Magnetic resonance imaging (MRI) has become an important tool to study the correlation between the imaging phenotypes and the molecular profiles of Glioblastoma multiforme (GBM), the most frequent and lethal brain tumor. This type of study is named “Radiogenomics”. Currently, many radiogenomics studies segmented the tumors manually and then extracted hand-crafted MRI features for analysis. Automated segmentation approach as well as automatically learned features are urgently needed to release the burden of manual operation. In this study, we developed a predictive model, named DeepRA, based on deep imaging features and machine learning technologies to identify MRI signatures that enable accurate prediction of GBM molecular subtype and patient overall survival. We here for the first time used state-of-the-art deep imaging features to predict both molecular subtype and overall survival. We converted the high-dimensional deep feature representations to interpretable feature vector, selected the most distinguishing features and conducted the prediction. Experiments validated on The Cancer Genome Atlas (TCGA) data have shown that the DeepRA outperformed the traditional hand-crafted method. Also, compared with the regular convolutional neural networks used to segment tumors, the DeepRA presents a better performance, which shows the features extracted from DeepRA are more predictive. The implementation of the proposed method is available at https://github.com/RanSuLab/GBM-subtype-survival.

磁共振成像 (MRI) 已成为研究成像表型与多形性胶质母细胞瘤 (GBM) 分子谱之间相关性的重要工具，多形性胶质母细胞瘤是最常见和致命的脑肿瘤。这种类型的研究被称为“放射基因组学”。目前，许多放射基因组学研究手动分割肿瘤，然后提取手工制作的 MRI 特征进行分析。迫切需要自动分割方法以及自动学习的特征来减轻手动操作的负担。在这项研究中，我们开发了一个名为 DeepRA 的预测模型，该模型基于深度成像特征和机器学习技术来识别 MRI 特征，从而能够准确预测 GBM 分子亚型和患者总生存率。我们在这里首次使用最先进的深度成像特征来预测分子亚型和总体存活率。我们将高维深度特征表示转换为可解释的特征向量，选择最有区别的特征并进行预测。在癌症基因组图谱 (TCGA) 数据上验证的实验表明，DeepRA 优于传统的手工制作方法。此外，与用于分割肿瘤的常规卷积神经网络相比，DeepRA 表现出更好的性能，这表明从 DeepRA 提取的特征更具预测性。所提出方法的实现可在 https://github.com/RanSuLab/GBM-subtype-survival 获得。我们将高维深度特征表示转换为可解释的特征向量，选择最有区别的特征并进行预测。在癌症基因组图谱 (TCGA) 数据上验证的实验表明，DeepRA 优于传统的手工制作方法。此外，与用于分割肿瘤的常规卷积神经网络相比，DeepRA 表现出更好的性能，这表明从 DeepRA 提取的特征更具预测性。所提出方法的实现可在 https://github.com/RanSuLab/GBM-subtype-survival 获得。我们将高维深度特征表示转换为可解释的特征向量，选择最有区别的特征并进行预测。在癌症基因组图谱 (TCGA) 数据上验证的实验表明，DeepRA 优于传统的手工制作方法。此外，与用于分割肿瘤的常规卷积神经网络相比，DeepRA 表现出更好的性能，这表明从 DeepRA 提取的特征更具预测性。所提出方法的实现可在 https://github.com/RanSuLab/GBM-subtype-survival 获得。与用于分割肿瘤的常规卷积神经网络相比，DeepRA 表现出更好的性能，这表明从 DeepRA 提取的特征更具预测性。所提出方法的实现可在 https://github.com/RanSuLab/GBM-subtype-survival 获得。与用于分割肿瘤的常规卷积神经网络相比，DeepRA 表现出更好的性能，这表明从 DeepRA 提取的特征更具预测性。所提出方法的实现可在 https://github.com/RanSuLab/GBM-subtype-survival 获得。