Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma

Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A...

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Bibliographic Details
Published in:	Scientific reports Vol. 7; no. 1; pp. 5467 - 11
Main Authors:	Li, Zeju, Wang, Yuanyuan, Yu, Jinhua, Guo, Yi, Cao, Wei
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 14.07.2017 Nature Publishing Group Nature Portfolio
Subjects:	119/118 45/77 631/67/1857 631/67/1922 639/166/985 639/705/117 692/53/2422 Brain tumors Deep learning Glioma Humanities and Social Sciences Image processing Information processing Isocitrate dehydrogenase Magnetic resonance imaging multidisciplinary Neural networks Radiomics Science Science (multidisciplinary)
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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