Fig. (IMAGE) Osaka University Caption MR images of gliomas were fed into a pre-trained deep neural network that can classify natural images. The activities of neurons in each layer of the network were used to characterize the textures of the MR images. These texture features were used to infer three key molecular subtypes of gliomas. The obtained algorithm was able to predict these molecular subtypes with an accuracy of 63.1%. Credit Osaka University Usage Restrictions None License Licensed content Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.