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

ABSTRACT: 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 modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dim

SUBMITTER: Li Z 

PROVIDER: S-EPMC5511238 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature