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

• 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)

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...