Topics in magnetic resonance imaging : TMRI
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This manuscript will review emerging applications of artificial intelligence, specifically deep learning, and its application to glioblastoma multiforme (GBM), the most common primary malignant brain tumor. Current deep learning approaches, commonly convolutional neural networks (CNNs), that take input data from MR images to grade gliomas (high grade from low grade) and predict overall survival will be shown. There will be more in-depth review of recent articles that have applied different CNNs to predict the genetics of glioma on pre-operative MR images, specifically 1p19q codeletion, MGMT promoter, and IDH mutations, which are important criteria for the diagnosis, treatment management, and prognostication of patients with GBM. Finally, there will be a brief mention of current challenges with DL techniques and their application to image analysis in GBM.
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Top Magn Reson Imaging · Apr 2020
ReviewAdult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know.
Recent advances in the understanding of the genetic makeup of gliomas have led to a paradigm shift in the diagnosis and classification of these tumors. Driven by these changes, the World Health Organization (WHO) introduced an update to its classification system of central nervous system (CNS) tumors in 2016. ⋯ Familiarity with this new classification scheme, the individual molecular markers, and corresponding imaging findings is critical for the radiologists who play an important role in diagnostic and surveillance imaging of patients with CNS tumors. The goals of this article are to review these updates to the WHO classification of CNS tumors with a focus on adult gliomas, provide an overview of key genomic markers of gliomas, and review imaging features pertaining to various genomic subgroups of adult gliomas.