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Topics in Magnetic Resonance Imaging - Current Issue

Topics in Magnetic Resonance Imaging - Current Issue
  1. Untying the Knot: What Lies Behind the Image?
    No abstract available

  2. From K-space to Nucleotide: Insights Into the Radiogenomics of Brain Tumors
    imageAbstract: Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.

  3. A Comprehensive Review of Genomics and Noncoding RNA in Gliomas
    imageAbstract: Glioblastoma (GBM) is the most malignant primary adult brain tumor. In spite of our greater understanding of the biology of GBMs, clinical outcome of GBM patients remains poor, as their median survival with best available treatment is 12 to 18 months. Recent efforts of The Cancer Genome Atlas (TCGA) have subgrouped patients into 4 molecular/transcriptional subgroups: proneural, neural, classical, and mesenchymal. Continuing efforts are underway to provide a comprehensive map of the heterogeneous makeup of GBM to include noncoding transcripts, genetic mutations, and their associations to clinical outcome. In this review, we introduce key molecular events (genetic and epigenetic) that have been deemed most relevant as per studies such as TCGA, with a specific focus on noncoding RNAs such as microRNAs (miRNA) and long noncoding RNAs (lncRNA). One of our main objectives is to illustrate how miRNAs and lncRNAs play a pivotal role in brain tumor biology to define tumor heterogeneity at molecular and cellular levels. Ultimately, we elaborate how radiogenomics-based predictive models can describe miRNA/lncRNA-driven networks to better define heterogeneity of GBM with clinical relevance.

  4. Interrogating IDH Mutation in Brain Tumor: Magnetic Resonance and Hyperpolarization
    imageAbstract: Magnetic resonance spectroscopy (MRS) offers the possibility to noninvasively quantify 2HG concentration in the brain in the clinic, thereby serving as a valuable tool for patient-stratification as well as targeted treatment monitoring. Recently, hyperpolarized magnetic resonance techniques have opened up new opportunities for metabolic imaging not possible with conventional MRS in the brain. With over 10,000-fold increase in signal-to-noise ratio (SNR), dynamic metabolic processes can be interrogated in vivo with very high specificity by hyperpolarized MRI. In the following article, we will review relevant clinical studies and practical considerations of MRS and hyperpolarized MRS, as well as discuss some promising preclinical hyperpolarization studies to interrogate real-time metabolism in IDH mutations in vivo.

  5. Cell Signaling Pathways in Brain Tumors
    imageAbstract: Primary brain tumors, particularly glioblastoma, are associated with significant morbidity and are often recalcitrant to standard therapies. In recent years, brain tumors have been the focus of large-scale genomic sequencing efforts, providing unprecedented insight into the genomic aberrations and cellular signaling mechanisms that drive these cancers. Discoveries from these efforts have translated into novel diagnostic algorithms, biomarkers, and therapeutic strategies in Neuro-Oncology. However, the cellular mechanisms that drive brain tumors are heterogeneous and complex: applying this new knowledge to improve patient outcomes remains a challenge. Efforts to characterize and target these molecular vulnerabilities are evolving.

  6. Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images
    imageAbstract: Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other “omics” information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

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