论文标题
深层神经网络整合基因组学和组织病理学图像，以预测结肠癌的阶段和生存时间
Deep Neural Networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer
论文作者
论文摘要
在预定义的结肠癌阶段存在无法解释的不同变化，仅使用基因组学或组织病理学全部幻灯片图像作为预后因素的特征。 Unraveling this variation will bring about improved in staging and treatment outcome, hence motivated by the advancement of Deep Neural Network libraries and different structures and factors within some genomic dataset, we aggregate atypical patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA Methylation as an integrative input source into an ensemble deep neural network for colon cancer stages classification and samples stratification into low or高风险生存群体。我们整体深度卷积神经网络模型的结果表明，在集成数据集上的阶段分类中的性能得到改善。当仅基因组和图像特征分别用于舞台分类时，曲线接收器操作特性曲线（AUC ROC）下的融合输入特征返回区域为0.95，而获得的AUC ROC为0.71和0.68。此外，提取的特征被用来将患者分为低风险生存组。在2548个融合功能中，1695个功能显示出由提取的特征定义的两个风险组之间具有统计学意义的生存概率差异。
There exists unexplained diverse variation within the predefined colon cancer stages using only features either from genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved in staging and treatment outcome, hence motivated by the advancement of Deep Neural Network libraries and different structures and factors within some genomic dataset, we aggregate atypical patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA Methylation as an integrative input source into an ensemble deep neural network for colon cancer stages classification and samples stratification into low or high risk survival groups. The results of our Ensemble Deep Convolutional Neural Network model show an improved performance in stages classification on the integrated dataset. The fused input features return Area under curve Receiver Operating Characteristic curve (AUC ROC) of 0.95 compared with AUC ROC of 0.71 and 0.68 obtained when only genomics and images features are used for the stage's classification, respectively. Also, the extracted features were used to split the patients into low or high risk survival groups. Among the 2548 fused features, 1695 features showed a statistically significant survival probability differences between the two risk groups defined by the extracted features.
