MR Imaging-derived Oxygen Metabolism and Neovascularization Characterization for Grading and IDH Gene Mutation Detection of Gliomas

Vessel-specific quantification of cerebral venous oxygenation with velocity-encoding preparation and rapid acquisition

PURPOSE

Non-invasive measurement of cerebral venous oxygenation (Yv) is of critical importance in brain diseases. The present work proposed a fast method to quantify regional Yv map for both large and small veins.

METHODS

A new sequence was developed, referred to as TRU-VERA (T2 relaxation under velocity encoding and rapid acquisition, which isolates blood spins from static tissue with velocity-encoding preparation, modulates the T2 weighting of venous signal with T2-preparation and utilizes a bSSFP readout to achieve fast acquisition with high resolution. The sequence was first optimized to achieve best sensitivity for both large and small veins, and then validated with TRUST (T2 relaxation under spin tagging), TRUPC (T2 relaxation under phase contrast), and accelerated TRUPC MRI. Regional difference of Yv was evaluated, and test-retest reproducibility was examined.

RESULTS

Optimal Venc was determined to be 3 cm/s, while recovery time and balanced SSFP flip angle within reasonable range had minimal effect on SNR efficiency. Venous T2 measured with TRU-VERA was highly correlated with T2 from TRUST (R2 = 0.90), and a conversion equation was established for further calibration to Yv. TRU-VERA sequences showed consistent Yv estimation with TRUPC (R2 = 0.64) and accelerated TRUPC (R2 = 0.79). Coefficient of variation was 0.84% for large veins and 2.49% for small veins, suggesting an excellent test-retest reproducibility.

CONCLUSION

The proposed TRU-VERA sequence is a promising method for vessel-specific oxygenation assessment.

Quantitative BOLD (qBOLD) imaging of oxygen metabolism and blood oxygenation in the human body: A scoping review

PURPOSE

There are many approaches to the quantitative BOLD (qBOLD) technique described in the literature, differing in pulse sequences, MRI parameters and data processing. Thus, in this review, we summarized the acquisition methods, approaches used for oxygenation quantification and clinical populations investigated.

METHODS

Three databases were systematically searched (Medline, Embase, and Web of Science) for published research that used qBOLD methods for quantification of oxygen metabolism. Data extraction and synthesis were performed by one author and reviewed by a second author.

RESULTS

A total of 93 relevant papers were identified. Acquisition strategies were summarized, and oxygenation parameters were found to have been investigated in many pathologies such as steno-occlusive diseases, stroke, glioma, and multiple sclerosis disease.

CONCLUSION

A summary of qBOLD approaches for oxygenation measurements and applications could help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.

Identification of prognostic imaging biomarkers in H3 K27-altered diffuse midline gliomas in adults: impact of tumor oxygenation imaging biomarkers on survival

PURPOSE

To investigate prognostic markers for H3 K27-altered diffuse midline gliomas (DMGs) in adults with clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics.

METHODS

Retrospective chart and imaging reviews were conducted on 32 adults with H3 K27-altered DMGs between 2017 and 2023. Clinical and qualitative imaging characteristics were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate normalized cerebral blood volume (nCBV), capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen (rCMRO2), and mean ADC values. Leptomeningeal metastases (LM) was diagnosed with imaging. Cox analyses were conducted to determine predictors of overall survival (OS) in entire patients and a subgroup of patients with contrast-enhancing (CE) tumor.

RESULTS

The median patient age was 40.5 years (range 19.9-75.7), with an OS of 30.3 months (interquartile range 11.3-32.3). In entire patients, the presence of LM was the only independent predictor of OS (hazard ratio [HR] = 6.01, P = 0.009). In the subgroup of 23 (71.9%) patients with CE tumors, rCMRO2 of CE tumor (HR = 1.08, P = 0.019) and the presence of LM (HR = 5.92, P = 0.043) were independent predictors of OS.

CONCLUSION

The presence of LM was independently associated with poor prognosis in adult patients with H3 K27-altered DMG. In patients with CE tumors, higher rCMRO2 of CE tumor, which may reflect higher metabolic activity in the tumor oxygenation microenvironment, may be a useful imaging biomarker to predict poor prognosis.

Hemodynamic response function description in patients with glioma

INTRODUCTION

Functional magnetic resonance imaging is a powerful tool that has provided many insights into cognitive sciences. Yet, as its analysis is mostly based on the knowledge of an a priori canonical hemodynamic response function (HRF), its reliability in patients' applications has been questioned. There have been reports of neurovascular uncoupling in patients with glioma, but no specific description of the Hemodynamic Response Function (HRF) in glioma has been reported so far. The aim of this work is to describe the HRF in patients with glioma.

METHODS

Forty patients were included. MR images were acquired on a 1.5T scanner. Activated clusters were identified using a fuzzy general linear model; HRFs were adjusted with a double-gamma function. Analyses were undertaken considering the tumor grade, age, sex, tumor location, and activated location.

RESULTS

Differences are found in the occipital, limbic, insular, and sub-lobar areas, but not in the frontal, temporal, and parietal lobes. The presence of a glioma slows the time-to-peak and onset times by 5.2 and 3.8 % respectively; high-grade gliomas present 8.1 % smaller HRF widths than low-grade gliomas.

DISCUSSION AND CONCLUSION

There is significant HRF variation due to the presence of glioma, but the magnitudes of the observed differences are small. Most processing pipelines should be robust enough for this magnitude of variation and little if any impact should be visible on functional maps. The differences that have been observed in the literature between functional mapping obtained with magnetic resonance vs. that obtained with direct electrostimulation during awake surgery are more probably due to the intrinsic difference in the mapping process: fMRI mapping detects all recruited areas while intra-surgical mapping indicates only the areas indispensable for the realization of a certain task. Surgical mapping might not be the gold standard to use when trying to validate the fMRI mapping process.

Noninvasive Isocitrate Dehydrogenase 1 Status Prediction in Grade II/III Glioma Based on Magnetic Resonance Images: A Transfer Learning Strategy

OBJECTIVE

The aim of this study was to evaluate transfer learning combined with various convolutional neural networks (TL-CNNs) in predicting isocitrate dehydrogenase 1 ( IDH1 ) status of grade II/III gliomas.

METHODS

Grade II/III glioma patients diagnosed at the Tangdu Hospital (August 2009 to May 2017) were retrospectively enrolled, including 54 patients with IDH1 mutant and 56 patients with wild-type IDH1 . Convolutional neural networks, AlexNet, GoogLeNet, ResNet, and VGGNet were fine-tuned with T2-weighted imaging (T2WI), fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (T1CE) images. The single-modal networks were integrated with averaged sigmoid probabilities, logistic regression, and support vector machine. FLAIR-T1CE-fusion (FC-fusion), T2WI-T1CE-fusion (TC-fusion), and FLAIR-T2WI-T1CE-fusion (FTC-fusion) were used for fine-tuning TL-CNNs.

RESULTS

IDH1 -mutant prediction accuracies using AlexNet, GoogLeNet, ResNet, and VGGNet achieved 70.0% (AUC = 0.660), 65.0% (AUC = 0.600), 70.0% (AUC = 0.700), and 80.0% (AUC = 0.730) for T2WI images, 70.0% (AUC = 0.660), 70.0% (AUC = 0.620), 70.0% (AUC = 0.710), and 80.0% (AUC = 0.720) for FLAIR images, and 73.7% (AUC = 0.744), 73.7% (AUC = 0.656), 73.7% (AUC = 0.633), and 73.7% (AUC = 0.700) for T1CE images, respectively. The highest AUC (0.800) was achieved using VGGNet and FC-fusion images.

CONCLUSIONS

TL-CNNs (especially VGGNet) had a potential predictive value for IDH1 -mutant status of grade II/III gliomas.

Toward vendor-independent measurement of cerebral venous oxygenation: Comparison of TRUST MRI across three major MRI manufacturers and association with end-tidal CO2

Cerebral venous oxygenation (Yv ) is a valuable biomarker for a variety of brain diseases. T2 relaxation under spin tagging (TRUST) MRI is a widely used method for Yv quantification. In this work, there were two main objectives. The first was to evaluate the reproducibility of TRUST Yv measurements across MRI scanners from different vendors. The second was to examine the correlation between Yv and end-tidal CO2 (EtCO2 ) in a multisite, multivendor setting and determine the usefulness of this correlation to account for variations in Yv caused by normal variations and physiological fluctuations. Standardized TRUST pulse sequences were implemented on three scanners from major MRI vendors (GE, Siemens, Philips). These scanners were located at two research institutions. Ten healthy subjects were scanned. On each scanner, the subject underwent two scan sessions, each of which included three TRUST scans, to evaluate the intrasession and intersession reproducibility of Yv . Each scanner was also equipped with a capnograph device to record the EtCO2 of the subject during the MRI scan. We found no significant bias in Yv measurements across the three scanners (P = 0.18). The measured Yv values on the three scanners were also strongly correlated with each other (intraclass correlation coefficients > 0.85, P < 0.001). The intrasession and intersession coefficients of variation of Yv were less than 4% and showed no significant difference among the scanners. In addition, our results revealed that (1) within the same subject, Yv increased with EtCO2 at a rate of 1.24 ± 0.17%/mmHg (P < 0.0001), and (2) across different subjects, individuals with a higher EtCO2 had a higher Yv , at a rate of 0.94 ± 0.36%/mmHg (P = 0.01). These results suggest that (1) the standardized TRUST sequences had similar accuracies and reproducibilities for the quantification of Yv across the scanners, and (2) recording of EtCO2 may be a useful complement to Yv measurement to account for CO2 -related physiological fluctuations in Yv in multisite, multivendor studies.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Differentiating high-grade glioma progression from treatment-related changes with dynamic [18F]FDOPA PET: a multicentric study

OBJECTIVES

Diagnostic accuracy of amino-acid PET for distinguishing progression from treatment-related changes (TRC) is currently based on single-center non-homogeneous glioma populations. Our study assesses the diagnostic value of static and dynamic [¹⁸F]FDOPA PET acquisitions to differentiate between high-grade glioma (HGG) recurrence and TRC in a large cohort sourced from two independent nuclear medicine centers.

METHODS

We retrospectively identified 106 patients with suspected glioma recurrences (WHO GIII, n = 38; GIV, n = 68; IDH-mutant, n = 35, IDH-wildtype, n = 71). Patients underwent dynamic [¹⁸F]FDOPA PET/CT (n = 83) or PET/MRI (n = 23), and static tumor-to-background ratios (TBRs), metabolic tumor volumes and dynamic parameters (time to peak and slope) were determined. The final diagnosis was either defined by histopathology or a clinical-radiological follow-up at 6 months. Optimal [¹⁸F]FDOPA PET parameter cut-offs were obtained by receiver operating characteristic analysis. Predictive factors and clinical parameters were assessed using univariate and multivariate Cox regression survival analyses.

RESULTS

Surgery or the clinical-radiological 6-month follow-up identified 71 progressions and 35 treatment-related changes. TBR_mean, with a threshold of 1.8, best-differentiated glioma recurrence/progression from post-treatment changes in the whole population (sensitivity 82%, specificity 71%, p < 0.0001) whereas curve slope was only significantly different in IDH-mutant HGGs (n = 25). In survival analyses, MTV was a clinical independent predictor of progression-free and overall survival on the multivariate analysis (p ≤ 0.01). A curve slope > -0.12/h was an independent predictor for longer PFS in IDH-mutant HGGs CONCLUSION: Our multicentric study confirms the high accuracy of [¹⁸F]FDOPA PET to differentiate recurrent malignant gliomas from TRC and emphasizes the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant HGGs.

KEY POINTS

• The diagnostic accuracy of dynamic amino-acid PET, for distinguishing progression from treatment-related changes, is currently based on single-center non-homogeneous glioma populations. • This multicentric study confirms the high accuracy of static [¹⁸F]FDOPA PET images for differentiating progression from treatment-related changes in a homogeneous population of high-grade gliomas and highlights the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant high-grade gliomas. • Dynamic acquisitions should be performed in IDH-mutant glioma patients to provide valuable information for the differential diagnosis of recurrence and treatment-related changes.

Cyclin-Dependent Kinase Inhibitor 2A/B Homozygous Deletion Prediction and Survival Analysis

Cyclin-Dependent Kinase Inhibitor 2A/B (CDKN2A/B) homozygous deletion was a significant prognostic factor for gliomas and affected the treatment strategy. However, the radiomic features of CDKN2A/B homozygous deletion in gliomas have not been developed, and whether the radiomic features and molecular subgroups can provide prognostic value in low-grade gliomas (LGGs) has yet to be studied. Thus, this study aimed to develop a predictive model of CDKN2A/B in gliomas and investigate the prognostic value of this biomarker and radiomic features in isocitrate dehydrogenase (IDH)-mutant LGGs. First, we developed the predictive model of CDKN2A/B homozygous deletion in 292 patients. The results revealed that radiomic features predict CDKN2A/B homozygous deletion with high accuracy and reliability. Subsequently, the prognostic survival models of 104 patients (IDH-mutant LGGs) were established, which provided an essential value for prognostic evaluation and indicated that CDKN2A/B homozygous deletion can be used as an independent predictor of prognosis in LGGs.

Measurement of Cerebral Oxygen Extraction Fraction Using Quantitative BOLD Approach: A Review

Quantification of brain oxygenation and metabolism, both of which are indicators of the level of brain activity, plays a vital role in understanding the cerebral perfusion and the pathophysiology of brain disorders. Magnetic resonance imaging (MRI), a widely used clinical imaging technique, which is very sensitive to magnetic susceptibility, has the possibility of substituting positron emission tomography (PET) in measuring oxygen metabolism. This review mainly focuses on the quantitative blood oxygenation level-dependent (qBOLD) method for the evaluation of oxygen extraction fraction (OEF) in the brain. Here, we review the theoretic basis of qBOLD, as well as existing acquisition and quantification methods. Some published clinical studies are also presented, and the pros and cons of qBOLD method are discussed as well.

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.

Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO₂) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO₂; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO₂ and mitoPO₂ values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.

Review of the Research Progress of Human Brain Oxygen Extraction Fraction by Magnetic Resonance Imaging

In recent years, the incidence of cerebrovascular diseases (CVD) is increasing, which seriously endangers human health. The study on hemodynamics of cerebrovascular disease can help us to understand, prevent, and treat the disease. As one of the important parameters of human cerebral hemodynamics and tissue metabolism, OEF (oxygen extraction fraction) is of great value in central nervous system diseases. The use of BOLD (blood oxygen level dependent) effect offers the possibility to study cerebral hemodynamic and metabolic characteristics by MRI (magnetic resonance imaging) measurements. Therefore, this paper reviews the hemodynamic parameters of brain tissue, discusses the principles and methods of quantitative BOLD-based MRI measurements of OEF, and discusses the advantages and disadvantages of each method.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Using quantitative MRI to study the association of isocitrate dehydrogenase (IDH) status with oxygen metabolism and cellular structure changes in glioma

OBJECTIVE

To investigate the characteristics of oxygen metabolism and the cellular structure of glioma using quantitative MRI to predict the isocitrate dehydrogenase 1 (IDH1) status and to further understand the biological characteristics of gliomas.

METHODS

In this retrospective study, 94 patients with gliomas eventually received quantitative MRI measures to study oxygen metabolism. The oxygen metabolism biomarker maps (oxygen extraction fraction [OEF] and cerebral metabolic rate of oxygen [CMRO₂]) and the tissue-cellular-specific (R₂t*) MRI relaxation parameter were evaluated in different regions of glioma.

RESULTS

MRI results showed differences in oxygen metabolism measures in all patients with gliomas of different IDH1 statuses. Compared to patients with IDH1 mutant gliomas, patients with IDH1 wild type gliomas showed increased (P < 0.01) CMRO₂, OEF, cerebral blood volume [CBF], and R₂t* measures in tumor regions, while only OEF, CBF and R₂t* were found to be increased (P < 0.05) in the peritumoral area. OEF achieved the best performance for distinguishing IDH1 wild type and mutant gliomas in the tumor area (AUC = 0.732, P < 0.001). R₂t* values correlated with Ki-67(R = 0.35, P < 0.001) in the tumor area, while no significant correlations between Ki-67 and R₂t* were found in the peritumoral area (R = 0.19, P = 0.072).

CONCLUSION

Quantitative MRI has potential applications in studying the tumor and peritumoral areas of glioma, and it has the ability to predict and reveal the characteristics of oxygen metabolism and cellular structure in different regions of gliomas.

A Comparative Study Between Tumor Blood Vessels and Dynamic Contrast-enhanced MRI for Identifying Isocitrate Dehydrogenase Gene 1 (IDH1) Mutation Status in Glioma

OBJECTIVE

Isocitrate dehydrogenase gene (IDH) mutations are associated with tumor angiogenesis and therefore play an important role in glioma management. This study compared the performance of tumor blood vessels counted from contrast-enhanced 3D brain volume (3D-BRAVO) sequence and dynamic contrast-enhanced (DCE) MRI in differentiating IDH1 status in gliomas.

METHODS

Forty-four glioma patients [16 with IDH1 mutant-type (IDH1-MT), 28 with IDH1 wild-type (IDH1-WT)] were retrospectively analyzed. A blood vessel entering a tumor was defined as an intratumoral vessel; a blood vessel adjacent to the edge of a tumor was defined as a peritumoral vessel. Combined vessels were defined as the sum of the intratumoral and peritumoral vessels. DCE-derived metrics of tumor were normalized to the contralateral normal-appearing white matter.

RESULTS

Intratumoral, peritumoral, and combined tumor blood vessels were all significantly different between IDH1-MT and IDH1-WT gliomas, and the range of area under curves (AUCs) was 0.816-0.855. For DCE-derived parameters, cerebral blood volume, cerebral blood flow, mean transit time, and volume transfer constant were significantly different between IDH1-MT and IDH1-WT gliomas, and the range of AUCs was 0.703-0.756. Combined vessels possessed the best performance for identifying IDH1 mutations in gliomas (AUC: 0.855, sensitivity: 0.857, specificity: 0.812, P<0.001).

CONCLUSION

The number of tumor blood vessels has comparable diagnostic performance with DCE-derived parameters for differentiating IDH1 mutations and can serve as a potential imaging biomarker to reflect IDH1 mutations in gliomas.

Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data

Simple Summary

The pretreatment diagnosis of contrast-enhancing brain tumors is still challenging in clinical neuro-oncology due to their very similar appearance on conventional MRI. A precise initial characterization, however, is essential to initiate appropriate treatment management, which can substantially differ between brain tumor entities. To overcome the disadvantage of the low specificity of conventional MRI, several new neuroimaging methods have been developed and validated over the past decades. This increasing amount of diagnostic information makes a timely evaluation without computational support impossible in a clinical setting. Artificial intelligence methods such as machine learning offer new options to support clinicians. In this study, we combined nine common machine learning algorithms with a physiological MRI technique (we named this approach “radiophysiomics”) to investigate the effectiveness of the multiclass classification of contrast-enhancing brain tumors in a clinical setting. We were able to demonstrate that radiophysiomics could be helpful in the routine diagnostics of contrast-enhancing brain tumors, but further automation using deep neural networks is required.

Abstract

The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.

Cerebral oxygen extraction fraction MRI: Techniques and applications

The human brain constitutes 2% of the body's total mass but uses 20% of the oxygen. The rate of the brain's oxygen utilization can be derived from a knowledge of cerebral blood flow and the oxygen extraction fraction (OEF). Therefore, OEF is a key physiological parameter of the brain's function and metabolism. OEF has been suggested to be a useful biomarker in a number of brain diseases. With recent advances in MRI techniques, several MRI-based methods have been developed to measure OEF in the human brain. These MRI OEF techniques are based on the T₂ of blood, the blood signal phase, the magnetic susceptibility of blood-containing voxels, the effect of deoxyhemoglobin on signal behavior in extravascular tissue, and the calibration of the BOLD signal using gas inhalation. Compared to ¹⁵ O PET, which is considered the "gold standard" for OEF measurement, MRI-based techniques are non-invasive, radiation-free, and are more widely available. This article provides a review of these emerging MRI-based OEF techniques. We first briefly introduce the role of OEF in brain oxygen homeostasis. We then review the methodological aspects of different categories of MRI OEF techniques, including their signal mechanisms, acquisition methods, and data analyses. The strengths and limitations of the techniques are discussed. Finally, we review key applications of these techniques in physiological and pathological conditions.

Cerebrovascular Dysregulation in Patients with Glioma Assessed with Time-shifted BOLD fMRI

Background Microscopic vascular events, such as neovascularization and neurovascular uncoupling, are common in cerebral glioma. Mapping the cerebrovascular network remodeling at the macroscopic level may provide an alternative approach to assess hemodynamic dysregulation in patients with glioma. Purpose To investigate cerebrovascular dynamics and their relevance to tumor aggressiveness by using time-shift analysis (TSA) of the systemic low-frequency oscillation (sLFO) of the resting-state blood oxygenation level-dependent signal and a decision tree model. Materials and Methods In this retrospective study, 96 patients with histologically confirmed cerebral glioma were consecutively included (March 2012 to February 2017). TSA was performed to quantify the temporal properties of sLFO signals. Alteration in the time-shift properties was assessed in the tumor region and the contralesional hemisphere relative to the brains of healthy controls by using the Mann-Whitney U test. A decision tree model based on time-shift features was developed to predict the World Health Organization (WHO) glioma grade. Results A total of 88 patients with glioma (WHO grade II, 45; grade III, 21; grade IV, 22; mean age, 42 years; age range, 20-73 years; 51 men) and 40 healthy individuals from the 1000 Functional Connectomes Project (mean age, 32 years; age range, 24-49 years; 19 men) were included. The sLFO of the brain tissues was characterized by increased time shift in the tumor region and enhanced correlation with the global reference signal in the contralesional hemisphere compared with healthy brains. The proportion of tumor voxels with negative correlation to the reference signal significantly increased with the glioma malignancy grade. The decision tree model achieved an accuracy of 91% (80 of 88 patients) in predicting the glioma malignancy grade at the individual level (P = .004) based on the time-shift features. Conclusion Gliomas induced grade-specific cerebrovascular dysregulation in the entire brain, with altered time-shift features of systemic low-frequency oscillation signals. © RSNA, 2022 Online supplemental material is available for this article.

Association between tissue hypoxia, perfusion restrictions, and microvascular architecture alterations with lesion-induced impairment of neurovascular coupling

Functional magnetic resonance imaging (fMRI) has been mainly utilized for the preoperative localization of eloquent cortical areas. However, lesion-induced impairment of neurovascular coupling (NVC) in the lesion border zone may lead to false-negative fMRI results. The purpose of this study was to determine physiological factors impacting the NVC. Twenty patients suffering from brain lesions were preoperatively examined using multimodal neuroimaging including fMRI, magnetoencephalography (MEG) during language or sensorimotor tasks (depending on lesion location), and a novel physiologic MRI approach for the combined quantification of oxygen metabolism, perfusion state, and microvascular architecture. Congruence of brain activity patterns between fMRI and MEG were found in 13 patients. In contrast, we observed missing fMRI activity in perilesional cortex that demonstrated MEG activity in seven patients, which was interpreted as lesion-induced impairment of NVC. In these brain regions with impaired NVC, physiologic MRI revealed significant brain tissue hypoxia, as well as significantly decreased macro- and microvascular perfusion and microvascular architecture. We demonstrated that perilesional hypoxia with reduced vascular perfusion and architecture is associated with lesion-induced impairment of NVC. Our physiologic MRI approach is a clinically applicable method for preoperative risk assessment for the presence of false-negative fMRI results and may prevent severe postoperative functional deficits.

The potential of advanced MR techniques for precision radiotherapy of glioblastoma

As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO₂), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO₂ to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping

PURPOSE

To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).

METHODS

The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test.

RESULTS

In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps.

CONCLUSION

QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.

Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

Radiomics and radiogenomics in gliomas: a contemporary update

The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

Physiological MRI of microvascular architecture, neovascularization activity, and oxygen metabolism facilitate early recurrence detection in patients with IDH-mutant WHO grade 3 glioma

PURPOSE

This study aimed to determine the diagnostic performance of physiological MRI biomarkers including microvascular perfusion and architecture, neovascularization activity, tissue oxygen metabolism, and tension for recurrence detection of IDH-mutant WHO grade 3 glioma.

METHODS

Sixty patients with IDH-mutant WHO grade 3 glioma who received overall 288 follow-up MRI examinations at 3 Tesla after standard treatment were retrospectively evaluated. A conventional MRI protocol was extended with a physiological MRI approach including vascular architecture mapping and quantitative blood-oxygen-level-dependent imaging which required 7 min extra data acquisition time. Custom-made MATLAB software was used for the calculation of MRI biomarker maps of microvascular perfusion and architecture, neovascularization activity, tissue oxygen metabolism, and tension. Statistical procedures included receiver operating characteristic analysis.

RESULTS

Overall, 34 patients showed recurrence of the WHO grade 3 glioma; of these, in 15 patients, recurrence was detected one follow-up examination (averaged 160 days) earlier by physiological MRI data than by conventional MRI. During this time period, the tumor volume increased significantly (P = 0.001) on average 7.4-fold from 1.5 to 11.1 cm³. Quantitative analysis of MRI biomarkers demonstrated microvascular but no macrovascular hyperperfusion in early recurrence. Neovascularization activity (AUC = 0.833), microvascular perfusion (0.682), and oxygen metabolism (0.661) showed higher diagnostic performance for early recurrence detection of WHO grade 3 glioma compared to conventional MRI including cerebral blood volume (0.649).

CONCLUSION

This study demonstrated that the targeted assessment of microvascular features and tissue oxygen tension as an early sign of neovascularization activity provided valuable information for recurrence diagnostic of WHO grade 3 glioma.

Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD

PURPOSE

To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO₂ ) using temporal clustering, tissue composition, and total variation (CCTV).

METHODS

Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO₂ were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test.

RESULTS

In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001).

CONCLUSION

The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.

In vivo characterization of physiological and metabolic changes related to isocitrate dehydrogenase 1 mutation expcression by multiparametric MRI and MRS in a rat model with orthotopically grafted human-derived glioblastoma cell lines

The physiological mechanism induced by the isocitrate dehydrogenase 1 (IDH1) mutation, associated with better treatment response in gliomas, remains unknown. The aim of this preclinical study was to characterize the IDH1 mutation through in vivo multiparametric MRI and MRS. Multiparametric MRI, including the measurement of blood flow, vascularity, oxygenation, permeability, and in vivo MRS, was performed on a 4.7 T animal MRI system in rat brains grafted with human-derived glioblastoma U87 cell lines expressing or not the IDH1 mutation by the CRISPR/Cas9 method, and secondarily characterized with additional ex vivo HR-MAS and histological analyses. In univariate analyses, compared with IDH1-, IDH1+ tumors exhibited higher vascular density (p < 0.01) and better perfusion (p = 0.02 for cerebral blood flow), but lower vessel permeability (p < 0.01 for time to peak (TTP), p = 0.04 for contrast enhancement) and decreased T1 map values (p = 0.02). Using linear discriminant analysis, vascular density and TTP values were found to be independent MRI parameters for characterizing the IDH1 mutation (p < 0.01). In vivo MRS and ex vivo HR-MAS analysis showed lower metabolites of tumor aggressiveness for IDH1+ tumors (p < 0.01). Overall, the IDH1 mutation exhibited a higher vascularity on MRI, a lower permeability, and a less aggressive metabolic profile. These MRI features may prove helpful to better pinpoint the physiological mechanisms induced by this mutation.

Physiological MRI Biomarkers in the Differentiation Between Glioblastomas and Solitary Brain Metastases

PURPOSE

Glioblastomas (GB) and solitary brain metastases (BM) are the most common brain tumors in adults. GB and BM may appear similar in conventional magnetic resonance imaging (cMRI). Their management strategies, however, are quite different with significant consequences on clinical outcome. The aim of this study was to evaluate the usefulness of a previously presented physiological MRI approach scoping to obtain quantitative information about microvascular architecture and perfusion, neovascularization activity, and oxygen metabolism to differentiate GB from BM.

PROCEDURES

Thirty-three consecutive patients with newly diagnosed, untreated, and histopathologically confirmed GB or BM were preoperatively examined with our physiological MRI approach as part of the cMRI protocol.

RESULTS

Physiological MRI biomarker maps revealed several significant differences in the pathophysiology of GB and BM: Central necrosis was more hypoxic in GB than in BM (30 %; P = 0.036), which was associated with higher neovascularization activity (65 %; P = 0.043) and metabolic rate of oxygen (48 %; P = 0.004) in the adjacent contrast-enhancing viable tumor parts of GB. In peritumoral edema, GB infiltration caused neovascularization activity (93 %; P = 0.018) and higher microvascular perfusion (30 %; P = 0.022) associated with higher tissue oxygen tension (33 %; P = 0.020) and lower oxygen extraction from vasculature (32 %; P = 0.040).

CONCLUSION

Our physiological MRI approach, which requires only 7 min of extra data acquisition time, might be helpful to noninvasively distinguish GB and BM based on pathophysiological differences. However, further studies including more patients are required.

Hypoxia and Microvascular Alterations Are Early Predictors of IDH-Mutated Anaplastic Glioma Recurrence

Simple Summary

Anaplastic gliomas (AGs) are considered the most common and aggressive primary brain tumors of young adults with inevitable recurrence and treatment failure. The aim of this study was to investigate whether the imaging biomarkers of hypoxia, microvascular architecture and neovascularization activity can be of assistance to detect pathophysiological changes in the early developmental stages of isocitrate-dehydrogenase (IDH) mutated AG recurrence. We evaluated 142 physiological magnetic resonance imaging follow-up examinations as a part of the conventional magnetic resonance imaging (MRI) protocol in 60 AG patients after standard therapy. Physiological MRI biomarkers showed intensifying local tissue hypoxia 250 days prior to radiological recurrence with following upregulation of neovascularization activity 50 to 70 days later. Integration of physiological MRI in the monitoring of AG patients may be of clinical significance to make personalized decision of early tumor recurrence without an additional delay for multimodal therapy.

Abstract

Anaplastic gliomas (AG) represents aggressive brain tumors that often affect young adults. Although isocitrate-dehydrogenase (IDH) gene mutation has been identified as a more favorable prognostic factor, most IDH-mutated AG patients are confronted with tumor recurrence. Hence, increased knowledge about pathophysiological precursors of AG recurrence is urgently needed in order to develop precise diagnostic monitoring and tailored therapeutic approaches. In this study, 142 physiological magnetic resonance imaging (phyMRI) follow-up examinations in 60 AG patients after standard therapy were evaluated and magnetic resonance imaging (MRI) biomarker maps for microvascular architecture and perfusion, neovascularization activity, oxygen metabolism, and hypoxia calculated. From these 60 patients, 34 patients developed recurrence of the AG, and 26 patients showed no signs for AG recurrence during the study period. The time courses of MRI biomarker changes were analyzed regarding early pathophysiological alterations over a one-year period before radiological AG recurrence or a one-year period of stable disease for patients without recurrence, respectively. We detected intensifying local tissue hypoxia 250 days prior to radiological recurrence which initiated upregulation of neovascularization activity 50 to 70 days later. These changes were associated with a switch from an avascular infiltrative to a vascularized proliferative phenotype of the tumor cells another 30 days later. The dynamic changes of blood perfusion, microvessel density, neovascularization activity, and oxygen metabolism showed a close physiological interplay in the one-year period prior to radiological recurrence of IDH-mutated AG. These findings may path the wave for implementing both new MR-based imaging modalities for routine follow-up monitoring of AG patients after standard therapy and furthermore may support the development of novel, tailored therapy options in recurrent AG.

Advanced Imaging Techniques for Radiotherapy Planning of Gliomas

The accuracy of target delineation in radiation treatment (RT) planning of cerebral gliomas is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Conventional magnetic resonance imaging (MRI), including contrast-enhanced T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, represents the current standard imaging modality for target volume delineation of gliomas. However, conventional sequences have limited capability to discriminate treatment-related changes from viable tumors, owing to the low specificity of increased blood-brain barrier permeability and peritumoral edema. Advanced physiology-based MRI techniques, such as MR spectroscopy, diffusion MRI and perfusion MRI, have been developed for the biological characterization of gliomas and may circumvent these limitations, providing additional metabolic, structural, and hemodynamic information for treatment planning and monitoring. Radionuclide imaging techniques, such as positron emission tomography (PET) with amino acid radiopharmaceuticals, are also increasingly used in the workup of primary brain tumors, and their integration in RT planning is being evaluated in specialized centers. This review focuses on the basic principles and clinical results of advanced MRI and PET imaging techniques that have promise as a complement to RT planning of gliomas.

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.

Tissue Hypoxia and Alterations in Microvascular Architecture Predict Glioblastoma Recurrence in Humans

PURPOSE

Insufficient control of infiltrative glioblastoma (GBM) cells is a major cause of treatment failure and tumor recurrence. Hence, detailed insights into pathophysiologic changes that precede GBM recurrence are needed to develop more precise neuroimaging modalities for tailored diagnostic monitoring and therapeutic approaches.

EXPERIMENTAL DESIGN

Overall, 168 physiologic MRI follow-up examinations of 56 patients with GBM who developed recurrence after standard therapy were retrospectively evaluated, that is, two post-standard-therapeutic follow-ups before and one at radiological recurrence. MRI biomarkers for microvascular architecture and perfusion, neovascularization activity, oxygen metabolism, and hypoxia were determined for brain areas that developed in the further course into recurrence and for the recurrent GBM itself. The temporal pattern of biomarker changes was fitted with locally estimated scatterplot smoothing functions and analyzed for pathophysiologic changes preceding radiological GBM recurrence.

RESULTS

Our MRI approach demonstrated early pathophysiologic changes prior to radiological GBM recurrence in all patients. Analysis of the time courses revealed a model for the pathophysiology of GBM recurrence: 190 days prior to radiological recurrence, vascular cooption by GBM cells induced vessel regression, detected as decreasing vessel density/perfusion and increasing hypoxia. Seventy days later, neovascularization activity was upregulated, which reincreased vessel density and perfusion. Hypoxia, however, continued to intensify for 30 days and peaked 90 days before radiological recurrence.

CONCLUSIONS

Hypoxia may represent an early sign for GBM recurrence. This might become useful in the development of new combined diagnostic-therapeutic approaches for tailored clinical management of recurrent GBM. Further preclinical and in-human studies are required for validation and evaluation.

Non-Invasive Assessment of Hypoxia and Neovascularization with MRI for Identification of Aggressive Breast Cancer

The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric breast MRI including quantitative blood-oxygen-level-dependent (qBOLD) imaging for hypoxia and vascular architecture mapping for neovascularization. Quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO2), mitochondrial oxygen tension (mitoPO2), microvessel radius (VSI), microvessel density (MVD), and microvessel type indicator (MTI) were calculated. Histopathology was the standard of reference. Histopathological markers (vascular endothelial growth factor receptor 1 (FLT1), podoplanin, hypoxia-inducible factor 1-alpha (HIF-1alpha), carbonic anhydrase 9 (CA IX), vascular endothelial growth factor C (VEGF-C)) were used to confirm imaging biomarker findings. Univariate and multivariate regression analyses were performed to differentiate less aggressive luminal from aggressive non-luminal (HER2-positive, triple negative) malignancies and assess the interplay between hypoxia and neoangiogenesis markers. Aggressive non-luminal cancers (n = 40) presented with significantly higher MRO2 (i.e., oxygen consumption), lower mitoPO2 values (i.e., hypoxia), lower MTI, and higher MVD than less aggressive cancers (n = 13). Data suggest that a model derived from OEF, mitoPO2, and MVD can predict tumor proliferation rate. This novel MRI approach, which can be easily implemented in routine breast MRI exams, aids in the non-invasive identification of aggressive breast cancer.

Development of a Non-invasive Assessment of Hypoxia and Neovascularization with Magnetic Resonance Imaging in Benign and Malignant Breast Tumors: Initial Results

PURPOSE

To develop a novel magnetic resonance imaging (MRI) approach for the noninvasive assessment of hypoxia and neovascularization in breast tumors.

PROCEDURES

In this IRB-approved prospective study, 20 patients with suspicious breast lesions (BI-RADS 4/5) underwent multiparametric breast MRI including quantitative BOLD (qBOLD) and vascular architecture mapping (VAM). Custom-made in-house MatLab software was used for qBOLD and VAM data postprocessing and calculation of quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO₂), and mitochondrial oxygen tension (mitoPO₂) to measure tissue hypoxia and neovascularization including vascular architecture including microvessel radius (VSI), density (MVD), and type (MTI). Histopathology was used as standard of reference. Appropriate statistics were performed to assess and compare correlations between MRI biomarkers for hypoxia and neovascularization.

RESULTS

qBOLD and VAM data with good quality were obtained from all patients with 13 invasive ductal carcinoma (IDC) and 7 benign breast tumors with a lesion diameter of at least 10 mm in all spatial directions. MRI biomarker maps of oxygen metabolism and neovascularization demonstrated intratumoral spatial heterogeneity with a broad range of biomarker values. Bulk tumor neovasculature consisted of draining venous microvasculature with slow flowing blood. High OEF and low mitoPO₂ were associated with low MVD and vice versa. The heterogeneous pattern of MRO₂ values showed spatial congruence with VSI. IDCs showed significantly higher MRO₂ (P = 0.007), lower mitoPO₂ (P = 0.021), higher MVD (P = 0.005), and lower (i.e., more pathologic) MTI (P = 0.001) compared with benign breast tumors. These results indicate that IDCs consume more oxygen and are more hypoxic and neovascularized than benign tumors.

CONCLUSIONS

We developed a novel MRI approach for the noninvasive assessment of hypoxia and neovascularization in benign and malignant breast tumors that can be easily integrated in a diagnostic MRI protocol and provides insight into intratumoral heterogeneity.

Predicting Glioblastoma Response to Bevacizumab Through MRI Biomarkers of the Tumor Microenvironment

PURPOSE

Glioblastoma (GB) is one of the most vascularized of all solid tumors and, therefore, represents an attractive target for antiangiogenic therapies. Many lesions, however, quickly develop escape mechanisms associated with changes in the tumor microenvironment (TME) resulting in rapid treatment failure. To prevent patients from adverse effects of ineffective therapy, there is a strong need to better predict and monitor antiangiogenic treatment response.

PROCEDURES

We utilized a novel physiological magnetic resonance imaging (MRI) method combining the visualization of oxygen metabolism and neovascularization for classification of five different TME compartments: necrosis, hypoxia with/without neovascularization, oxidative phosphorylation, and aerobic glycolysis. This approach, termed TME mapping, was used to monitor changes in tumor biology and pathophysiology within the TME in response to bevacizumab treatment in 18 patients with recurrent GB.

RESULTS

We detected dramatic changes in the TME by rearrangement of its compartments after the onset of bevacizumab treatment. All patients showed a decrease in active tumor volume and neovascularization as well as an increase in hypoxia and necrosis in the first follow-up after 3 months. We found that recurrent GB with a high percentage of neovascularization and active tumor before bevacizumab onset showed a poor or no treatment response.

CONCLUSIONS

TME mapping might be useful to develop strategies for patient stratification and response prediction before bevacizumab onset.

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.

Characterizing white matter fiber orientation effects on multi-parametric quantitative BOLD assessment of oxygen extraction fraction

Relative oxygen extraction fraction (rOEF) is a fundamental indicator of cerebral metabolic function. An easily applicable method for magnetic resonance imaging (MRI) based rOEF mapping is the multi-parametric quantitative blood oxygenation level dependent (mq-BOLD) approach with separate acquisitions of transverse relaxation times   T 2 * and T2 and dynamic susceptibility contrast (DSC) based relative cerebral blood volume (rCBV). Given that transverse relaxation and rCBV in white matter (WM) strongly depend on nerve fiber orientation, mq-BOLD derived rOEF is expected to be affected as well. To investigate fiber orientation related rOEF artefacts, we present a methodological study characterizing anisotropy effects of WM as measured by diffusion tensor imaging (DTI) on mq-BOLD in 30 healthy volunteers. Using a 3T clinical MRI-scanner, we performed a comprehensive correlation of all parameters (  T 2 * , T2, R 2 ' , rCBV, rOEF, where R 2 ' =1/  T 2 * -1/T2) with DTI-derived fiber orientation towards the main magnetic field (B0). Our results confirm strong dependencies of transverse relaxation and rCBV on the nerve fiber orientation towards B0, with anisotropy-driven variations up to 37%. Comparably weak orientation-dependent variations of mq-BOLD derived rOEF (3.8%) demonstrate partially counteracting influences of R 2 ' and rCBV effects, possibly suggesting applicability of rOEF as an oxygenation sensitive biomarker. However, unresolved issues warrant caution when applying mq-BOLD to WM.

Physiologic MR imaging of the tumor microenvironment revealed switching of metabolic phenotype upon recurrence of glioblastoma in humans

Treating recurrent glioblastoma (GB) is one of the challenges in modern neurooncology. Hypoxia, neovascularization, and energy metabolism are of crucial importance for therapy failure and recurrence. Twenty-one patients with initially untreated GB who developed recurrence were examined with a novel MRI approach for noninvasive visualization of the tumor microenvironment (TME). Imaging biomarker information about oxygen metabolism (mitochondrial oxygen tension) and neovascularization (microvascular density and type) were fused for classification of five different TME compartments: necrosis, hypoxia with/without neovascularization, oxidative phosphorylation, and glycolysis. Volume percentages of these TME compartments were compared between untreated and recurrent GB. At initial diagnosis, all 21 GB showed either the features of a glycolytic dominant phenotype with a high percentage of functional neovasculature (N = 12) or those of a necrotic/hypoxic dominant phenotype with a high percentage of defective tumor neovasculature (N = 9). At recurrence, all 21 GB revealed switching of the initial metabolic phenotype: either from the glycolytic to the necrotic/hypoxic dominant phenotype or vice-versa. A necrotic/hypoxic phenotype at recurrence was associated with a higher rate of multifocality of the recurrent lesions. Our MRI approach may be helpful for a better understanding of treatment-induced metabolic phenotype switching and for future studies developing targeted therapeutic strategies for recurrent GB.

Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent magnitude (qBOLD)-based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) mapping

PURPOSE

To improve the accuracy of QSM plus quantitative blood oxygen level-dependent magnitude (QSM + qBOLD or QQ)-based mapping of the oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using cluster analysis of time evolution (CAT).

METHODS

3D multi-echo gradient echo and arterial spin labeling images were acquired in 11 healthy subjects and 5 ischemic stroke patients. DWI was also carried out on patients. CAT was developed for analyzing signal evolution over TE. QQ-based OEF and CMRO2 were reconstructed with and without CAT, and results were compared using region of interest analysis and a paired t-test.

RESULTS

Simulations demonstrated that CAT substantially reduced noise error in QQ-based OEF. In healthy subjects, QQ-based OEF appeared less noisy and more uniform with CAT than without CAT; average OEF with and without CAT in cortical gray matter was 32.7 ± 4.0% and 37.9 ± 4.5%, with corresponding CMRO2 of 148.4 ± 23.8 and 171.4 ± 22.4 μmol/100 g/min, respectively. In patients, regions of low OEF were confined within the ischemic lesions defined on DWI when using CAT, which was not observed without CAT.

CONCLUSION

The cluster analysis of time evolution (CAT) significantly improves the robustness of QQ-based OEF against noise.

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.

Intratumoral heterogeneity of oxygen metabolism and neovascularization uncovers 2 survival-relevant subgroups of IDH1 wild-type glioblastoma

Background

The intratumoral heterogeneity of oxygen metabolism in combination with variable patterns of neovascularization (NV) as well as reprogramming of energy metabolism affects the landscape of tumor microenvironments (TMEs) in glioblastoma. Knowledge of the hypoxic and perivascular niches within the TME is essential for understanding treatment failure.

Methods

Fifty-two patients with untreated glioblastoma (isocitrate dehydrogenase 1 wild type [IDH1wt]) were examined with a physiological MRI protocol including a multiparametric quantitative blood oxygen level dependent (qBOLD) approach and vascular architecture mapping (VAM). Imaging biomarker information about oxygen metabolism (mitochondrial oxygen tension) and neovascularization (microvascular density and type) were fused for classification of 6 different TMEs: necrosis, hypoxia with/without neovascularization, oxidative phosphorylation (OxPhos), and glycolysis with/without neovascularization. Association of the different TME volume fractions with progression-free survival (PFS) was assessed using Kaplan-Meier analysis and Cox proportional hazards models.

Results

A common spatial structure of TMEs was detected: central necrosis surrounded by tumor hypoxia (with defective and functional neovasculature) and different TMEs with a predominance of OxPhos and glycolysis for energy production, respectively. The percentage of the different TMEs on the total tumor volume uncovered 2 clearly different subtypes of glioblastoma IDH1wt: a glycolytic dominated phenotype with predominantly functional neovasculature and a necrotic/hypoxic dominated phenotype with approximately 50% of defective neovasculature. Patients with a necrotic/hypoxic dominated phenotype showed significantly shorter PFS (P = 0.035).

Conclusions

Our non-invasive mapping approach allows for classification of the TME and detection of tumor-supportive niches in glioblastoma which may be helpful for both clinical patient management and research.

Vessel architecture imaging using multiband gradient-echo/spin-echo EPI

Objectives

To apply the MB (multiband) excitation and blipped-CAIPI (blipped-controlled aliasing in parallel imaging) techniques in a spin and gradient-echo (SAGE) EPI sequence to improve the slice coverage for vessel architecture imaging (VAI).

Materials and methods

Both MB excitation and blipped-CAIPI with in-plane parallel imaging were incorporated into a gradient-echo (GE)/spin-echo (SE) EPI sequence for simultaneous tracking of the dynamic MR signal changes in both GE and SE contrasts after the injection of contrast agent. MB and singleband (SB) excitation were compared using a 20-channel head coil at 3 Tesla, and high-resolution MB VAI could be performed in 32 glioma patients.

Results

Whole-brain covered high resolution VAI can be achieved after applying multiband excitation with a factor of 2 and in-plane parallel imaging with a factor of 3. The quality of the images resulting from MB acceleration was comparable to those from the SB method: images were reconstructed without any loss of spatial resolution or severe distortions. In addition, MB and SB signal-to-noise ratios (SNR) were similar. A relative low g-factor induced from the MB acceleration method was achieved after using a blipped-CAIPI technique (1.35 for GE and 1.33 for SE imaging). Performing quantitative VAI, we found that, among all VAI parametric maps, microvessel type indicator (MTI), distance map (I) and vascular-induced bolus peak-time shift (VIPS) were highly correlated. Likewise, VAI parametric maps of slope, slope length and short axis were highly correlated.

Conclusions

Multiband accelerated SAGE successfully doubles the number of readout slices in the same measurement time when compared to conventional readout sequences. The corresponding VAI parametric maps provide insights into the complexity and heterogeneity of vascular changes in glioma.

Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD

PURPOSE

To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization.

METHODS

Random combinations of OEF, deoxygenated blood volume ( ν ), R₂ , and nonblood magnetic susceptibility ( χ nb ) with each parameter following a Gaussian distribution that represented physiological gray matter and white matter values were used to simulate quantitative BOLD signals and QSM values. An ANN was trained with the simulated data with added Gaussian noise. The ANN was applied to multigradient echo brain data of 7 healthy subjects, and the reconstructed parameters and maps were compared to QN results using Student t test and Bland-Altman analysis.

RESULTS

Intersubject means and SDs of gray matter were OEF = 43.5 ± 0.8 %, R 2 = 13.5 ± 0.3 Hz, ν = 3.4 ± 0.1 %, χ nb = - 25 ± 5 ppb for ANN; and OEF = 43.8 ± 5.2 %, R 2 = 12.2 ± 0.8 Hz, ν = 4.2 ± 0.6 %, χ nb = - 39 ± 7 ppb for QN, with a significant difference ( P < 0.05 ) for R 2 , ν , and χ nb . For white matter, they were OEF = 47.5 ± 1.1 %, R 2 = 17.1 ± 0.4 Hz, ν = 2.5 ± 0.2 %, χ nb = - 38 ± 5 ppb for ANN; and OEF = 42.3 ± 5.6 %, R 2 = 16.7 ± 0.7 Hz, ν = 2.9 ± 0.3 %, χ nb = - 45 ± 9 ppb for QN, with a significant difference ( P < 0.05 ) for OEF and ν . ANN revealed more gray-white matter contrast but less intersubject variation in OEF than QN. In contrast to QN, the ANN reconstruction did not need an additional sequence for parameter initialization and took approximately 1 s rather than roughly 1 h.

CONCLUSION

ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.

Quantifying effects of radiotherapy-induced microvascular injury; review of established and emerging brain MRI techniques

Microvascular changes are increasingly recognised not only as primary drivers of radiotherapy treatment response in brain tumours, but also as an important contributor to short- and long-term (cognitive) side effects arising from irradiation of otherwise healthy brain tissue. As overall survival of patients with brain tumours is increasing, monitoring long-term sequels of radiotherapy-induced microvascular changes in the context of their potential predictive power for outcome, such as cognitive disability, has become increasingly relevant. Ideally, radiotherapy-induced significant microvascular changes in otherwise healthy brain tissue should be identified as early as possible to facilitate adaptive radiotherapy and to proactively start treatment to minimise the influence on these side-effects on the final outcome. Although MRI is already known to be able to detect significant long-term radiotherapy induced microvascular effects, more recently advanced MR imaging biomarkers reflecting microvascular integrity and function have been reported and might provide a more accurate and earlier detection of microvascular changes. However, the use and validation of both established and new techniques in the context of monitoring early and late radiotherapy-induced microvascular changes in both target-tissue and healthy tissue currently are minimal at best. This review aims to summarise the performance and limitations of existing methods and future opportunities for detection and quantification of radiotherapy-induced microvascular changes, as well as the relation of these findings with key clinical parameters.

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas

PURPOSE

Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions.

METHODS

Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive.

RESULTS

Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198).

CONCLUSION

Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.

Simultaneous pH-sensitive and oxygen-sensitive MRI of human gliomas at 3 T using multi-echo amine proton chemical exchange saturation transfer spin-and-gradient echo echo-planar imaging (CEST-SAGE-EPI)

PURPOSE

To introduce a new pH-sensitive and oxygen-sensitive MRI technique using amine proton CEST echo spin-and-gradient echo (SAGE) EPI (CEST-SAGE-EPI).

METHODS

pH-weighting was obtained using CEST estimations of magnetization transfer ratio asymmetry (MTRasym ) at 3 ppm, and oxygen-weighting was obtained using R2' measurements. Glutamine concentration, pH, and relaxation rates were varied in phantoms to validate simulations and estimate relaxation rates. The values of MTRasym and R2' in normal-appearing white matter, T2 hyperintensity, contrast enhancement, and macroscopic necrosis were measured in 47 gliomas.

RESULTS

Simulation and phantom results confirmed an increase in MTRasym with decreasing pH. The CEST-SAGE-EPI estimates of R2 , R2*, and R2' varied linearly with gadolinium diethylenetriamine penta-acetic acid concentration (R2  = 6.2 mM-1 ·sec-1 and R2* = 6.9 mM-1 ·sec-1 ). The CEST-SAGE-EPI and Carr-Purcell-Meiboom-Gill estimates of R2 (R2  = 0.9943) and multi-echo gradient-echo estimates of R2* (R2  = 0.9727) were highly correlated. T2 lesions had lower R2' and higher MTRasym compared with normal-appearing white matter, suggesting lower hypoxia and high acidity, whereas contrast-enhancement tumor regions had elevated R2' and MTRasym , indicating high hypoxia and acidity.

CONCLUSION

The CEST-SAGE-EPI technique provides simultaneous pH-sensitive and oxygen-sensitive image contrasts for evaluation of the brain tumor microenvironment. Advantages include fast whole-brain acquisition, in-line B0 correction, and simultaneous estimation of CEST effects, R2 , R2*, and R2' at 3 T.

Voxel-wise analysis of dynamic 18F-FET PET: a novel approach for non-invasive glioma characterisation

BACKGROUND

Glioma grading with dynamic ¹⁸F-FET PET (0-40 min p.i.) is typically performed by analysing the mean time-activity curve of the entire tumour or a suspicious area within a heterogeneous tumour. This work aimed to ensure a reader-independent glioma characterisation and identification of aggressive sub-volumes by performing a voxel-based analysis with diagnostically relevant kinetic and static ¹⁸F-FET PET parameters. One hundred sixty-two patients with a newly diagnosed glioma classified according to histologic and molecular genetic properties were evaluated. The biological tumour volume (BTV) was segmented in static 20-40 min p.i. ¹⁸F-FET PET images using the established threshold of 1.6 × background activity. For each enclosed voxel, the time-to-peak (TTP), the late slope (Slope_15-40), and the tumour-to-background ratios (TBR_5-15, TBR_20-40) obtained from 5 to 15 min p.i. and 20 to 40 min p.i. images were determined. The percentage portion of these values within the BTV was evaluated with percentage volume fractions (PVFs) and cumulated percentage volume histograms (PVHs). The ability to differentiate histologic and molecular genetic classes was assessed and compared to volume-of-interest (VOI)-based parameters.

RESULTS

Aggressive WHO grades III and IV and IDH-wildtype gliomas were dominated by a high proportion of voxels with an early peak, negative slope, and high TBR, whereby the PVHs with TTP < 20 min p.i., Slope_15-40 < 0 SUV/h, and TBR_5-15 and TBR_20-40 > 2 yielded the most significant differences between glioma grades. We found significant differences of the parameters between WHO grades and IDH mutation status, where the effect size was predominantly higher for voxel-based PVHs compared to the corresponding VOI-based parameters. A low overlap of BTV sub-volumes defined by TTP < 20 min p.i. and negative Slope_15-40 with TBR_5-15 > 2- and TBR_{20-40 > 2}-defined hotspots was observed.

CONCLUSIONS

The presented approach applying voxel-wise analysis of dynamic ¹⁸F-FET PET enables an enhanced characterisation of gliomas and might potentially provide a fast identification of aggressive sub-volumes within the BTV. Parametric 3D ¹⁸F-FET PET information as investigated in this study has the potential to guide individual therapy instrumentation and may be included in future biopsy studies.

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.