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

Predictive Evolution Factors of Incidentally Discovered Suspected Low-Grade Gliomas: Results From a Consecutive Series of 101 Patients

BACKGROUND

Incidentally discovered suspected diffuse low-grade gliomas (LGGs) on brain imaging pose a challenge to neurosurgeons. Modern surgical series of LGGs favor early prophylactic surgery with a maximal extent of resection. However, some nonevolutive lesions may mimic LGGs on magnetic resonance imaging (MRI).

OBJECTIVE

To determine objective criteria to advocate surgical resection of an incidentally discovered suspected LGG based upon MRI findings.

METHODS

The prospective cohort of patients referred to our institution for an incidental finding suggestive of LGG was retrospectively reviewed. Stable lesions underwent systematic serial MRI follow-up, while evolutive lesions underwent prophylactic surgery under awake conditions. Initial clinico-radiological features were compared between stable and evolutive lesions in order to determine predictive criteria of further evolution.

RESULTS

Among 101 patients referred for surgical resection of incidentally discovered suspected LGG in our center, 19 patients (18.8%) had nonevolutive MRI lesions after a mean follow-up of 46.9 ± 34.9 mo. Insular topography (P = .003), higher mean volume at discovery (19.2 vs 5.2 cm3, P < .001), and adjacent sulcal effacement (P = .001) were associated with evolutive lesions. Histopathological diagnosis of LGG was confirmed in all surgical cases.

CONCLUSION

Increasing volume is an effective predictor of LGG diagnosis in incidental MRI findings, as all patients who subsequently underwent surgery had confirmed histopathological diagnosis of diffuse glioma. Approximately 18.8% of incidental findings were stable over time. Insular topography, adjacent sulcal effacement, and volume greater than 4.5 cm3 were predictive of further radiological progression. These significant elements will help neurosurgeons to define personalized strategies in this complex setting of incidental discovery.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.

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.

Dual-layer detector spectral CT-a new supplementary method for preoperative evaluation of glioma

PURPOSE

To investigate the value of the iodine concentration (IC) measured by dual-layer detector spectral CT (DLDSCT) in evaluating the factors related to the treatment scheme and survival prognosis of patients with glioma.

METHODS

From 2018 to 2019, we prospectively collected the data of 99 patients with glioma. The degree of CT enhancement and the IC of low grade gliomas (LGGs, II), high grade gliomas (HGGs, III and IV), grade II and III gliomas, were compared. The predictive performance of the degree of CT enhancement and IC was examined via receiver operating characteristic (ROC) analysis. The correlations between IC and Ki-67 labeling index, isocitrate dehydrogenase (IDH) mutation, chromosome 1p/19q deletion status of the tumor were examined.

RESULTS

Both IC and the degree of CT enhancement of patients with HGG were significantly higher than those of patients with LGG (p < 0.001; χ² =41.707, p <  0.001); IC had large area under the ROC curve for diagnostic HGG (0.931; 95 % CI: 0.882-0.979; p <  0.001). The IC in the grade III gliomas was significantly higher than that in grade II gliomas (p < 0.001); IC had a large area under the ROC curve for diagnostic grade III gliomas (0.865; 95 % CI: 0.779-0.952; p <  0.001). There was a significant positive correlation between IC and Ki-67 LI (r = 0.679; p < 0.001).

CONCLUSIONS

The DLDSCT technology can be used as a supplementary method to provide more information for preoperative grading of the gliomas and the prognosis assessment of the patients.

Analyzing magnetic resonance imaging data from glioma patients using deep learning

The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.

The Histopathologic and Radiologic Features of T2-FLAIR Mismatch Sign in IDH-Mutant 1p/19q Non-codeleted Astrocytomas

OBJECTIVE

The T2-FLAIR mismatch sign is a useful imaging sign in clinical magnetic resonance imaging studies for detecting isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted astrocytomas. However, the association between the mismatch sign and pathologic findings is poorly understood. Therefore, the aim of this study was to elucidate the relationship of histopathologic and radiologic features with the mismatch sign in IDH-mutant 1p/19q non-codeleted astrocytomas.

METHODS

We divided 17 IDH-mutant 1p/19q non-codeleted patients into 2 groups according to mismatch sign presence (WITH, n = 9; WITHOUT, n = 8) and retrospectively analyzed their pathologic findings and apparent diffusion coefficient (ADC) values. We also compared these findings between the tumor Core (central area) and Rim (marginal area).

RESULTS

In the pathologic analysis, Core of the WITH group contained numerous microcysts whereas Rim had abundant neuroglial fibrils and cellularity. In contrast, Core of the WITHOUT group had highly concentrated neuroglial fibrils. In ADC analysis, Core of the WITH group had significantly higher ADC values compared with Rim (P < 0.001). However, there was no significant difference between Core and Rim in the WITHOUT group (P = 0.12). The WITH group had a significantly higher Core/Rim ratio of ADC values compared with the WITHOUT group (P < 0.001).

CONCLUSIONS

This study provides evidence that a region-dependent microstructural difference could reflect the mismatch sign in IDH-mutant 1p/19q non-codeleted astrocytomas. Core of the mismatch sign characteristically had microcystic changes accompanied by higher ADC values, whereas Rim had abundant neuroglial fibrils and cellularity accompanied by lower ADC values.

Metabolic Control of m6A RNA Modification

Nutrients and metabolic pathways regulate cell growth and cell fate decisions via epigenetic modification of DNA and histones. Another key genetic material, RNA, also contains diverse chemical modifications. Among these, N⁶-methyladenosine (m⁶A) is the most prevalent and evolutionarily conserved RNA modification. It functions in various aspects of developmental and disease states, by controlling RNA metabolism, such as stability and translation. Similar to other epigenetic processes, m⁶A modification is regulated by specific enzymes, including writers (methyltransferases), erasers (demethylases), and readers (m⁶A-binding proteins). As this is a reversible enzymatic process, metabolites can directly influence the flux of this reaction by serving as substrates and/or allosteric regulators. In this review, we will discuss recent understanding of the regulation of m⁶A RNA modification by metabolites, nutrients, and cellular metabolic pathways.

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.

Advancing Imaging to Enhance Surgery: From Image to Information Guidance

Conventional magnetic resonance imaging (cMRI) has an established role as a crucial disease parameter in the multidisciplinary management of glioblastoma, guiding diagnosis, treatment planning, assessment, and follow-up. Yet, cMRI cannot provide adequate information regarding tissue heterogeneity and the infiltrative extent beyond the contrast enhancement. Advanced magnetic resonance imaging and PET and newer analytical methods are transforming images into data (radiomics) and providing noninvasive biomarkers of molecular features (radiogenomics), conveying enhanced information for improving decision making in surgery. This review analyzes the shift from image guidance to information guidance that is relevant for the surgical treatment of glioblastoma.

Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Simple Summary

Approximately 75–80% of according to the classification of world health organization (WHO) grade II and III gliomas are characterized by a mutation of the isocitrate dehydrogenase (IDH) enzymes, which are very important in glioma cell metabolism. Patients with IDH mutated glioma have a significantly better prognosis than patients with IDH wildtype status, typically seen in glioblastoma WHO grade IV. Here we used a prospective O-(2-¹⁸F-fluoroethyl)-L-tyrosine (¹⁸F-FET) positron emission tomography guided single-voxel ¹H-magnetic resonance spectroscopy approach to predict the IDH status before surgery. Finally, 34 patients were included in this neuroimaging study, of whom eight had additionally tissue analysis. Using a machine learning technique, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% and a specificity of 75.0%. It was newly recognized, that two metabolites (myo-inositol and glycine) have a particularly important role in the determination of the IDH status.

Abstract

Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard ¹H-magnetic resonance spectroscopy (¹H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-¹⁸F-fluoroethyl)-L-tyrosine (¹⁸F-FET) for optimized voxel placement in ¹H-MRS. Routine ¹H-magnetic resonance (¹H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the ¹H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%) and a specificity of 75.0% (95% CI, 42.9–94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo ¹H-nuclear magnetic resonance (¹H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting.

Clinical Management of Diffuse Low-Grade Gliomas

Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.

Take Advantage of Glutamine Anaplerosis, the Kernel of the Metabolic Rewiring in Malignant Gliomas

Glutamine is a non-essential amino acid that plays a key role in the metabolism of proliferating cells including neoplastic cells. In the central nervous system (CNS), glutamine metabolism is particularly relevant, because the glutamine-glutamate cycle is a way of controlling the production of glutamate-derived neurotransmitters by tightly regulating the bioavailability of the amino acids in a neuron-astrocyte metabolic symbiosis-dependent manner. Glutamine-related metabolic adjustments have been reported in several CNS malignancies including malignant gliomas that are considered ‘glutamine addicted’. In these tumors, glutamine becomes an essential amino acid preferentially used in energy and biomass production including glutathione (GSH) generation, which is crucial in oxidative stress control. Therefore, in this review, we will highlight the metabolic remodeling that gliomas undergo, focusing on glutamine metabolism. We will address some therapeutic regimens including novel research attempts to target glutamine metabolism and a brief update of diagnosis strategies that take advantage of this altered profile. A better understanding of malignant glioma cell metabolism will help in the identification of new molecular targets and the design of new therapies.

Updates in Pediatric Malignant Gliomas

Malignant gliomas constitute a smaller portion of brain tumors in children compared with adults. Nevertheless, they can be devastating tumors with poor prognosis. Recent advances and improved understanding of the genetic and molecular characterization of pediatric brain tumors, including those of malignant gliomas, have led to the reclassification of many pediatric brain tumors and new entities have been defined. In this paper, we will present some of the more recent characterization and pertinent changes in pediatric high-grade gliomas, along with the conventional and advanced imaging features associated with these entities. Implications of the recent changes in pediatric malignant glioma classifications will also be discussed.

Magnetic resonance spectroscopy of isocitrate dehydrogenase mutated gliomas: current knowledge on the neurochemical profile

PURPOSE OF REVIEW

Magnetic resonance spectroscopy (MRS) may play a key role for the management of patients with glioma. We highlighted the utility of MRS in the noninvasive diagnosis of gliomas with mutations in isocitrate dehydrogenase (IDH) genes, by providing an overview of the neurochemical alterations observed in different glioma subtypes, as well as during treatment and progression, both in vivo and ex vivo.

RECENT FINDINGS

D-2-hydroxyglutarate (2HG) decrease during anticancer treatments was recently shown to be associated with altered levels of other metabolites, including lactate, glutamate and glutathione, suggesting that tumour treatment leads to a metabolic reprogramming beyond 2HG depletion. In combination with 2HG quantification, cystathionine and glycine seem to be the most promising candidates for higher specific identification of glioma subtypes and follow-up of disease progression and response to treatment.

SUMMARY

The implementation of advanced MRS methods in the routine clinical practice will allow the quantification of metabolites that are not detectable with conventional methods and may enable immediate, accurate diagnosis of gliomas, which is crucial for planning optimal therapeutic strategies and follow-up examinations. The role of different metabolites as predictors of patient outcome still needs to be elucidated.

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.

Clinicopathological analysis of T2-FLAIR mismatch sign in lower-grade gliomas

T2-FLAIR mismatch sign is known as a highly specific imaging marker of IDH-mutant astrocytomas. This study was intended to clarify what the T2-FLAIR mismatch sign represents by pathological analysis of lower-grade gliomas rediagnosed in accordance with the WHO 2016 classification. We retrospectively analyzed the records of 64 patients diagnosed with WHO grade II and III diffuse gliomas between June 2009 and November 2018. T2-FLAIR mismatch sign was found in 10 (45%) out of 22 patients with IDH-mutant astrocytoma, 1 (5%) out of 20 with oligodendroglioma, and 1 (5%) out of 22 with IDH-wild-type astrocytoma. T2-FLAIR mismatch sign as a marker of IDH-mutant astrocytomas showed positive predictive value of 83%. Among 22 patients with IDH-mutant astrocytomas, microcystic change was found in eight, of which seven showed T2-FLAIR mismatch sign. Microcystic change was significantly associated with T2-FLAIR mismatch sign (P < 0.01). From multi-sampling in a patient, abundant microcysts were observed upon HE staining of specimens from the T2-FLAIR mismatched region, while microcysts were hardly observed from the T2-FLAIR matched one. All three protoplasmic astrocytomas among our IDH-mutant astrocytomas presented T2-FLAIR mismatch sign. In conclusion, T2-FLAIR mismatch sign may reflect microcyst formation in IDH-mutant astrocytomas and be common in IDH-mutant protoplasmic astrocytoma.

Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient's treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients' age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas' IDH status prediction.

The biological and clinical basis for early referral of low grade glioma patients to a surgical neuro-oncologist

The discovery of IDH1/2 (isocitrate dehydrogenase) mutation in large scale, genomewide mutational analyses of gliomas has led to profound developments in understanding tumourigenesis, and restructuring of the classification of both high and low grade gliomas. Owing to this progress made in the recognition of molecular markers which predict tumour behavior and treatment response, the increasing importance of adjuvant treatments such as chemo- and radiotherapy, and the tremendous advances in surgical technique and intraoperative monitoring which have facilitated superior extents of resection whilst preserving neurological functioning and quality of life, contemporary management of low grade glioma (LGG) has switched from a passive, observant approach to a more active, interventional one. Furthermore, this has implications for the manner in which patients with incidentally discovered and/or asymptomatic LGG are managed, and this review of the biological behaviour of LGG, as well as its clinical investigation and management, should act as a timely reminder to all clinicians of the importance of referring LGG patients early to a surgical neuro-oncologist who is not only familiar and acquainted with the vagaries of this disease process, but who, in addition, is devoted to delivering care to these patients with the support of a multi-disciplinary clinical decision-making unit, comprising medical neuro-oncologists, radiation oncologists and allied health professionals.

Italian consensus and recommendations on diagnosis and treatment of low-grade gliomas. An intersociety (SINch/AINO/SIN) document

In 2018, the SINch (Italian Society of Neurosurgery) Neuro-Oncology Section, AINO (Italian Association of Neuro-Oncology) and SIN (Italian Association of Neurology) Neuro-Oncology Section formed a collaborative Task Force to look at the diagnosis and treatment of low-grade gliomas (LGGs). The Task Force included neurologists, neurosurgeons, neuro-oncologists, pathologists, radiologists, radiation oncologists, medical oncologists, a neuropsychologist and a methodologist. For operational purposes, the Task Force was divided into five Working Groups: diagnosis, surgical treatment, adjuvant treatments, supportive therapies, and follow-up. The resulting guidance document is based on the available evidence and provides recommendations on diagnosis and treatment of LGG patients, considering all aspects of patient care along their disease trajectory.

Radiological differences between subtypes of WHO 2016 grade II-III gliomas: a systematic review and meta-analysis

BACKGROUND

Isocitrate dehydrogenase (IDH) mutation and 1p/19q-codeletion are oncogenetic alterations with a positive prognostic value for diffuse gliomas, especially grade II and III. Some studies have suggested differences in biological behavior as reflected by radiological characteristics. In this paper, the literature regarding radiological characteristics in grade II and III glioma subtypes was systematically evaluated and a meta-analysis was performed.

METHODS

Studies that addressed the relationship between conventional radiological characteristics and IDH mutations and/or 1p/19q-codeletions in newly diagnosed, grade II and III gliomas of adult patients were included. The "3-group analysis" compared radiological characteristics between the WHO 2016 glioma subtypes (IDH-mutant astrocytoma, IDH-wildtype astrocytoma, and oligodendroglioma), and the "2-group analysis" compared radiological characteristics between 1p/19q-codeleted gliomas and 1p/19q-intact gliomas.

RESULTS

Fourteen studies (3-group analysis: 670 cases, 2-group analysis: 1042 cases) were included. IDH-mutated astrocytomas showed more often sharp borders and less frequently contrast enhancement compared to IDH-wildtype astrocytomas. 1p/19q-codeleted gliomas had less frequently sharp borders, but showed a heterogeneous aspect, calcification, cysts, and edema more frequently. For the 1p/19q-codeleted gliomas, a sensitivity of 96% was found for heterogeneity and a specificity of 88.1% for calcification.

CONCLUSIONS

Significant differences in conventional radiological characteristics exist between the WHO 2016 glioma subtypes, which may reflect differences in biological behavior. However, the diagnostic value of the independent radiological characteristics is insufficient to reliably predict the molecular genetic subtype.

The T2-FLAIR–mismatch sign as an imaging biomarker for IDH and 1p/19q status in diffuse low-grade gliomas: a systematic review with a Bayesian approach to evaluation of diagnostic test performance

OBJECTIVE

With the revised WHO 2016 classification of brain tumors, there has been increasing interest in imaging biomarkers to predict molecular status and improve the yield of genetic testing for diffuse low-grade gliomas (LGGs). The T2-FLAIR–mismatch sign has been suggested to be a highly specific radiographic marker of isocitrate dehydrogenase (IDH) gene mutation and 1p/19q codeletion status in diffuse LGGs. The presence of T2-FLAIR mismatch indicates a T2-hyperintense lesion that is hypointense on FLAIR with the exception of a hyperintense rim.

METHODS

In accordance with PRISMA guidelines, we performed a systematic review of the Ovid Medline, Embase, Scopus, and Cochrane databases for reports of studies evaluating the diagnostic performance of T2-FLAIR mismatch in predicting the IDH and 1p/19q codeletion status in diffuse LGGs. Results were combined into a 2 × 2 format, and the following diagnostic performance parameters were calculated: sensitivity, specificity, positive predictive value, negative predictive value, and positive (LR+) and negative (LR−) likelihood ratios. In addition, we utilized Bayes theorem to calculate posttest probabilities as a function of known pretest probabilities from previous genome-wide association studies and the calculated LRs. Calculations were performed for 1) IDH mutation with 1p/19q codeletion (IDHmut-Codel), 2) IDH mutation without 1p/19q codeletion (IDHmut-Noncodel), 3) IDH mutation overall, and 4) 1p/19q codeletion overall. The QUADAS-2 (revised Quality Assessment of Diagnostic Accuracy Studies) tool was utilized for critical appraisal of included studies.

RESULTS

A total of 4 studies were included, with inclusion of 2 separate cohorts from a study reporting testing and validation (n = 746). From pooled analysis of all cohorts, the following values were obtained for each molecular profile—IDHmut-Codel: sensitivity 30%, specificity 73%, LR+ 1.1, LR− 1.0; IDHmut-Noncodel: sensitivity 33.7%, specificity 98.5%, LR+ 22.5, LR− 0.7; IDH: sensitivity 32%, specificity 100%, LR+ 32.1, LR− 0.7; 1p/19q codeletion: sensitivity 0%, specificity 54%, LR+ 0.01, LR− 1.9. Bayes theorem was used to calculate the following posttest probabilities after a positive and negative result, respectively—IDHmut-Codel: 32.2% and 29.4%; IDHmut-Noncodel: 95% and 40%; IDH: 99.2% and 73.5%; 1p/19q codeletion: 0.4% and 35.1%.

CONCLUSIONS

The T2-FLAIR–mismatch sign was an insensitive but highly specific marker of IDH mutation and IDHmut-Noncodel profile, although significant exceptions may exist to this finding. Tumors with a positive sign may still be IDHwt or 1p/19q codeleted. These findings support the utility of T2-FLAIR mismatch as an imaging-based biomarker for positive selection of patients with IDH-mutant gliomas.

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker

Proton magnetic resonance spectroscopy (¹H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, ¹H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of ¹H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T₁ and T₂ relaxation; B₁ field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of ¹H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo¹H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by ¹H-MRS.

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.

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

BACKGROUND

The molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines.

METHODS

Fresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing.

RESULTS

Fresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%-94% sensitivity and 90%-100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min.

CONCLUSION

These data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.

Risk Factors for Progression Among Low-Grade Gliomas After Gross Total Resection and Initial Observation in the Molecular Era

PURPOSE

To identify risk factors for progression-free survival (PFS) in the molecular era among patients with low-grade glioma (LGG) who undergo gross total resection (GTR) followed by initial observation.

METHODS AND MATERIALS

We reviewed patients with World Health Organization grade 2 LGG treated at a single institution. We included only those who underwent magnetic resonance imaging (MRI)-confirmed GTR followed by initial observation. Molecular classification was obtained at either the time of diagnosis or pathology review. Cox proportional hazards regression, the Kaplan-Meier method, and the log-rank test were used. P values <.05 were considered statistically significant.

RESULTS

We included 144 patients who underwent MRI-confirmed GTR between 1994 and 2014 followed by initial observation. Median age was 29 years (interquartile range [IQR], 18-41), median tumor size was 2.7 cm (IQR, 1.8-4.0), and median follow-up was 81 months (IQR, 36-132). Molecular classification was 13% IDH-mutant 1p19q-codeleted; 21% IDH-mutant 1p19q-intact; 39% IDH1-R132H-wildtype; and 28% undetermined. For the entire cohort, 5- and 10-year PFS and overall survival were 71% and 53%, and 98% and 90%, respectively. On multivariate analysis, factors associated with worse PFS included increasing age at diagnosis (hazard ratio [HR], 1.05; 95% CI, 1.00-1.09; P = .03), increasing preoperative tumor size (HR, 1.07; 95% CI, 1.04-1.10; P < .0001), and IDH-mutant 1p19q-intact classification (HR, 3.18; 95% CI, 1.15-8.74, P = .025). Median PFS for patients with IDH-mutant 1p19q-codeleted, IDH-mutant 1p19q-intact, and IDH1-R132H-wildtype tumors were 113 months, 56 months, and not reached, respectively. Molecular classification was significantly associated with PFS (P < .0001) but not overall survival (P = .20).

CONCLUSIONS

Among patients with LGG who undergo MRI-confirmed GTR and initial observation in the molecular era, increasing age, increasing tumor size, and IDH-mutant 1p19q-intact classification are associated with worse PFS. Because tumor progression is associated with adverse health-related quality of life, these factors may aid clinicians and patients in the shared decision-making process regarding goals of surgery and timing of postoperative therapy. Further study is required to elucidate why IDH-mutant 1p19q-intact LGGs are at higher risk for early progression.

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.