多形性胶质母细胞瘤预后：MRI缺失模态生成、分割和放射基因组生存预测
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction
摘要：
多形性胶质母细胞瘤（GBM）的准确预后在制定相关手术和治疗方案中起着至关重要的作用。传统的生存预测模型依赖于使用磁共振成像（MRI）的放射组学特征。在本文中，我们提出了一种放射基因组总生存期（OS）预测方法，将基因表达数据与放射组学特征，如形状，几何形状和临床信息。我们利用TCGA（癌症基因组图谱）数据集，并在条件生成对抗网络（cGAN）中使用全卷积网络（FCN）合成缺失的MRI模式。同时，相同的FCN架构使得能够从可用的和合成的MRI模态进行肿瘤分割。所提出的FCN架构包括倍频程卷积（OctConv）和一种新颖的解码器，在空间和通道挤压和激励（skip-scSE）块中具有跳过连接。OctConv可以单独处理低频和高频特征，并通过减少通道冗余来提高模型效率。Skip-scSE应用空间和通道方式的激励来表示基本特征，并使用跳过连接来降低更深层学习参数的稀疏性。所提出的方法进行了评估，通过比较实验与国家的最先进的模型在合成，分割，和总生存期（OS）的预测。我们观察到，添加缺失的MRI模态改善了分割预测，并且基因标记物的表达水平在GBM预后预测中具有很高的贡献，并且融合的放射基因组学特征提高了OS估计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
