Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging.

• Type of Material: Article
• Authors: Madeleine M. Shaver, University of California Irvine; Paul A. Kohanteb, University of California Irvine; Catherine Chiou, University of California Irvine; Michelle D. Bardis, University of California Irvine; Chanon Chantaduly, University of California Irvine; Daniela Bota, University of California Irvine; Christopher G. Filippi, North Shore University Hospital; Brent Weinberg, Emory University; Jack Grinband, Columbia University; Daniel S. Chow, University of California Irvine; Peter D. Chang, University of California Irvine
• Language: English
• Date: 2019-06-14
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2019 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers11060829
• Title of Journal or Parent Work: Cancers
• ISSN: 2072-6694
• Volume: 11
• Issue: 6
• Grant/Funding Information: This research received no external funding.; The APC was funded by CANON MEDICAL SYSTEMS CORPORATION, grant title: “Machine Learning Classification of Glioblastoma Genetic Heterogeneity”.
• Keywords: machine learning; neural network; glioma; artificial intelligence; deep learning; glioblastoma
• Research Categories: Health Sciences, Oncology; Biology, Neuroscience

Abstract

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.