Glioblastoma multiforme (GBM) is a very aggressive and infiltrative brain tumor with a high mortality rate. There are radiomic models with handcrafted features to estimate glioblastoma prognosis. In this work, we evaluate to what extent of combining genomic with radiomic features makes an impact on the prognosis of overall survival (OS) in patients with GBM. We apply a hypercolumn-based convolutional network to segment tumor regions from magnetic resonance images (MRI), extract radiomic features (geometric, shape, histogram), and fuse with gene expression profiling data to predict survival rate for each patient. Several state-of-the-art regression models such as linear regression, support vector machine, and neural network are exploited to conduct prognosis analysis. The Cancer Genome Atlas (TCGA) dataset of MRI and gene expression profiling is used in the study to observe the model performance in radiomic, genomic, and radiogenomic features. The results demonstrate that genomic data are correlated with the GBM OS prediction, and the radiogenomic model outperforms both radiomic and genomic models. We further illustrate the most significant genes, such as IL1B, KLHL4, ATP1A2, IQGAP2, and TMSL8, which contribute highly to prognosis analysis.

Our Proposed fully automated "Radiogenomic"" approach for survival prediction overview. It fuses geometric, intensity, volumetric, genomic and clinical information to predict OS.

多形胶质母细胞瘤（GBM）是一种非常具有侵略性的浸润性脑肿瘤，死亡率很高。有些放射线模型具有手工制作的功能，可以评估胶质母细胞瘤的预后。在这项工作中，我们评估了基因组学与放射学特征相结合的程度在多大程度上对GBM患者的总生存期（OS）的预后有影响。我们应用基于超列的卷积网络从磁共振图像（MRI）分割肿瘤区域，提取放射特征（几何形状，形状，直方图），并与基因表达谱数据融合以预测每个患者的存活率。利用几种最新的回归模型（例如线性回归，支持向量机和神经网络）进行预后分析。这项研究使用MRI和基因表达谱的癌症基因组图谱（TCGA）数据集来观察模型在放射学，基因组和放射基因组特征方面的表现。结果表明，基因组数据与GBM OS预测相关，并且放射性基因组模型的性能优于放射性和基因组模型。我们进一步说明了最重要的基因，例如IL1B，KLHL4，ATP1A2，IQGAP2和TMSL8，它们对预后分析做出了重要贡献。

我们建议的全自动“放射基因组学”方法用于生存预测概述，它融合了几何，强度，体积，基因组和临床信息以预测OS。