Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial-Spectral Fusion Features.

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.

Survival outcome and predictors of WHO grade 2 and 3 insular gliomas: A classification based on the tumor spread.

OBJECTIVE: The study aimed to identify if clinical features and survival outcomes of insular glioma patients are associated with our classification based on the tumor spread. METHODS: Our study included 283 consecutive patients diagnosed with histological grade 2 and 3 insular gliomas. A new classification was proposed, and tumors restricted to the paralimbic system were defined as type 1. When tumors invaded the limbic system (referred to as the hippocampus and its surrounding structures in this study) simultaneously, they were defined as type 2. Tumors with additional internal capsule involvement were defined as type 3. RESULTS: Tumors defined as type 3 had a higher age at diagnosis (p = 0.002) and a higher preoperative volume (p < 0.001). Furthermore, type 3 was more likely to be diagnosed as IDH wild type (p < 0.001), with a higher rate of Ki-67 index (p = 0.015) and a lower rate of gross total resection (p < 0.001). Type 1 had a slower tumor growth rate than type 2 (mean 3.3%/month vs. 19.8%/month; p < 0.001). Multivariate Cox regression analysis revealed the extent of resection (HR 0.259, p = 0.004), IDH status (HR 3.694, p = 0.012), and tumor spread type (HR = 1.874, p = 0.012) as independent predictors of overall survival (OS). Tumor grade (HR 2.609, p = 0.008), the extent of resection (HR 0.488, p = 0.038), IDH status (HR 2.225, p = 0.025), and tumor spread type (HR 1.531, p = 0.038) were significant in predicting progression-free survival (PFS). CONCLUSION: The current study proposes a classification of the insular glioma according to the tumor spread. It indicates that the tumors defined as type 1 have a relatively better nature and biological characteristics, and those defined as type 3 can be more aggressive and refractory. Besides its predictive value for prognosis, the classification has potential value in formulating surgical strategies for patients with insular gliomas.

Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As an alternative, computer-assisted methods, particularly deep convolutional neural networks (DCNNs), have gained traction. This research paper explores the recent advancements in DCNNs for glioma grading using brain magnetic resonance images (MRIs) from 2015 to 2023. The study evaluated various DCNN architectures and their performance, revealing remarkable results with models such as hybrid and ensemble based DCNNs achieving accuracy levels of up to 98.91%. However, challenges persisted in the form of limited datasets, lack of external validation, and variations in grading formulations across diverse literature sources. Addressing these challenges through expanding datasets, conducting external validation, and standardizing grading formulations can enhance the performance and reliability of DCNNs in glioma grading, thereby advancing brain tumor classification and extending its applications to other neurological disorders.

An effective ensemble learning approach for classification of glioma grades based on novel MRI features.

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Glial and glioneuronal tumors: Navigating the complexity of evolving concepts and new classification.

The World Health Organization (WHO) published the 5th edition classification of tumors of central nervous system in 2021, commonly abbreviated as WHO CNS5, which became the new standard for brain tumor diagnosis and therapy. This edition dramatically impacted tumor diagnostics. In short it introduced new tumors, changed the names of previously recognized tumors, and modified the working definition of previously known tumors. The new system appears complex due to the integration of morphological and multiple molecular criteria. The most radical changes occurred in the field of glial and glioneuronal tumors, which constitutes the lengthy first chapter of this new edition. Herein we present an illustrative outline of the evolving concepts of glial and glioneuronal tumors. We also attempt to explain the rationales behind this substantial change in tumor classification and the challenges to update and integrate it into clinical practice. We aim to present a concise and precise roadmap to aid navigation through the intricate conceptual framework of glial and glioneuronal tumors in the context of WHO CNS5.

Identification and classification of glioma subtypes based on RNA-binding proteins.

BACKGROUND: Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. METHODS: RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. RESULTS: Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. CONCLUSIONS: The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.

PURPOSE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. METHODS: In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. RESULTS: The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. CONCLUSION: Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis.

BACKGROUND: Classifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types. METHODS: A systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity. RESULTS: Fifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI]: 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI: 0.85-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI: 0.97-1.00) and 0.94, (95% CI: 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI: 0.83-0.93) and 0.87 (95% CI: 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications: sensitivity of 0.99 (95% CI: 0.99-1.00) and specificity of 0.99 (95% CI: 0.98-1.00). CONCLUSIONS: ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.

Major Changes in 2021 World Health Organization Classification of Central Nervous System Tumors.

The World Health Organization (WHO) published the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5) in 2021, as an update of the WHO central nervous system (CNS) classification system published in 2016. WHO CNS5 was drafted on the basis of recommendations from the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) and expounds the classification scheme of the previous edition, which emphasized the importance of genetic and molecular changes in the characteristics of CNS tumors. Multiple newly recognized tumor types, including those for which there is limited knowledge regarding neuroimaging features, are detailed in WHO CNS5. The authors describe the major changes introduced in WHO CNS5, including revisions to tumor nomenclature. For example, WHO grade IV tumors in the fourth edition are equivalent to CNS WHO grade 4 tumors in the fifth edition, and diffuse midline glioma, H3 K27M-mutant, is equivalent to midline glioma, H3 K27-altered. With regard to tumor typing, isocitrate dehydrogenase (IDH)-mutant glioblastoma has been modified to IDH-mutant astrocytoma. In tumor grading, IDH-mutant astrocytomas are now graded according to the presence or absence of homozygous CDKN2A/B deletion. Moreover, the molecular mechanisms of tumorigenesis, as well as the clinical characteristics and imaging features of the tumor types newly recognized in WHO CNS5, are summarized. Given that WHO CNS5 has become the foundation for daily practice, radiologists need to be familiar with this new edition of the WHO CNS tumor classification system. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. ©RSNA, 2022.

Analysis of the Prognosis of High-Grade gliomas in the View of New Immunohistochemistry Markers and 2016 WHO Classification.

AIM: To evaluate isocitrate dehydrogenase (IDH) mutation status and Ki67 percentages of tumors that were treated in our institution to determine whether these markers affected the initial diagnosis and survival rates. MATERIAL AND METHODS: High-grade glioma patients, who were operated in our department between 2013 and 2018, were enrolled in the study and retrospectively reviewed. New immunohistochemistry staining studies were conducted and survival analyses were performed. RESULTS: Of 135 patients and 136 tumors, 117 were glioblastoma multiforme (GBM), 8 were grade III-IV glioma, 4 were anaplastic astrocytoma and 7 were anaplastic oligodendroglioma. One patient had two different lesions, which were GBM and anaplastic astrocytoma respectively. Mean age was 55 (7-85) years, and 88 (65%) were male and 47 (35%) were female. The most common complaint was motor deficit (56%). Fourteen patients underwent reoperation due to recurrent disease. Tumors were most commonly found in the frontal lobe (53, 39%). Magnetic resonance imaging (MRI) features showed that existence of necrosis is strongly related to GBM (p < 0.01). Approximately 126 patients were found to be IDH-wildtype, which changed 6 patients? diagnosis to GBM, IDH wildtype from grade III-IV glioma. Five patients, who were diagnosed with anaplastic astrocytoma and anaplastic oligodendroglioma initially were found to be IDH wildtype. IDH mutation status, extend of resection, and age were found to affect survival. CONCLUSION: IDH mutation status is important in classifying high-grade gliomas, as well as its effects on prognosis. This mutation is related to several radiological features of tumors. Extent of resection and patient age are also profoundly affect survival. Detailing the diagnosis with molecular features will help physicians to shape targeted adjuvant therapies, which would better outcomes.

Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes.

Based on alterations in gene expression associated with the production of glycolysis and cholesterol, this research classified glioma into prognostic metabolic subgroups. In this study, data from the CGGA325 and The Cancer Genome Atlas (TCGA) datasets were utilized to extract single nucleotide variants (SNVs), RNA-seq expression data, copy number variation data, short insertions and deletions (InDel) mutation data, and clinical follow-up information from glioma patients. Glioma metabolic subtypes were classified using the ConsensusClusterPlus algorithm. This study determined four metabolic subgroups (glycolytic, cholesterogenic, quiescent, and mixed). Cholesterogenic patients had a higher survival chance. Genome-wide investigation revealed that inappropriate amplification of MYC and TERT was associated with improper cholesterol anabolic metabolism. In glioma metabolic subtypes, the mRNA levels of mitochondrial pyruvate carriers 1 and 2 (MPC1/2) presented deletion and amplification, respectively. Differentially upregulated genes in the glycolysis group were related to pathways, including IL-17, HIF-1, and TNF signaling pathways and carbon metabolism. Downregulated genes in the glycolysis group were enriched in terpenoid backbone biosynthesis, nitrogen metabolism, butanoate metabolism, and fatty acid metabolism pathway. Cox analysis of univariate and multivariate survival showed that risks of glycolysis subtypes were significantly higher than other subtypes. Those results were validated in the CGGA325 dataset. The current findings greatly contribute to a comprehensive understanding of glioma and personalized treatment.

T2-FLAIR mismatch sign: a roadmap of pearls and pitfalls.

T2-FLAIR mismatch sign has been advocated to be 100% specific for IDH-mutant 1p/19q non-codeleted gliomas (diffuse astrocytomas). However, false positives have been reported in recent works. Loose application of the criteria may lead to erroneous classification, especially by non-trained neuroradiologists. In this pictorial essay, we aim to bring attention to the need for strict criteria for the application of T2-FLAIR mismatch sign and to discuss the potential pitfalls in the application of these criteria. For that, a series of adult brain tumour cases are presented to demonstrate how to apply this radiological sign in the clinical practice.

Glioma-related epilepsy in patients with diffuse high-grade glioma after the 2016 WHO update: seizure characteristics, risk factors, and clinical outcomes.

OBJECTIVE: The aim of this study was to investigate the epidemiological characteristics, associated risk factors, and prognostic value of glioma-related epilepsy in patients with diffuse high-grade gliomas (DHGGs) that were diagnosed after the 2016 updated WHO classification was released. METHODS: Data from 449 patients with DHGGs were retrospectively collected. Definitive diagnosis was reaffirmed according to the 2016 WHO classification. Seizure outcome was assessed using the Engel classification at 12 months after surgery. Univariate and multivariate analyses were performed to identify risk factors associated with preoperative and postoperative glioma-related epilepsy. Lastly, the prognostic value of glioma-related epilepsy was evaluated by Kaplan-Meier and Cox analysis. RESULTS: The incidence of glioma-related epilepsy decreased gradually as the malignancy of the tumor increased. Age < 45 years (OR 2.601, p < 0.001), normal neurological function (OR 3.024, p < 0.001), and lower WHO grade (OR 2.028, p = 0.010) were independently associated with preoperative glioma-related epilepsy, while preoperative glioma-related epilepsy (OR 7.554, p < 0.001), temporal lobe involvement (OR 1.954, p = 0.033), non-gross-total resection (OR 2.286, p = 0.012), and lower WHO grade (OR 2.130, p = 0.021) were identified as independent predictors of poor seizure outcome. Furthermore, postoperative glioma-related epilepsy, rather than preoperative glioma-related epilepsy, was demonstrated as an independent prognostic factor for overall survival (OR 0.610, p = 0.010). CONCLUSIONS: The updated WHO classification seems conducive to reveal the distribution of glioma-related epilepsy in DHGG patients. For DHGG patients with high-risk predictors of poor seizure control, timely antiepileptic interventions could be beneficial. Moreover, glioma-related epilepsy (especially postoperative glioma-related epilepsy) is associated with favorable overall survival.

Application of multiplex ligation-dependent probe amplification (MLPA) and low pass whole genome sequencing (LP-WGS) to the classification / characterisation of low grade glioneuronal tumours.

AIMS: Glioneuronal tumours, although rare, are an important cause of treatment-resistant epilepsy. Differential diagnosis of glioneuronal tumour subtypes is complicated by their variable histological appearance and the lack of pathognomonic histological or molecular biomarkers. In this study we have applied techniques available in a diagnostic laboratory setting to characterise molecular and cytogenetic abnormalities in a single institution cohort of glioneuronal tumours. METHODS: A cohort of 29 glioneuronal tumours that included 21 gangliogliomas and 5 dysembryoplastic neuroepithelial tumours (DNETs) was analysed using low pass whole genome sequencing (WGS) and 2 multiplex ligation-dependent probe amplification (MLPA) central nervous system (CNS) tumour probesets. RESULTS: Low pass WGS identified chromosomal or subchromosomal alterations in 15 specimens. The most common chromosomal alterations were gains of chromosome 7 (n = 8) and chromosome 16 (n = 3). The BRAFV600E mutation was detected by MLPA in 9/21 (42.9%) gangliogliomas and 2/2 pleomorphic xanthoastrocytomas (PXAs). Chromosome 7 gains detected by WGS were reflected in MLPAs by overall gains of chromosome 7 gene probes, including those for BRAF, KIAA1549 and EGFR, while an internal BRAF/MKRN1 duplication was detected in a single ganglioglioma. Homozygous CDKN2A/B loss was detected by MLPA in 3 gangliogliomas, with p16 immunohistochemistry supporting these results. CONCLUSIONS: The combination of low pass WGS and MLPA identifies multiple, diverse genetic and chromosomal alterations in glioneuronal tumours, irrespective of histological tumour grade.

TMEM2 Combined with IDH and 1p19q in Refining Molecular Subtypes for Predicting Survival of Patients with Glioma.

Gliomas are common intracranial tumors with high morbidity and mortality in adults. Transmembrane protein 2 (TMEM2) is involved in the malignant behavior of solid tumors. TMEM2 regulates cell adhesion and metastasis as well as intercellular communication by degrading nonprotein components of the extracellular matrix. This study aimed to evaluate the relationship between TMEM2 expression levels and glioma subtypes or patient prognosis. Our findings revealed that TMEM2 expression was abnormally upregulated in high-grade glioma. Moreover, combining TMEM2, the status of isocitrate dehydrogenase (IDH) and 1p19q, we subdivided molecular subtypes with significant differences in survival. Patients in the MT-codel-low subgroup had better prognosis than those in the WT-no-codel-high subgroup, who fared the worst. Additionally, correlation analysis of TMEM2 and immune cell infiltration indicated an altered tumor microenvironment (TME) and cell redistribution in the TMEM2 high-expression subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that focal adhesion and PI3K-Akt signaling pathways were enriched in the TMEM2-expressing group. In conclusion, aberrant TMEM2 expression can be used as an independent prognostic marker for refining glioma molecular subtyping and accurate prognosis. These findings will improve rational decision making to provide individualized therapy for patients with glioma.

Differential genes and scoring criteria among immunogenomic clusters of lower-grade gliomas.

High incidence of recurrency had been a significant threat among glioma patients. Moreover, the performance of traditional therapies among recurrent gliomas was far from satisfying. Advances in the tumor microenvironment (TME) and immune responses on the brain inspired immunotherapy researches. Nevertheless, verification of classic PD-1/PD-L1 inhibitors failed in phase III clinical trials. Additional gene targets were required for future studies among glioma patients. Immune cell infiltration (ICI) scores, defined based on multiple prognostic genes, were proved as the marker for the sensitivity of immunotherapies in many tumors. However, relevant results were not reported in gliomas. In the study, a retrospective cohort of 495 patients was classified into two ICI score subgroups. High ICI scores were closely related to high tumor mutation burden (TMB) values, indicating a high instability of genes. Furthermore, ICI scores were proved as reliable prognostic predictors. And a predictive model was built based on the ICI scores and multiple clinical features. The model showed its superiority through both internal validation and external validation. The ICI scores and the predictive model showed significant clinical values through decision curve analysis (DCA) since high ICI scores were related to high sensitivity for treatment. The prognostic immune-related gene list provided targets for immunotherapy researches.

Molecular subtype impacts surgical resection in low-grade gliomas: A Chinese Glioma Genome Atlas database analysis.

Surgeons have considered extending the resection margins for better outcomes in gliomas, but have not considered molecular pathology. We investigated the impact of molecular pathology on the surgical benefit in gliomas. Herein, we collected the clinical and pathological information of 449 patients with glioma from the Chinese Glioma Genome Atlas database, and enrolled those who underwent surgical resection. We measured the impact of the extent of resection on survival time in subgroups classified by clinical characteristics. We found that gross total resection (GTR) was associated with longer survival times in the entire cohort, and each of the three molecular subtypes. Even after age stratification, there was no survival benefit from GTR in those with a Karnofsky performance score (KPS) ≤ 80. In patients aged >45 years with a KPS >80, extensive resection resulted in longer survival times in isocitrate dehydrogenase-mutated astrocytomas. Additionally, GTR was associated with longer overall survival times in patients aged ≤45 years with a KPS >80. In conclusion, extensive resection does not always prolong survival in patients with glioma. Along with clinical characteristics, molecular pathology positively impacts survival in gliomas. Neurosurgeons may consider our findings when planning surgery in the future.

Diffuse midline gliomas, H3 K27M-mutant are associated with less peritumoral edema and contrast enhancement in comparison to glioblastomas, H3 K27M-wildtype of midline structures.

PURPOSE: The entity 'diffuse midline glioma, H3 K27M-mutant (DMG)' was introduced in the revised 4th edition of the 2016 WHO classification of brain tumors. However, there are only a few reports on magnetic resonance imaging (MRI) of these tumors. Thus, we conducted a retrospective survey focused on MRI features of DMG compared to midline glioblastomas H3 K27M-wildtype (mGBM-H3wt). METHODS: We identified 24 DMG cases and 19 mGBM-H3wt patients as controls. After being retrospectively evaluated for microscopic evidence of microvascular proliferations (MVP) and tumor necrosis by two experienced neuropathologists to identify the defining histological criteria of mGBM-H3wt, the samples were further analyzed by two experienced readers regarding imaging features such as shape, peritumoral edema and contrast enhancement. RESULTS: The DMG were found in the thalamus in 37.5% of cases (controls 63%), in the brainstem in 50% (vs. 32%) and spinal cord in 12.5% (vs. 5%). In MRI and considering MVP, DMG were found to be by far less likely to develop peritumoral edema (OR: 0.13; 95%-CL: 0.02-0.62) (p = 0.010). They, similarly, were associated with a significantly lower probability of developing strong contrast enhancement compared to mGBM-H3wt (OR: 0.10; 95%-CL: 0.02-0.47) (P = 0.003). CONCLUSION: Despite having highly variable imaging features, DMG exhibited markedly less edema and lower contrast enhancement in MRI compared to mGBM-H3wt. Of these features, the enhancement level was associated with evidence of MVP.

The impact of the molecular classification of glioblastoma on the interpretation of therapeutic clinical trial results.

In 2016, the World Health Organization (WHO) released the most recent update to the classification of central nervous system tumors. This update has led to the reshaping of tumor identification and subsequently changed current understanding of treatment options for patients. Moreover, the restructuring of the classification of central nervous system tumors to include molecular markers has led to the need to re-evaluate how to interpret pivotal trials. These trials originally enrolled patients purely based upon histologic diagnoses without the use of adjunctive, and frequently diagnostic molecular testing. With this new paradigm also comes the need to assess how one should incorporate molecular markers into current trials as well as shape future trials. First, we will discuss updates on the molecular classification of glioblastoma (GBM) (and its histologic mimics). This will be followed by a review of key pivotal trials which have defined our standard of care for glioblastoma within the context of molecular classification of their study populations. This will be followed by preliminary results of ongoing phase 3 cooperative group trials for high-grade gliomas that were initiated prior to routine molecular classification of tumors and how one could interpret these results in light of advances in molecular classification. Finally, we will end with suggestions for future clinical trial design with a focus on enrollment based upon molecular diagnostics.