Deep Learning AI Applications in the Imaging of Glioma. Issue 2 (April 2020)

• Record Type: Journal Article
• Authors: Zlochower, Avraham; Chow, Daniel S.; Chang, Peter; Khatri, Deepak; Boockvar, John A.; Filippi, Christopher G.
• Is Part Of: Topics in magnetic resonance imaging. Volume 29:Issue 2(2020:Apr.)
• Publication Date: 2020-04
• Subjects: artificial intelligence -- deep learning -- genomics -- glioma -- MRI; Magnetic resonance imaging -- Periodicals; 616.07548
• DOI: 10.1097/RMR.0000000000000237
• Languages: English
• ISSNs: 0899-3459

Abstract

Abstract : Abstract: 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.