###### Abstract  
Low-grade gliomas are a heterogeneous group of infiltrative neoplasms. Radiomics allows the characterization of phenotypes with high-throughput extraction of quantitative imaging features from radiologic images. Deep learning models, such as convolutional neural networks (CNNs), offer well-performing models and a simplified pipeline by automatic feature learning. In our study, MRI data were retrospectively obtained from The Cancer Imaging Archive (TCIA), which contains MR images for a subset of the LGG patients in The Cancer Genome Atlas (TCGA). Corresponding molecular genetics and clinical information were obtained from TCGA. Three genes included in the genetic signatures were WEE1, CRTAC1, and SEMA4G. A CNN-based deep learning model was used to classify patients into low and high-risk groups, with the median gene signature risk score as the cut-off value. The data were randomly split into training and test sets, with 61 patients in the training set and 20 in the test set. In the test set, models using T1 and T2 weighted images had an area under the receiver operating characteristic curve of 73% and 79%, respectively. In conclusion, we developed a CNN-based model to predict non-invasively the risk stratification provided by the prognostic gene signature in LGGs. Numerous previously discovered gene signatures and novel genetic identifiers that will be developed in the future may be utilized with this method. deep learning; radiomics; neural networks; gliomas; LGG; gene signatures Article  
[PERSON], B.  
LGGs. However, the complex genetic mechanisms and heterogeneity of LGGs require more prognostic markers to explore better risk stratification strategies and efficient treatment modalities. Genetic signatures integrating multiple biomarkers are believed to be superior to single predictive biomarkers [12]. In recent years, gene signatures integrating multiple genes were proposed for LGGs. [PERSON] et al. reported a 3-gene (EMP3, GSX2, EMILIN3) prognostic signature [13]; [PERSON] et al. reported a 4-gene (EMP3, GNG12, KIF2C, IFI44) model [14]; and [PERSON] et al. constructed a 3-gene (WEE1, CRTAC1, SEMA4G) signature [12]. In the study by [PERSON] et al., the proposed model was proved to have better prognostic value in predicting 1-, 3- and 5-year OS rates than other models [12]. Within the limits of the current technology, genetic and molecular profiling of gliomas has a few drawbacks. First, it requires direct tumor sampling, which involves invasive procedures such as surgical resection or biopsy. Since the standard of care for LGGs include surgical removal of the tumor in many cases, it is not too much to say that the samples obtained during the surgery will not allow tailored surgical strategies [1]. Multiple biopsy-based approaches may allow more patient-specific treatment, but the diagnostic information that biopsy yields may be limited due to sampling error and intratumor heterogeneity [15, 16]. On the other hand, high cost and time-consuming remain serious shortcomings for the widespread clinical use of conventional genetic and molecular profiling methods [17, 18]. For these reasons, noninvasive determination of genetic and molecular profiles could allow more individualized treatment options and prognostication for the patients. Radiomics is a generic term to refer to the analytical technologies in medical imaging and the image features obtained by this approach. Research on the relationship between medical images and the genome is called 'radiogenomics'. Radiomics allow the characterization of imaging phenotypes with high-throughput extraction of quantitative imaging features from radiologic images [19]. Commonly, the radiomic approach includes image acquisition, preprocessing of the images, region of interest (ROI) delineation (segmentation), feature extraction, selection of the most relevant features, and building predictive models for the desired outcome [20]. Extracted radiomic features in many studies are pre-engineered or handcrafted, such as shape, intensity, texture, and wavelet features.