Title
Optimizing neuro-oncology imaging: A review of deep learning approaches for glioma imaging
Publication Date
2019
Journal Title
Cancers
Abstract
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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.
Volume Number
11
Issue Number
6
Document Type
Article
Status
Faculty
Facility
School of Medicine
Primary Department
Radiology
DOI
10.3390/cancers11060829
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