 of 51), and 0.80 (95% confidence interval [CI]: 0.70, 0.88; C statistic, 0.80), respectively. The sensitivity, specificity, and AUC of the 2HG/(lipid + lactate) ratio for predicting IDH-mutant gliomas were 79% (22 of 28), 92% (47 of 51), and 0.90 (95% CI: 0.81, 0.96; C statistics, 0.90), respectively. The optimal cutoff value for the 2HG/(lipid + lactate) ratio was 0.63. The 2HG/(lipid + lactate) ratio was significantly better for predicting IDH mutation status than the 2HG concentration alone (P < .01). In glioblastoma, the 2HG/(lipid + lactate) ratio was also better for predicting IDH mutations than the 2HG concentration alone, with borderline significance (P = .052). In lower-grade glioma, the 2HG/(lipid + lactate) ratio and the 2HG concentration showed comparable diagnostic performance (P = .72).

Conclusion

The 2HG/(lipid + lactate) ratio is more accurate for predicting IDH mutation status in patients with glioma than the 2HG concentration alone.Keywords: Brain/Brain Stem, CNS, MR-Imaging, MR-Spectroscopy, Neoplasms-Primary, Neuro-Oncology© RSNA, 2020.

Correction to: Advanced imaging in adult diffusely infiltrating low-grade gliomas

The original article [1] contains errors in Table 1 in rows ktrans and Ve; the correct version of Table 1 can be viewed in this Correction article.

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon.Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons.Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas

BACKGROUND

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network.

METHODS

Multiparametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive and The Cancer Genome Atlas. Two separate networks were developed, including a T2w image-only network (T2-net) and a multicontrast (T2w, fluid attenuated inversion recovery, and T1 postcontrast) network (TS-net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D Dense-UNets. Three-fold cross-validation was performed to generalize the networks' performance. Receiver operating characteristic analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy.

RESULTS

T2-net demonstrated a mean cross-validation accuracy of 97.14% ± 0.04 in predicting IDH mutation status, with a sensitivity of 0.97 ± 0.03, specificity of 0.98 ± 0.01, and an area under the curve (AUC) of 0.98 ± 0.01. TS-net achieved a mean cross-validation accuracy of 97.12% ± 0.09, with a sensitivity of 0.98 ± 0.02, specificity of 0.97 ± 0.001, and an AUC of 0.99 ± 0.01. The mean whole tumor segmentation Dice scores were 0.85 ± 0.009 for T2-net and 0.89 ± 0.006 for TS-net.

CONCLUSION

We demonstrate high IDH classification accuracy using only T2-weighted MR images. This represents an important milestone toward clinical translation.

Intraoperative assessment of isocitrate dehydrogenase mutation status in human gliomas using desorption electrospray ionization-mass spectrometry

OBJECTIVE

The authors describe a rapid intraoperative ambient ionization mass spectrometry (MS) method for determining isocitrate dehydrogenase (IDH) mutation status from glioma tissue biopsies. This method offers new glioma management options and may impact extent of resection goals. Assessment of the IDH mutation is key for accurate glioma diagnosis, particularly for differentiating diffuse glioma from other neoplastic and reactive inflammatory conditions, a challenge for the standard intraoperative diagnostic consultation that relies solely on morphology.

METHODS

Banked glioma specimens (n = 37) were analyzed by desorption electrospray ionization-MS (DESI-MS) to develop a diagnostic method to detect the known altered oncometabolite in IDH-mutant gliomas, 2-hydroxyglutarate (2HG). The method was used intraoperatively to analyze tissue smears obtained from glioma patients undergoing resection and to rapidly diagnose IDH mutation status (< 5 minutes). Fifty-one tumor core biopsies from 25 patients (14 wild type [WT] and 11 mutant) were examined and data were analyzed using analysis of variance and receiver operating characteristic curve analysis.

RESULTS

The optimized DESI-MS method discriminated between IDH-WT and IDH-mutant gliomas, with an average sensitivity and specificity of 100%. The average normalized DESI-MS 2HG signal was an order of magnitude higher in IDH-mutant glioma than in IDH-WT glioma. The DESI 2HG signal intensities correlated with independently measured 2HG concentrations (R2 = 0.98). In 1 case, an IDH1 R132H-mutant glioma was misdiagnosed as a demyelinating condition by frozen section histology during the intraoperative consultation, and no resection was performed pending the final pathology report. A second craniotomy and tumor resection was performed after the final pathology provided a diagnosis most consistent with an IDH-mutant glioblastoma. During the second craniotomy, high levels of 2HG in the tumor core biopsies were detected.

CONCLUSIONS

This study demonstrates the capability to differentiate rapidly between IDH-mutant gliomas and IDH-WT conditions by DESI-MS during tumor resection. DESI-MS analysis of tissue smears is simple and can be easily integrated into the standard intraoperative pathology consultation. This approach may aid in solving differential diagnosis problems associated with low-grade gliomas and could influence intraoperative decisions regarding extent of resection, ultimately improving patient outcome. Research is ongoing to expand the patient cohort, systematically validate the DESI-MS method, and investigate the relationships between 2HG and tumor heterogeneity.

Advanced imaging in adult diffusely infiltrating low-grade gliomas

The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADCmin), angiogenesis (rCBVmax), capillary permeability (Ktrans), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADCmin, high rCBVmax, and Ktrans values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBVmax values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.

In vivo 2-hydroxyglutarate-proton magnetic resonance spectroscopy (3 T, PRESS technique) in treatment-naïve suspect lower-grade gliomas: feasibility and accuracy in a clinical setting

Isocitrate dehydrogenase 1/2 (IDH1/2) mutations are often detected in lower-grade gliomas (LGG) and result into 2-hydroxyglutarate (2HG) synthesis. Prior studies showed that 2HG can be detected in vivo using magnetic resonance spectroscopy (MRS), but its accuracy and translational impact are still under investigation.

PURPOSE

To investigate the clinical feasibility of MRS for in vivo detection and quantification of 2HG on consecutive treatment-naïve suspect LGG patients and to compare MRS accuracy with tissue IDH1/2 analysis.

METHODS

MRS spectra at 3 T were acquired with 1H-MRS single-voxel PRESS 2HG-tailored sequences with TE 30 (group 1) or TE 97 (groups 2A and B). Voxel sizes were 1.5 × 1.5 × 1.5 cm³ for group 1 (n = 13) and group 2A (n = 14) and 2 × 2 × 2 cm³ for group 2B (n = 32). Multiple metabolites' concentrations were analyzed with LCModel. Tumors were assessed for IDH status and main molecular markers. 2HG levels in urine/blood were measured by liquid chromatography-mass spectrometry.

RESULTS

The larger voxel TE 97 sequence resulted in highest specificity (100%), sensitivity (79%), and accuracy (87%). Urine and blood 2HG did not result predictive.

CONCLUSION

Our data confirm that 2 × 2 × 2-cm³ voxel TE 97 MRS shows high accuracy for 2HG detection, with good sensitivity and 100% specificity in distinguishing IDH mutant gliomas. Main limits of the technique are small tumor volume and low cellularity. Integrating 2HG-MRS with other metabolites may help non-invasive diagnosis of glioma, prognostic assessment, and treatment planning in clinical setting.

2-Hydroxyglutarate MR spectroscopy for prediction of isocitrate dehydrogenase mutant glioma: a systemic review and meta-analysis using individual patient data

Background

Noninvasive and accurate modality to predict isocitrate dehydrogenase (IDH) mutant glioma may have great potential in routine clinical practice. We aimed to investigate the diagnostic performance of 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) for prediction of IDH mutant glioma and provide an optimal cutoff value for 2HG.

Methods

A systematic literature search of Ovid-MEDLINE and EMBASE was performed to identify original articles investigating the diagnostic performance of 2HG MRS up to March 20, 2018. Pooled sensitivity and specificity were calculated using a bivariate random-effects model. Subgroup analysis and meta-regression were performed to explain heterogeneity effects. An optimal cutoff value for 2HG was calculated from studies providing individual patient data.

Results

Fourteen original articles with 460 patients were included. The pooled sensitivity and specificity for the diagnostic performance of 2HG MRS for prediction of IDH mutant glioma were 95% (95% CI, 85-98%) and 91% (95% CI, 83-96%), respectively. The Higgins I2 statistic demonstrated that heterogeneity was present in the sensitivity (I2 = 50.69%), but not in the specificity (I2 = 30.37%). In the meta-regression, echo time (TE) was associated with study heterogeneity. Among the studies using point-resolved spectroscopy (PRESS), a long TE (97 ms) resulted in higher sensitivity (92%) and specificity (97%) than a short TE (30-35 ms; sensitivity of 90%, specificity of 88%; P < 0.01). The optimal 2HG cutoff value of 2HG using individual patient data was 1.76 mM.

Conclusion

2HG MRS demonstrated excellent specificity for prediction of IDH mutant glioma, with TE being associated with heterogeneity in the sensitivity.

Key Points

1. HG MRS has excellent diagnostic performance in the prediction of IDH mutant glioma. 2. The pooled sensitivity was 95% and the pooled specificity was 91%. 3. Echo time was associated with study heterogeneity in the meta-regression.

Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy

Background

Mutations in the isocitrate dehydrogenase (IDH) enzyme affect 40% of gliomas and represent a major diagnostic and prognostic marker. The goals of this study were to evaluate the performance of noninvasive magnetic resonance spectroscopy (MRS) methods to determine the IDH status of patients with brain gliomas through detection of the oncometabolite 2-hydroxyglutarate (2HG) and to compare performance of these methods with DNA sequencing and tissue 2HG analysis.

Methods

Twenty-four subjects with suspected diagnosis of low-grade glioma were included prospectively in the study. For all subjects, MRS data were acquired at 3T using 2 MRS methods, edited MRS using Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS) sequence and a PRESS sequence optimized for 2HG detection, using a volume of interest larger than 6 mL. IDH mutational status was determined by a combination of automated immunohistochemical analysis and Sanger sequencing. Levels of 2HG in tissue samples measured by gas chromatography-mass spectrometry were compared with those estimated by MRS.

Results

Edited MRS provided 100% specificity and 100% sensitivity in the detection of 2HG. The 2HG levels estimated by this technique were in line with those derived from tissue samples. Optimized PRESS provided lower performance, in agreement with previous findings.

Conclusions

Our results suggest that edited MRS is one of the most reliable tools to predict IDH mutation noninvasively, showing high sensitivity and specificity for 2HG detection. Integrating edited MRS in clinical practice may be highly beneficial for noninvasive diagnosis of glioma, prognostic assessment, and treatment planning.

Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning

Isocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose an automated pipeline for noninvasively predicting IDH status using deep learning and T2-weighted (T2w) magnetic resonance (MR) images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MR images and genomic data were obtained from The Cancer Imaging Archive dataset for 260 subjects (120 high-grade and 140 low-grade gliomas). A fully automated two-dimensional densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects using fivefold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated, and IDH wild type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

Molecular Subtype Classification in Lower-Grade Glioma with Accelerated DTI

BACKGROUND AND PURPOSE

Image-based classification of lower-grade glioma molecular subtypes has substantial prognostic value. Diffusion tensor imaging has shown promise in lower-grade glioma subtyping but currently requires lengthy, nonstandard acquisitions. Our goal was to investigate lower-grade glioma classification using a machine learning technique that estimates fractional anisotropy from accelerated diffusion MR imaging scans containing only 3 diffusion-encoding directions.

MATERIALS AND METHODS

Patients with lower-grade gliomas (n = 41) (World Health Organization grades II and III) with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status were imaged preoperatively with DTI. Whole-tumor volumes were autodelineated using conventional anatomic MR imaging sequences. In addition to conventional ADC and fractional anisotropy reconstructions, fractional anisotropy estimates were computed from 3-direction DTI subsets using DiffNet, a neural network that directly computes fractional anisotropy from raw DTI data. Differences in whole-tumor ADC, fractional anisotropy, and estimated fractional anisotropy were assessed between IDH-wild-type and IDH-mutant lower-grade gliomas with and without 1p/19q codeletion. Multivariate classification models were developed using whole-tumor histogram and texture features from ADC, ADC + fractional anisotropy, and ADC + estimated fractional anisotropy to identify the added value provided by fractional anisotropy and estimated fractional anisotropy.

RESULTS

ADC (P = .008), fractional anisotropy (P < .001), and estimated fractional anisotropy (P < .001) significantly differed between IDH-wild-type and IDH-mutant lower-grade gliomas. ADC (P < .001) significantly differed between IDH-mutant gliomas with and without codeletion. ADC-only multivariate classification predicted IDH mutation status with an area under the curve of 0.81 and codeletion status with an area under the curve of 0.83. Performance improved to area under the curve = 0.90/0.94 for the ADC + fractional anisotropy classification and to area under the curve = 0.89/0.89 for the ADC + estimated fractional anisotropy classification.

CONCLUSIONS

Fractional anisotropy estimates made from accelerated 3-direction DTI scans add value in classifying lower-grade glioma molecular status.

False-Positive Measurement at 2-Hydroxyglutarate MR Spectroscopy in Isocitrate Dehydrogenase Wild-Type Glioblastoma: A Multifactorial Analysis

Background Isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management. Measurement of 2-hydroxyglutarate (2HG) with MR spectroscopy has shown high pooled sensitivity, although false-positive results with MR spectroscopy have been reported. Purpose To investigate factors associated with false-positive 2HG measurements at MR spectroscopy in patients with IDH wild-type glioblastoma. Materials and Methods This retrospective study was approved by the institutional review board, and informed consent was waived. Consecutive patients with histopathologically confirmed pre- and posttreatment glioblastoma were evaluated between December 2017 and August 2018. Spectroscopy parameters, including 2HG measurements, were obtained with single-voxel point-resolved spectroscopy, and apparent diffusion coefficient (ADC) values were calculated. Necrosis was graded according to the proportion of necrosis within a volume of interest. Poisson regression analyses were performed to determine factors related to false-positive 2HG measurements. Results A total of 82 patients were included (mean age, 55 years ± 12 [standard deviation]; 40 men). The 2HG measurement showed a false-positive rate of 21% (17 of 82; 95% CI: 13%, 31%) in patients with IDH wild-type glioblastoma. Multivariable analysis revealed that necrosis (prevalence ratio [PR], 3.9; 95% CI: 1.6, 9.4; P = .01) and ADC value (PR, 0.1 × 10^-3 mm²/sec; 95% CI: [0.0, 0.7] × 10^-3 mm²/sec; P = .02) were associated with a greater false-positive rate for the 2HG measurement. Necrosis of more than 20% was associated with a higher rate of false-positive 2HG measurements (50%) than was necrosis of 20% or less (15%, P = .01). The 2HG false-positive rate was higher in patients with pretreatment glioblastoma (46%) than in those with posttreatment glioblastoma (14%, P < .01). Among 17 patients with false-positive findings, 15 (88%; 95% CI: 64%, 99%) had a lactate concentration of 2.0 mmol/L or higher, and 14 (82%, 95% CI: 57%, 96%) had a lactate concentration of 3.0 mmol/L or higher. Conclusion Necrosis and apparent diffusion coefficient were associated with false-positive measurements of 2-hydroxyglutarate at MR spectroscopy in patients with isocitrate dehydrogenase wild-type glioblastoma. © RSNA, 2019 Online supplemental material is available for this article.

Analysis of human gliomas by swab touch spray-mass spectrometry: applications to intraoperative assessment of surgical margins and presence of oncometabolites

Touch spray mass spectrometry using medical swabs is an ambient ionization technique (ionization of unprocessed sample in the open air) that has potential intraoperative application in quickly identifying the disease state of tissue and in better characterizing the resection margin. To explore this potential, we studied 29 human brain tumor specimens and obtained evidence that this technique can provide diagnostic molecular information that is relevant to brain cancer. Touch spray using medical swabs involves the physical sampling of tissue using a medical swab on a spatial scale of a few mm² with subsequent ionization occurring directly from the swab tip upon addition of solvent and application of a high voltage. Using a tertiary mixture of acetonitrile, N,N-dimethylformamide, and ethanol, membrane-derived phospholipids and oncometabolites are extracted from the tissue, incorporated into the sprayed microdroplets, vacuumed into the mass spectrometer, and characterized in the resulting mass spectra. The tumor cell load was assessed from the complex phospholipid pattern in the mass spectra and also separately by measurement of N-acetylaspartate. Mutation status of the isocitrate dehydrogenase gene was determined via detection of the oncometabolite 2-hydroxyglutarate. The lack of sample pretreatment makes touch spray mass spectrometry using medical swabs a feasible intraoperative strategy for rapid surgical assessment.

The clinical use of IDH1 and IDH2 mutations in gliomas

Introduction: Mutations in the genes isocitrate dehydrogenase (IDH) 1 and 2 have been reported in a limited number of tumors. In gliomas, IDH mutations are primarily detected in WHO grade II-III tumors and represent a major biomarker with diagnostic, prognostic, and predictive implications. The recent development of IDH inhibitors and vaccines suggests that the IDH mutation is also an appealing target for therapy. Areas covered: This review focuses on the role of IDH mutations in diffuse gliomas. Besides discussing their role in gliomagenesis, we will emphasize the role of IDH mutations in clinical practice as a diagnostic, prognostic and predictive biomarker, and as a potential therapeutic target. Noninvasive detection of the IDH mutation by means of liquid biopsy and MR spectroscopy will also be discussed. Expert commentary: While IDH mutation is a consolidated diagnostic and prognostic biomarker in clinical practice, its role in oncogenesis is far from being elucidated, and there are several pending issues. The routine use of noninvasive techniques for detection and monitoring of the IDH status remains challenging. Although the IDH mutation is a very early alteration in gliomagenesis, it may then be omitted during tumor progression. This observation has important implications when designing targeted clinical trials.

Combined texture analysis of diffusion-weighted imaging with conventional MRI for non-invasive assessment of IDH1 mutation in anaplastic gliomas

AIM

To examine whether texture analysis (TA) of diffusion-weighted imaging (DWI) combined with conventional magnetic resonance imaging (MRI) could non-invasively predict isocitrate dehydrogenase 1 (IDH1) mutational status in anaplastic gliomas.

MATERIALS AND METHODS

Fifty-two patients with histologically confirmed anaplastic glioma was reviewed retrospectively. Conventional MRI was evaluated using the Visually Accessible Rembrandt Images (VASARI) scoring system. TA of DWI based on the entire tumour volume was compared between IDH1-mutant and wild-type tumours by using unpaired Student's t-test. Receiver operating characteristic curve (ROC) and logistic regression were used to assess their diagnostic performance.

RESULTS

Significant statistical differences in VASARI features and TA of DWI were observed between IDH1-mutant and wild-type tumours (all p<0.05). Using multivariable logistic regression, the proportion of the tumour that was non-enhancing and the entropy of apparent diffusion coefficient (ADC) were found to possess higher prediction potential for IDH1 mutation with areas under the ROC curve (AUC) of 0.918 and 0.724, respectively. A combination of these for the identification of IDH1 mutations improved the AUC to 0.954, with a sensitivity and a specificity of 81% and 96%.

CONCLUSIONS

The combined assessment of the conventional MRI and TA of DWI were useful for predicting IDH1 mutation in anaplastic gliomas.

Prediction of Isocitrate Dehydrogenase Genotype in Brain Gliomas with MRI: Single-Shell versus Multishell Diffusion Models

Purpose The primary aim of this prospective observational study was to assess whether diffusion MRI metrics correlate with isocitrate dehydrogenase (IDH) status in grade II and III gliomas. A secondary aim was to investigate whether multishell acquisitions with advanced models such as neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging offer greater diagnostic accuracy than diffusion-tensor imaging (DTI). Materials and Methods Diffusion MRI (b = 700 and 2000 sec/mm²) was performed preoperatively in 192 consecutive participants (113 male and 79 female participants; mean age, 46.18 years; age range, 14-77 years) with grade II (n = 62), grade III (n = 58), or grade IV (n = 72) gliomas. DTI, diffusion kurtosis imaging, and NODDI metrics were measured in regions with or without hyperintensity on diffusion MR images and compared among groups defined according to IDH genotype, 1p/19q codeletion status, and tumor grade by using Mann-Whitney tests. Results In grade II and III IDH wild-type gliomas, the maximum fractional anisotropy, kurtosis anisotropy, and restriction fraction were significantly higher and the minimum mean diffusivity was significantly lower than in IDH-mutant gliomas (P = .011, P = .002, P = .044, and P = .027, respectively); areas under the receiver operating characteristic curve ranged from 0.72 to 0.76. In IDH wild-type gliomas, no difference among grades II, III, and IV was found. In IDH-mutant gliomas, no difference between those with and those without 1p/19q loss was found. Conclusion Diffusion MRI metrics showed correlation with isocitrate dehydrogenase status in grade II and III gliomas. Advanced diffusion MRI models did not add diagnostic accuracy, supporting the inclusion of a single-shell diffusion-tensor imaging acquisition in brain tumor imaging protocols. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis

OBJECTIVES

To evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma.

METHODS

A systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for 'glioma', 'IDH mutation' and 'MRI'. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported.

RESULTS

Twenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%-91%) and the summary specificity was 87% (95% CI, 78-92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

CONCLUSIONS

IDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities.

KEY POINTS

• IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value. • The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79-91%) and the summary specificity was 87% (95% CI, 78-92%). • In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91-100%)] was higher than the summary sensitivities of other imaging modalities.

The biology and mathematical modelling of glioma invasion: a review

Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics

BACKGROUND

Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging.

METHODS

Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes.

RESULTS

Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status.

CONCLUSION

Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.

IDH1 mutation in human glioma induces chemical alterations that are amenable to optical Raman spectroscopy

INTRODUCTION

Mutations in the isocytrate dehydrogenase 1 (IDH1) gene are early genetic events in glioma pathogenesis and cause profound metabolic changes. Because this genotype is found in virtually every tumor cell, therapies targeting mutant IDH1 protein are being developed. The intraoperative administration of those therapies would require fast technologies for the determination of IDH1 genotype. As of today, there is no such diagnostic test available. Recently, infrared spectroscopy was shown to bridge this gap. Here, we tested Raman spectroscopy for analysis of IDH1 genotype in glioma, which constitutes an alternative contact-free technique with the potential of being applicable in situ.

METHODS

Human glioma samples (n = 36) were obtained during surgery and cryosections were prepared. IDH1 mutations were assessed using DNA sequencing and 100 Raman spectra were obtained for each sample.

RESULTS

Analysis of Raman spectra revealed increased intensities in spectral bands related to DNA in IDH1 mutant glioma while bands assigned to molecular vibrations of lipids were significantly decreased. Moreover, intensities of Raman bands assigned to proteins differed in IDH1 mutant and IDH1 wild-type glioma, suggesting alterations in the protein profile. The selection of five bands (498, 826, 1003, 1174 and 1337 cm^-1) allowed the classification of Raman spectra according to IDH1 genotype with a correct rate of 89%.

CONCLUSION

Raman spectroscopy constitutes a simple, rapid and safe procedure for determination of the IDH1 mutation that shows great promise for clinically relevant in situ diagnostics.

Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response

Glioblastoma is the most aggressive adult primary brain tumor which is incurable despite intensive multimodal treatment. Inter- and intratumoral heterogeneity poses one of the biggest barriers in the diagnosis and treatment of glioblastoma, causing differences in treatment response and outcome. Noninvasive prognostic and predictive tests are highly needed to complement the current armamentarium. Noninvasive testing of glioblastoma uses multiple techniques that can capture the heterogeneity of glioblastoma. This set of diagnostic approaches comprises advanced MRI techniques, nuclear imaging, liquid biopsy, and new integrated approaches including radiogenomics and radiomics. New treatment options such as agents targeted at driver oncogenes and immunotherapy are currently being developed, but benefit for glioblastoma patients still has to be demonstrated. Understanding and unraveling tumor heterogeneity and microenvironment can help to create a treatment regime that is patient-tailored to these specific tumor characteristics. Improved noninvasive tests are crucial to this success. This review discusses multiple diagnostic approaches and their effect on predicting and monitoring treatment response in glioblastoma.