The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely on radiomic features using magnetic resonance imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS) prediction approach by incorporating gene expression data with radiomic features such as shape, geometry, and clinical information. We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional Generative Adversarial Network (cGAN). Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities. The proposed FCN architecture comprises octave convolution (OctConv) and a novel decoder, with skip connections in spatial and channel squeeze & excitation (skip-scSE) block. The OctConv can process low and high-frequency features individually and improve model efficiency by reducing channel-wise redundancy. Skip-scSE applies spatial and channel-wise excitation to signify the essential features and reduces the sparsity in deeper layers learning parameters using skip connections. The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction. We observe that adding missing MRI modality improves the segmentation prediction, and expression levels of gene markers have a high contribution in the GBM prognosis prediction, and fused radiogenomic features boost the OS estimation.
翻译:Glioblastoma 多种形式(GBM) 的准确预测在规划相关手术和治疗方面发挥着必不可少的作用。传统的生存预测模型依靠使用磁共振成像(MRI)的放射特征。在本文件中,我们建议采用放射性基因组总体生存预测方法,将基因表达数据与成形、几何和临床信息等放射特征结合起来。我们利用TTCGA(癌症基因组图集)数据集,在有条件的General Adversarial网络(cAN)中,利用一个完全同流网络(FCN)来综合缺失的MRI模式。与此同时,同样的FCN结构也利用现有和综合的MRI模型来帮助肿瘤分解现有和综合的MRI模式。 拟议的FCN结构包括进化(Oct Convil)和一个新型的脱coder,在空间和频道挤压和感光(skip-scSEE)块中跳过连接。OConvilable能单独处理低和高频特性,并通过减少频道的冗缺损作用来提高模型的效率。 Spedisc-SE-SEOS应用空间和频道的预估测测测测测测测测测测测测测地点,在GLLLLLLLVDLLLLLLLLLLLLD 的深度上,在比较的深度上,在比较的深度分析中,通过测测测测测测测地和降低了基础。