Genetics of glioblastoma: a window into its imaging and histopathologic variability

Targeting the Endocannabinoid System Present in the Glioblastoma Tumour Microenvironment as a Potential Anti-Cancer Strategy

The highly aggressive and invasive glioblastoma (GBM) tumour is the most malignant lesion among adult-type diffuse gliomas, representing the most common primary brain tumour in the neuro-oncology practice of adults. With a poor overall prognosis and strong resistance to treatment, this nervous system tumour requires new innovative treatment. GBM is a polymorphic tumour consisting of an array of stromal cells and various malignant cells contributing to tumour initiation, progression, and treatment response. Cannabinoids possess anti-cancer potencies against glioma cell lines and in animal models. To improve existing treatment, cannabinoids as functionalised ligands on nanocarriers were investigated as potential anti-cancer agents. The GBM tumour microenvironment is a multifaceted system consisting of resident or recruited immune cells, extracellular matrix components, tissue-resident cells, and soluble factors. The immune microenvironment accounts for a substantial volume of GBM tumours. The barriers to the treatment of glioblastoma with cannabinoids, such as crossing the blood-brain barrier and psychoactive and off-target side effects, can be alleviated with the use of nanocarrier drug delivery systems and functionalised ligands for improved specificity and targeting of pharmacological receptors and anti-cancer signalling pathways. This review has shown the presence of endocannabinoid receptors in the tumour microenvironment, which can be used as a potential unique target for specific drug delivery. Existing cannabinoid agents, studied previously, show anti-cancer potencies via signalling pathways associated with the hallmarks of cancer. The results of the review can be used to provide guidance in the design of future drug therapy for glioblastoma tumours.

Artificial Intelligence Applications in Glioma With 1p/19q Co-Deletion: A Systematic Review

As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.

Dural Tail Sign and Middle Meningeal Artery Hypertrophy in Glioblastoma: A Rarity?

BACKGROUND

Dural tail sign and increased caliber of branches of the external carotid artery (ECA) are common findings in meningioma and they have been rarely reported in intra-axial lesions. Anyway, some cases of glioblastoma (GBM) are reported in the literature, mostly superficially localized, characterized by these 2 findings and therefore, misdiagnosed with meningioma. The aim of this study is to verify the prevalence of dural tail sign and hypertrophy of middle meningeal artery (MMA) in a large cohort of GBMs.

METHODS

180 GBM patients were retrospectively evaluated. Deep or superficial localization of GBM was established and the presence of dural tail sign and hypertrophy of the ipsilateral MMA were assessed. The rate of tumor necrosis and the incidence of dural metastases during the radiological follow-up were also evaluated. Inter-rater reliability was calculated using Cohen's K-test.

RESULTS

Dural tail sign and enlarged MMA were evident in 30% and 19% of 96 superficial GBM, respectively. Deep GBM did not present those signs. Only one patient developed dural metastasis at follow-up and no differences in terms of tumor necrosis and hypoxic biomarkers expression were evident among GBMs with and without dural and vessel signs.

CONCLUSIONS

Dural tail sign and hypertrophy of the MMA in superficial GBM are more common than expected. They probably represent reactive rather than a neoplastic infiltration. Knowing these radiological signs may be important in terms of neurosurgery planning and avoiding excessive bleeding. Anyway, this hypothesis should be confirmed by a prospective neurosurgery studio.

Amide proton transfer MRI differentiates between progressive multifocal leukoencephalopathy and malignant brain tumors: a pilot study

BACKGROUND

Progressive multifocal leukoencephalopathy (PML) is a demyelinating disease of the central nerve system caused by the John Cunningham virus. On MRI, PML may sometimes appear similar to primary central nervous system lymphoma (PCNSL) and glioblastoma multiforme (GBM). The purpose of this pilot study was to evaluate the potential of amide proton transfer (APT) imaging for differentiating PML from PCNSL and GBM.

METHODS

Patients with PML (n = 4; two men; mean age 52.3 ± 6.1 years), PCNSL (n = 7; four women; mean age 74.4 ± 5.8 years), or GBM (n = 11; 6 men; mean age 65.0 ± 15.2 years) who underwent APT-CEST MRI between January 2021 and September 2022 were retrospectively evaluated. Magnetization transfer ratio asymmetry (MTR_asym) values were measured on APT imaging using a region of interest within the lesion. Receiver operating characteristics curve analysis was used to determine diagnostic cutoffs for MTR_asym.

RESULTS

The mean MTR_asym values were 0.005 ± 0.005 in the PML group, 0.025 ± 0.005 in the PCNSL group, and 0.025 ± 0.009 in the GBM group. There were significant differences in MTR_asym between PML and PCNSL (P = 0.023), and between PML and GBM (P = 0.015). For differentiating PML from PCNSL, an MTR_asym threshold of 0.0165 gave diagnostic sensitivity, specificity, positive predictive value, and negative predictive value of 100% (all). For differentiating PML from GBM, an MTR_asym threshold of 0.015 gave diagnostic sensitivity, specificity, positive predictive value, and negative predictive value of 100%, 90.9%, 80.0%, and 100%, respectively.

CONCLUSION

MTR_asym values obtained from APT imaging allowed patients with PML to be clearly discriminated from patients with PCNSL or GBM.

GSK343, an Inhibitor of Enhancer of Zeste Homolog 2, Reduces Glioblastoma Progression through Inflammatory Process Modulation: Focus on Canonical and Non-Canonical NF-κB/IκBα Pathways

Glioblastoma (GB) is a tumor of the central nervous system characterized by high proliferation and invasiveness. The standard treatment for GB includes radiotherapy and chemotherapy; however, new therapies are needed. Particular attention was given to the role of histone methyltransferase enhancer of zeste-homolog-2 (EZH2) in GB. Recently, several EZH2-inhibitors have been developed, particularly GSK343 is well-known to regulate apoptosis and autophagy processes; however, its abilities to modulate canonical/non-canonical NF-κB/IκBα pathways or an immune response in GB have not yet been investigated. Therefore, this study investigated for the first time the effect of GSK343 on canonical/non-canonical NF-κB/IκBα pathways and the immune response, by an in vitro, in vivo and ex vivo model of GB. In vitro results demonstrated that GSK343 treatments 1, 10 and 25 μM significantly reduced GB cell viability, showing the modulation of canonical/non-canonical NF-κB/IκBα pathway activation. In vivo GSK343 reduced subcutaneous tumor mass, regulating canonical/non-canonical NF-κB/IκBα pathway activation and the levels of reactive oxygen species (ROS), malondialdehyde (MDA), and superoxide dismutase (SOD). Ex vivo results confirmed the anti-proliferative effect of GSK343 and also demonstrated its ability to regulate immune response through CXCL9, CXCL10 and CXCL11 expression in GB. Thus, GSK343 could represent a therapeutic strategy to counteract GB progression, thanks to its ability to modulate canonical/non-canonical NF-κB/IκBα pathways and immune response.

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach

Background

Conventionally, identifying isocitrate dehydrogenase (IDH) mutation in gliomas is based on histopathological analysis of tissue specimens acquired via stereotactic biopsy or definitive resection. Accurate pre-treatment prediction of IDH mutation status using magnetic resonance imaging (MRI) can guide clinical decision-making. We aim to evaluate the diagnostic performance of deep learning (DL) to determine IDH mutation status in gliomas.

Methods

A systematic search of Cochrane Library, Web of Science, Medline, and Scopus was conducted to identify relevant publications until August 1, 2021. Articles were included if all the following criteria were met: (I) patients with histopathologically confirmed World Health Organization (WHO) grade II, III, or IV gliomas; (II) histopathological examination with the IDH mutation; (III) DL was used to predict the IDH mutation status; (IV) sufficient data for reconstruction of confusion matrices in terms of the diagnostic performance of the DL algorithms; and (V) original research articles. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was used to assess the studies' quality. Bayes theorem was utilized to calculate the posttest probability.

Results

Four studies with a total of 1,295 patients were included. In the training set, the pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were 93.9%, 90.9% and 0.958, respectively. In the validation set, the pooled sensitivity, specificity, and area under the SROC curve were 90.8%, 85.5% and 0.939, respectively. With a known pretest probability of 80.2%, the Bayes theorem yielded a posttest probability of 97.6% and 96.0% for a positive test and 27.0% and 30.6% for a negative test for training sets and validation sets, respectively.

Discussion

This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting IDH mutation status in gliomas via the Bayes theorem. DL algorithms demonstrate excellent diagnostic performance in predicting IDH mutation in gliomas. Radiomic features associated with IDH mutation, and its underlying pathophysiology extracted from advanced MRI may improve prediction probability. However, more studies are required to optimize and increase its reliability. Limitations include obtaining some data via email and lack of training and test sets statistics.

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

Clinical Applications of Artificial Intelligence, Machine Learning, and Deep Learning in the Imaging of Gliomas: A Systematic Review

In neuro-oncology, magnetic resonance imaging (MRI) is a critically important, non-invasive radiologic assessment technique for brain tumor diagnosis, especially glioma. Deep learning improves MRI image characterization and interpretation through the utilization of raw imaging data and provides unprecedented enhancement of images and representation for detection and classification through deep neural networks. This systematic review and quality appraisal method aim to summarize deep learning approaches used in neuro-oncology imaging to aid healthcare professionals. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a total of 20 low-risk studies on the established use of deep learning models to identify glioma genetic mutations and grading were selected, based on a Quality Assessment of Diagnostic Accuracy Studies 2 score of ≥9. The included studies provided the deep learning models used alongside their outcome measures, the number of patients, and the molecular markers for brain glioma classification. In 19 studies, the researchers determined that the deep learning model improved the clinical outcome and treatment protocol in patients with a brain tumor. In five studies, the authors determined the sensitivity of the deep learning model used, and in four studies, the authors determined the specificity of the models. Convolutional neural network models were used in 16 studies. In eight studies, the researchers examined glioma grading by using different deep learning models compared with other models. In this review, we found that deep learning models significantly improve the diagnostic and classification accuracy of brain tumors, particularly gliomas without the need for invasive methods. Most studies have presented validated results and can be used in clinical practice to improve patient care and prognosis.

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

PURPOSE

We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

METHODS

This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

RESULTS

Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

CONCLUSIONS

MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

Imaging Findings of New Entities and Patterns in Brain Tumor: Isocitrate Dehydrogenase Mutant, Isocitrate Dehydrogenase Wild-Type, Codeletion, and MGMT Methylation

Molecular features are now essential in distinguishing between glioma histologic subtypes. Currently, isocitrate dehydrogenase mutation, 1p19q codeletion, and MGMT methylation status play significant roles in optimizing medical and surgical treatment. Noninvasive pretreatment and post-treatment determination of glioma subtype is of great interest. Although imaging cannot replace the genetic panel at present, image findings have shown promising signs to identify and diagnose the types and subtypes of gliomas. This article details key imaging findings in the most common molecular glioma subtypes and highlights recent advances in imaging technologies to differentiate these lesions noninvasively.

Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis

Purpose

This study aimed to estimate the diagnostic accuracy of machine learning- (ML-) based radiomics in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG) and to identify potential covariates that could affect the diagnostic accuracy of ML-based radiomic analysis in classifying gliomas.

Method

A primary literature search of the PubMed database was conducted to find all related literatures in English between January 1, 2009, and May 1, 2020, with combining synonyms for “machine learning,” “glioma,” and “radiomics.” Five retrospective designed original articles including LGG and HGG subjects were chosen. Pooled sensitivity, specificity, their 95% confidence interval, area under curve (AUC), and hierarchical summary receiver-operating characteristic (HSROC) models were obtained.

Result

The pooled sensitivity when diagnosing HGG was higher (96% (95% CI: 0.93, 0.98)) than the specificity when diagnosing LGG (90% (95% CI 0.85, 0.93)). Heterogeneity was observed in both sensitivity and specificity. Metaregression confirmed the heterogeneity in sample sizes (p=0.05), imaging sequence types (p=0.02), and data sources (p=0.01), but not for the inclusion of the testing set (p=0.19), feature extraction number (p=0.36), and selection of feature number (p=0.18). The results of subgroup analysis indicate that sample sizes of more than 100 and feature selection numbers less than the total sample size positively affected the diagnostic performance in differentiating HGG from LGG.

Conclusion

This study demonstrates the excellent diagnostic performance of ML-based radiomics in differentiating HGG from LGG.

Main genetic differences in high-grade gliomas may present different MR imaging and MR spectroscopy correlates

OBJECTIVE

To assess whether the main genetic differences observed in high-grade gliomas (HGG) will present different MR imaging and MR spectroscopy correlates that could be used to better characterize lesions in the clinical setting.

METHODS

Seventy-nine patients with histologically confirmed HGG were recruited. Immunohistochemistry analyses for isocitrate dehydrogenase gene 1 (IDH1), alpha thalassemia mental retardation X-linked gene (ATRX), Ki-67, and p53 protein expression were performed. Tumour radiological features were examined on MR images. Metabolic profile and infiltrative pattern were assessed with MR spectroscopy. MR features were analysed to identify imaging-molecular associations. The Kaplan-Meier method and the Cox regression model were used to identify survival prognostic factors.

RESULTS

In total, 17.7% of the lesions were IDH1-mutated, 8.9% presented ATRX-mutated, 70.9% presented p53 unexpressed, and 22.8% had Ki-67 > 5%. IDH1 wild-type tumours had higher levels of mobile lipids (p = 0.001). The tumour-infiltrative pattern was higher in HGG with unexpressed p53 (p = 0.009). Mutated ATRX tumours presented higher levels of glutamate and glutamine (Glx) (p = 0.001). An association was observed between Glx tumour levels (p = 0.038) and Ki-67 expression (p = 0.008) with the infiltrative pattern. Survival analyses identified IDH1 status, age, and tumour choline levels as independent predictors of prognostic significance.

CONCLUSIONS

Our results suggest that IDH1-wt tumours are more necrotic than IDH1-mut. And that the presence of an infiltrative pattern in HGG is associated with loss of p53 expression, Ki-67 index, and Glx levels. Finally, tumour choline levels could be used as a predictive factor in survival in addition to the IDH1 status to provide a more accurate prediction of survival in HGG patients.

KEY POINTS

• IDH1-wt tumours present higher levels of mobile lipids than IDH1-mut. • Mutated ATRX tumours exhibit higher levels of glutamate and glutamine. • Loss of p53 expression, Ki-67 expression, and glutamate and glutamine levels may contribute to the presence of an infiltrative pattern in HGG.

Analysis of MRI Data in Diagnostic Neuroradiology

Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.

Clinical and Molecular Characterization of Adult Glioblastomas in Southern Brazil

We investigated 113 adult Brazilian patients with glioblastoma (GBM) for comparison with patients from distinct geographical areas and evaluation of suitability for novel targeted therapies. Patients were assessed for clinical features and tumor genomic characteristics such as ROS1 and NTRK1 rearrangements, KIT, PDGFRA, and KDR amplification, and RB1 deletion using multicolor fluorescence in situ hybridization. The majority of patients were male (53%), over 40 years (94%), with tumor located in single site (64%), in the right cerebral hemisphere (60%), and underwent partial resection (71%); 14% presented complications after surgery. The main clinical sign at diagnosis was focal abnormality (57%); frontal (31%); and temporal (20%) regions were most commonly affected. Median hospitalization time was 20 days, median survival was 175 days. One tumor was positive for rearrangement in NTRK1 and another in ROS1 (0.9% each). PDGFRA was amplified in 20% of cases, often co-amplified with KDR (>90%) and KIT (>60%). RB1 was deleted in 16% of patients. There was no association between these molecular abnormalities and patient survival. However, older age, complications after surgery, and right-sided tumors were independent variables associated with patient survival. This study contributes information on the molecular profile of glioblastomas in Latin America possibly supporting new target therapies.

Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas

OBJECTIVES

Prediction of progression-free survival (PFS) and overall survival (OS) and early identification of molecular biomarkers with prognostic information are clinically important in glioblastoma (GBM) patients. We aimed to explore the utility of arterial spin labeling perfusion-weighted imaging (ASL-PWI) in the prediction of molecular biomarkers and survival in GBM patients.

METHODS

We retrospectively analyzed 149 consecutive GBM patients, who had undergone maximal surgical resection or biopsy followed by concurrent chemoradiotherapy and adjuvant chemotherapy using temozolomide between November 2010 and June 2016. On preoperative ASL-PWI, cerebral blood flow (CBF) within contrast-enhancing (CE) and nonenhancing (NE) portions were evaluated both qualitatively (perfusion pattern_[CE] and perfusion pattern_[NE]) and quantitatively (nCBF_CE and nCBF_NE). ASL-PWI findings were correlated with molecular biomarkers, including isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) methylation statuses, and survival, using the Mann-Whitney U-test, Spearman rank correlation, Kaplan-Meier analysis, and receiver operating characteristics analysis.

RESULTS

nCBF_CE was significantly higher in the IDH wild-type group than in the IDH mutant group (p = .013) and in the MGMT unmethylated group than in the methylated group (p = .047). Areas under the receiver operating characteristic curve were 0.678 for IDH mutation (p = .022) and 0.601 for MGMT promoter methylation (p = .043). Hyperperfusion was associated with the shortest median PFS for both perfusion pattern_[CE] (7.6 months) and perfusion pattern_[NE] (4.0 months). The perfusion pattern_[NE] remained an independent predictor for PFS and OS even after adjusting for clinical and molecular predictors, unlike perfusion pattern_[CE].

CONCLUSIONS

ASL-PWI can aid to predict survival and molecular biomarkers including IDH mutation and MGMT promoter methylation statuses in GBM patients.

KEY POINTS

• ASL-PWI can aid to predict survival in GBM patients. • ASL-PWI can aid to predict IDH and MGMT promoter methylation statuses in GBM.

Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review

Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.

Volumetric assessment of glioblastoma and its predictive value for survival

BACKGROUND

The objective of this study was to evaluate the morphology of glioblastoma on structural pretreatment magnetic resonance imaging (MRI), defining imaging prognostic factors.

METHOD

We conducted a retrospective analysis of MR images from 114 patients harboring a primary glioblastoma, derived from two neurosurgical departments. Tumor segmentation was carried out in a semi-automated fashion. Tumor compartments comprised contrast-enhancing volume (CEV+), perifocal hyperintensity on fluid-attenuated inversion recovery (FLAIR) images (FLAIR+) excluding CEV+, and a non-enhancing area within the CEV+ lesion (CEV-). Additionally, two ratios were calculated from these volumes, the edema-tumor ratio (ETR) and necrosis-tumor ratio (NTR). All patients received surgical resection, followed by concomitant radiation and chemotherapy.

RESULTS

Tumor segmentation revealed the strongest correlation between the CEV+ volume and the CEV-, presenting intratumoral necrosis (p < 0.001). The relation between the tumor surrounding the FLAIR+ area and the CEV+ volume and the ETR is inversely correlated (p = 0.001). The most important prognostic factor in multivariable analysis was NTR (HR 2.63, p = 0.016). The cut-off value in our cohort for NTR was 0.33, equivalent to a decrease in survival if the necrotic core of the tumor (CEV-) accounts for more than 33% of the tumor mass itself (CEV+).

CONCLUSIONS

Our data emphasizes the importance of the necrosis-tumor ratio as a biomarker in glioblastoma imaging, rather than single tumor compartment volumes. NTR can help to identify a subset of tumors with a higher resistance to therapy and a dismal prognosis.

Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.

Glioblastoma heterogeneity and the tumour microenvironment: implications for preclinical research and development of new treatments

Glioblastoma is the deadliest form of brain cancer. Aside from inadequate treatment options, one of the main reasons glioblastoma is so lethal is the rapid growth of tumour cells coupled with continuous cell invasion into surrounding healthy brain tissue. Significant intra- and inter-tumour heterogeneity associated with differences in the corresponding tumour microenvironments contributes greatly to glioblastoma progression. Within this tumour microenvironment, the extracellular matrix profoundly influences the way cancer cells become invasive, and changes to extracellular (pH and oxygen levels) and metabolic (glucose and lactate) components support glioblastoma growth. Furthermore, studies on clinical samples have revealed that the tumour microenvironment is highly immunosuppressive which contributes to failure in immunotherapy treatments. Although technically possible, many components of the tumour microenvironment have not yet been the focus of glioblastoma therapies, despite growing evidence of its importance to glioblastoma malignancy. Here, we review recent progress in the characterisation of the glioblastoma tumour microenvironment and the sources of tumour heterogeneity in human clinical material. We also discuss the latest advances in technologies for personalised and in vitro preclinical studies using brain organoid models to better model glioblastoma and its interactions with the surrounding healthy brain tissue, which may play an essential role in developing new and more personalised treatments for this aggressive type of cancer.

Emerging Applications of Artificial Intelligence in Neuro-Oncology

Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

BACKGROUND AND PURPOSE

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

MATERIALS AND METHODS

MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification.

RESULTS

Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features.

CONCLUSIONS

Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.

Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma

OBJECTIVES

To assess the association between MR imaging features and major genomic profiles in glioblastoma.

METHODS

Qualitative and quantitative imaging features such as volumetrics and histogram analysis from normalised CBV (nCBV) and ADC (nADC) were evaluated based on both T2WI and CET1WI. The imaging parameters of different genetic profile groups were compared and regression analyses were used for identifying imaging-molecular associations. Progression-free survival (PFS) was analysed by a Kaplan-Meier test and Cox proportional hazards model.

RESULTS

An IDH mutation was observed in 18/176 patients, and ATRX loss was positive in 17/158 of the IDH-wt cases. The IDH-mut group showed a larger volume on T2WI and a higher volume ratio between T2WI and CET1WI than the IDH-wt group (p < 0.05). In the IDH-mut group, higher mean nADC values were observed compared with the IDH-wt tumours (p < 0.05). Among the IDH-wt tumours, IDH-wt, ATRX-loss tumours revealed higher 5th percentile nADC values than the IDH-wt, ATRX-noloss tumours (p = 0.03). PFS was the longest in the IDH-mut group, followed by the IDH-wt, ATRX-loss groups and the IDH-wt, ATRX-noloss groups, consecutively (p < 0.05). We found significant associations of PFS with the genetic profiles and imaging parameters.

CONCLUSION

Major genetic profiles of glioblastoma showed a significant association with MR imaging features, along with some genetic profiles, which are independent prognostic parameters for GBM.

KEY POINTS

• Significant correlation exists between radiological parameters such as volumetric and ADC values and major genomic profiles such as IDH mutation and ATRX loss status • Radiological parameters such as the ADC value were feasible predictors of glioblastoma patients' prognosis • Imaging features can predict major genomic profiles of the tumours and the prognosis of glioblastoma patients.

Imaging genomics of Glioblastoma: state of the art bridge between genomics and neuroradiology

Glioblastoma (GBM) is the most common and most aggressive primary malignant tumor of the central nervous system. Recently, researchers concluded that the "one-size-fits-all" approach for treatment of GBM is no longer valid and research should be directed toward more personalized and patient-tailored treatment protocols. Identification of the molecular and genomic pathways underlying GBM is essential for achieving this personalized and targeted therapeutic approach. Imaging genomics represents a new era as a noninvasive surrogate for genomic and molecular profile identification. This article discusses the basics of imaging genomics of GBM, its role in treatment decision-making, and its future potential in noninvasive genomic identification.

Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival

BACKGROUND

Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM.

METHODS

We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image.

RESULTS

Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively).

CONCLUSIONS

Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

OKN-007 decreases tumor necrosis and tumor cell proliferation and increases apoptosis in a preclinical F98 rat glioma model

BACKGROUND

Glioblastoma is a malignant World Health Organization (WHO) grade IV glioma with a poor prognosis in humans. New therapeutics are desperately required. The nitrone OKN-007 (2,4-disulfophenyl-PBN) has demonstrated effective anti-glioma properties in several rodent models and is currently being used as a clinical investigational drug for recurrent gliomas. We assessed the regional effects of OKN-007 in the tumor necrotic core and non-necrotic tumor parenchyma.

METHODS

An F98 rat glioma model was evaluated using proton magnetic resonance spectroscopy ((1) H-MRS), diffusion-weighted imaging (DWI), morphological T2-weighted imaging (T2W) at 7 Tesla (30 cm-bore MRI), as well as immunohistochemistry and microarray assessments, at maximum tumor volumes (15-23 days following cell implantation in untreated (UT) tumors, and 18-35 days in OKN-007-treated tumors).

RESULTS

(1) H-MRS data indicates that Lip0.9/Cho, Lip0.9/Cr, Lip1.3/Cho, and Lip1.3/Cr ratios are significantly decreased (all P < 0.05) in the OKN-007-treated group compared with UT F98 gliomas. The Cho/Cr ratio is also significantly decreased in the OKN-007-treated group compared with UT gliomas. In addition, the OKN-007-treated group demonstrates significantly lower ADC values in the necrotic tumor core and the nonnecrotic tumor parenchyma (both P < 0.05) compared with the UT group. There was also an increase in apoptosis following OKN-007 treatment (P < 0.01) compared with UT.

CONCLUSION

OKN-007 reduces both necrosis and tumor cell proliferation, as well as seems to mediate multiple effects in different tumor regions (tumor necrotic core and nonnecrotic tumor parenchyma) in F98 gliomas, indicating the efficacy of OKN-007 as an anti-cancer agent and its potential clinical use.

Modern brain tumor imaging

The imaging and clinical management of patients with brain tumor continue to evolve over time and now heavily rely on physiologic imaging in addition to high-resolution structural imaging. Imaging remains a powerful noninvasive tool to positively impact the management of patients with brain tumor. This article provides an overview of the current state-of-the art clinical brain tumor imaging. In this review, we discuss general magnetic resonance (MR) imaging methods and their application to the diagnosis of, treatment planning and navigation, and disease monitoring in patients with brain tumor. We review the strengths, limitations, and pitfalls of structural imaging, diffusion-weighted imaging techniques, MR spectroscopy, perfusion imaging, positron emission tomography/MR, and functional imaging. Overall this review provides a basis for understudying the role of modern imaging in the care of brain tumor patients.

Identification of intrinsic subtype-specific prognostic microRNAs in primary glioblastoma

Background

Glioblastoma multiforme (GBM) is the most malignant type of glioma. Integrated classification based on mRNA expression microarrays and whole–genome methylation subdivides GBM into five subtypes: Classical, Mesenchymal, Neural, Proneural-CpG island methylator phenotype (G-CIMP) and Proneural-non G-CIMP. Biomarkers that can be used to predict prognosis in each subtype have not been systematically investigated.

Methods

In the present study, we used Cox regression and risk-score analysis to construct respective prognostic microRNA (miRNA) signatures in the five intrinsic subtypes of primary glioblastoma in The Cancer Genome Atlas (TCGA) dataset.

Results

Patients who had high-risk scores had poor overall survival compared with patients who had low-risk scores. The prognostic miRNA signature for the Mesenchymal subtype (four risky miRNAs: miR-373, miR-296, miR-191, miR-602; one protective miRNA: miR-223) was further validated in an independent cohort containing 41 samples.

Conclusion

We report novel diagnostic tools for deeper prognostic sub-stratification in GBM intrinsic subtypes based upon miRNA expression profiles and believe that such signature could lead to more individualized therapies to improve survival rates and provide a potential platform for future studies on gene treatment for GBM.

The comparison of 13N-ammonia and 18F-FDG in the evaluation of untreated gliomas

OBJECTIVE

Noninvasive evaluation of glioma is of great help for clinical practice. In this study, we investigated the utility of 13N-ammonia in the evaluation of untreated gliomas and compared the results with that of 18F-FDG.

METHODS

Forty-five consecutive patients with final diagnosis of glioma were included in this study. PET/CT imaging was performed for all of them with both 18F-FDG and 13N-ammonia as tracers. Imaging results were analyzed by tumor-to-gray matter (T/G) ratios. Receiver operating characteristic curve analysis was conducted to determine the optimal T/G cutoff values of each tracer between low-grade and high-grade gliomas.

RESULTS

Forty-eight separate lesions were identified in all (grade II, n = 16; grade III, n = 12; and grade IV, n = 20). Twenty-nine out of 32 high-grade lesions (91%) showed higher uptakes than normal gray matter with N-ammonia in comparison with the result of 21 lesions (66%) with 18F-FDG. The optimal T/G cutoff values for 18F-FDG and 13N-ammonia were 0.64 and 0.86 separately with the area under each curve 0.910 and 0.943. The sensitivity and specificity of predicting high-grade gliomas with optimal cutoff values were 83% and 93% for 18F-FDG and 94% and 94% for 13N-ammonia, respectively.

CONCLUSION

13N-Ammonia is superior to 18F-FDG not only in separating low-grade gliomas from high-grade ones but also in the detection of high-grade gliomas for better tumor to normal gray matter contrast.

Imaging-based observational databases for clinical problem solving: the role of informatics

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.

Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging

BACKGROUND

Pseudoprogression is a treatment-related reaction with an increase in contrast-enhancing lesion size, followed by subsequent improvement. Differentiating tumor recurrence from pseudoprogression remains a problem in neuro-oncology.

PURPOSE

To validate the added value of arterial spin labeling (ASL), compared with dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) alone, in distinguishing early tumor progression from pseudoprogression in patients with newly diagnosed glioblastoma multiforme (GBM).

MATERIAL AND METHODS

We retrospectively evaluated 117 consecutive patients with newly diagnosed GBM who underwent surgical resection and concurrent chemoradiotherapy (CCRT) as standard treatment modality. Sixty-two patients who developed contrast-enhancing lesions were assessed by both ASL and DSC perfusion MRI and classified into groups of early tumor recurrence (n = 34) or pseudoprogression (n = 28) based on pathologic analysis or clinical-radiologic follow-up. We used a qualitative analysis and semi-quantitative grade system on the basis of the tumor perfusion signal intensity into those equal to white matter (grade I), gray matter (grade II), and blood vessels (grade III) on ASL imaging. ASL grade was correlated with histogram parameters derived from DSC perfusion MRI.

RESULTS

Pseudoprogression was observed in 15 (53.6%) patients with ASL grade I, 13 (46.4%) with grade II, and 0 (0%) with grade III, with early tumor progression observed in seven (20.6%) patients with ASL grade I, 11 (32.3%) with grade II, and 16 (47.1%) with grade III (P = 0.0022). DSC perfusion histogram parameters differed significantly among ASL grades. ASL grade was an independent predictor differentiating pseudoprogression from early tumor progression (odds ratio, 4.73; P = 0.0017). On qualitative review, adjunctive ASL produced eight (12.9%) more accurate results than DSC perfusion MRI alone.

CONCLUSION

ASL improves the diagnostic accuracy of DSC perfusion MRI in differentiating pseudoprogression from early tumor progression.