CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing

Purpose Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations. Methods Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2μT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3–4 ppm, amines: 1.5–2.5 ppm, amide/amine ratio) were calculated with two models: ‘asymmetry-based’ (AB) and ‘fluid-suppressed’ (FS). The presence of T2/FLAIR mismatch was noted. Results IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014). Conclusions CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.

Local detection of microvessels in IDH-wildtype glioblastoma using relative cerebral blood volume: an imaging marker useful for astrocytoma grade 4 classification

Background The microvessels area (MVA), derived from microvascular proliferation, is a biomarker useful for high-grade glioma classification. Nevertheless, its measurement is costly, labor-intense, and invasive. Finding radiologic correlations with MVA could provide a complementary non-invasive approach without an extra cost and labor intensity and from the first stage. This study aims to correlate imaging markers, such as relative cerebral blood volume (rCBV), and local MVA in IDH-wildtype glioblastoma, and to propose this imaging marker as useful for astrocytoma grade 4 classification. Methods Data from 73 tissue blocks belonging to 17 IDH-wildtype glioblastomas and 7 blocks from 2 IDH-mutant astrocytomas were compiled from the Ivy GAP database. MRI processing and rCBV quantification were carried out using ONCOhabitats methodology. Histologic and MRI co-registration was done manually with experts’ supervision, achieving an accuracy of 88.8% of overlay. Spearman’s correlation was used to analyze the association between rCBV and microvessel area. Mann-Whitney test was used to study differences of rCBV between blocks with presence or absence of microvessels in IDH-wildtype glioblastoma, as well as to find differences with IDH-mutant astrocytoma samples. Results Significant positive correlations were found between rCBV and microvessel area in the IDH-wildtype blocks (p < 0.001), as well as significant differences in rCBV were found between blocks with microvascular proliferation and blocks without it (p < 0.0001). In addition, significant differences in rCBV were found between IDH-wildtype glioblastoma and IDH-mutant astrocytoma samples, being 2–2.5 times higher rCBV values in IDH-wildtype glioblastoma samples. Conclusions The proposed rCBV marker, calculated from diagnostic MRIs, can detect in IDH-wildtype glioblastoma those regions with microvessels from those without it, and it is significantly correlated with local microvessels area. In addition, the proposed rCBV marker can differentiate the IDH mutation status, providing a complementary non-invasive method for high-grade glioma classification.

Identification of Methylation Immune Subtypes and Establishment of a Prognostic Signature for Gliomas Using Immune-Related Genes

DNA methylation patterns are essential in understanding carcinogenesis. However, the relationship between DNA methylation and the immune process has not been clearly established—this study aimed at elucidating the interaction between glioma and DNA methylation, consolidating glioma classification and prognosis. A total of 2,483 immune-related genes and 24,556 corresponding immune-related methylation probes were identified. From the Cancer Genome Atlas (TCGA) glioma cohort, a total of 683 methylation samples were stratified into two different clusters using unsupervised clustering, and eight types of other cancer samples from the TCGA database were shown to exhibit excellent distributions. A total of 3,562 differentially methylated probes (DMPs) were selected and used for machine learning. A five-probe signature was established to evaluate the prognosis of glioma as well as the potential benefits of radiotherapy and Procarbazine, CCNU, Vincristine (PCV) treatment. Other prognostic clinical models, such as nomogram and decision tree, were also evaluated. Our findings confirmed the interactions between immune-related methylation patterns and glioma. This novel approach for cancer molecular characterization and prognosis should be validated in further studies.

CEST MRI Provides Amides/amines Surrogate Biomarkers for Treatment-naïve Glioma Sub-typing

PurposeAccurate gliomas classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different Chemical Exchange Saturation Transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in gliomas subpopulations.MethodsForty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI at 3T. Magnetisation transfer ratio asymmetry and CEST metrics (amides: offset range 3-4ppm, amines: 1.5-2.5ppm, amides/amines ratio) were calculated with two models: ‘asymmetry-based’ (AB) and ‘fluid-suppressed’ (FS). Presence of T2/FLAIR mismatch was noted.ResultsIDH-wildtype had higher amides/amines ratio than IDH-mutant_1p/19qcodel (p<0.022). Amides/amines ratio and amines levels differentiated IDH-wildtype from IDH-mutant (p<0.0045) and from IDH-mutant_1p/19qret (p<0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p<0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch (p<0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p=0.014).ConclusionsCEST-derived biomarkers for amides, amines and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma sub-groups that may have prognostic and clinical relevance.

Advanced Diagnosis of Glioma by Using Emerging Magnetic Resonance Sequences

Glioma, the most common primary brain tumor in adults, can be difficult to discern radiologically from other brain lesions, which affects surgical planning and follow-up treatment. Recent advances in MRI demonstrate that preoperative diagnosis of glioma has stepped into molecular and algorithm-assisted levels. Specifically, the histology-based glioma classification is composed of multiple different molecular subtypes with distinct behavior, prognosis, and response to therapy, and now each aspect can be assessed by corresponding emerging MR sequences like amide proton transfer-weighted MRI, inflow-based vascular-space-occupancy MRI, and radiomics algorithm. As a result of this novel progress, the clinical practice of glioma has been updated. Accurate diagnosis of glioma at the molecular level can be achieved ahead of the operation to formulate a thorough plan including surgery radical level, shortened length of stay, flexible follow-up plan, timely therapy response feedback, and eventually benefit patients individually.

Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading

Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations.

Opposed Interplay between IDH1 Mutations and the WNT/β-Catenin Pathway: Added Information for Glioma Classification

Gliomas are the main common primary intraparenchymal brain tumor in the central nervous system (CNS), with approximately 7% of the death caused by cancers. In the WHO 2016 classification, molecular dysregulations are part of the definition of particular brain tumor entities for the first time. Nevertheless, the underlying molecular mechanisms remain unclear. Several studies have shown that 75% to 80% of secondary glioblastoma (GBM) showed IDH1 mutations, whereas only 5% of primary GBM have IDH1 mutations. IDH1 mutations lead to better overall survival in gliomas patients. IDH1 mutations are associated with lower stimulation of the HIF-1α a, aerobic glycolysis and angiogenesis. The stimulation of HIF-1α and the process of angiogenesis appears to be activated only when hypoxia occurs in IDH1-mutated gliomas. In contrast, the observed upregula aggressiveness and angiogenesis. Molecular pathways of the malignancy process are involved in early stages of WNT/β-catenin pathway-activated-gliomas, and this even under normoxic conditions. IDH1 mutations lead to decreased activity of the WNT/β-catenin pathway and its enzymatic targets. The opposed interplay between IDH1 mutations and the canonical WNT/β-catenin pathway in gliomas could participate in better understanding of the observed evolution of different tumors and could reinforce the glioma classification.