Predicting the mutation status of genes in tumors is of great clinical significance. Recent studies have suggested that certain mutations may be noninvasively predicted by studying image features of the tumors from Computed Tomography (CT) data. Currently, this kind of image feature identification method mainly relies on manual processing to extract generalized image features alone or machine processing without considering the morphological differences of the tumor itself, which makes it difficult to achieve further breakthroughs. In this paper, we propose a pyramid focusing network (PFNet) for mutation prediction and classification based on CT images. Firstly, we use Space Pyramid Pooling to collect semantic cues in feature maps from multiple scales according to the observation that the shape and size of the tumors are varied.Secondly, we improve the loss function based on the consideration that the features required for proper mutation detection are often not obvious in cross-sections of tumor edges, which raises more attention to these hard examples in the network. Finally, we devise a training scheme based on data augmentation to enhance the generalization ability of networks. Extensively verified on clinical gastric CT datasets of 20 testing volumes with 63648 CT images, our method achieves the accuracy of 94.90% in predicting the HER-2 genes mutation status of at the CT image.

中文翻译：

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用于 CT 图像突变预测和分类的金字塔聚焦网络

预测肿瘤中基因的突变状态具有重要的临床意义。最近的研究表明，通过从计算机断层扫描 (CT) 数据中研究肿瘤的图像特征，可以无创地预测某些突变。目前，这种图像特征识别方法主要依靠人工处理单独提取广义图像特征或机器处理，不考虑肿瘤本身的形态差异，难以取得进一步突破。在本文中，我们提出了一种基于 CT 图像的用于突变预测和分类的金字塔聚焦网络（PFNet）。首先，我们根据对肿瘤形状和大小变化的观察，使用空间金字塔池化从多个尺度收集特征图中的语义线索。 其次，我们改进损失函数是基于考虑到正确的突变检测所需的特征在肿瘤边缘的横截面中往往不明显，这引起了对网络中这些困难示例的更多关注。最后，我们设计了一种基于数据增强的训练方案，以增强网络的泛化能力。在具有 63648 张 CT 图像的 20 个测试卷的临床胃 CT 数据集上进行了广泛验证，我们的方法在预测 CT 图像上的 HER-2 基因突变状态方面达到了 94.90% 的准确率。我们设计了一个基于数据增强的训练方案来增强网络的泛化能力。在具有 63648 张 CT 图像的 20 个测试卷的临床胃 CT 数据集上进行了广泛验证，我们的方法在预测 CT 图像上的 HER-2 基因突变状态方面达到了 94.90% 的准确率。我们设计了一个基于数据增强的训练方案来增强网络的泛化能力。在具有 63648 张 CT 图像的 20 个测试卷的临床胃 CT 数据集上进行了广泛验证，我们的方法在预测 CT 图像上的 HER-2 基因突变状态方面达到了 94.90% 的准确率。