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 dimensionality greater than 1.6*10 4 were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images. Radiomics is an emerging method that uses a series of qualitative and quantitative analyses of high-throughput image features to obtain predictive or prognostic information from medical images 1 . Recently, radiomics methods have been used to analyze various medical images and have provided information related to patient outcomes, tumor phenotypes and the gene-protein signatures of different diseases. For instance, Aerts et al. presented a radiomics method to decode tumor phenotypes of both lung cancer and head-and-neck cancer based on computed tomography (CT) data 2 . That study showed that it is possible to improve cancer diagnosis and treatment by using routinely collected medical images. The features described in that study were divided into four groups: intensity, shape, texture and wavelet features. Then, the features with the best performance in each group were combined to establish the radiomics model. Kumar et al. summarized and demonstrated the processes and challenges of radiomics 3 with non-small-cell lung cancer (NSCLC). They proposed an NSCLC classification model that uses radiomics features. More recently, Vallieres et al. evaluated the lung metastasis risk of soft-tissue sarcomas (STSs) with a radiomics model 4 . Several non-textural features and texture features were extracted from the tumor regions in multiple medical scans. Their research showed that a combination of features extracted from positron emission computed tomography (PET) and magnetic resonance (MR) images could provide good metastasis estimation. Indeed, certain features in MR images were reported to exhibit characteristics associated with clinical diagnosis 5 . Gutman et al. analyzed the association between visual features of MR images and genetic alterations, gene expression and patient survival of glioblastoma (GBM) 6 . Visually Accessible Rembrandt Image (VASARI) features of the tumor regions from MR images were used in their study, and the results demonstrated a significant association between contrast-enhanced tumors and the Verhaak gene expression classification. A series of analyses of GBM was conducted by Gavaert et al. 7 . Gavaert et al. generated computational image features Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 2 Scientific RepoRts | 7: 5467 |
Li et al. REF proposed a six-layer CNN to segment tumors.
Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma
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