The IDH1 mutation is the most frequent somatic mutation in gliomas, and it has an important impact on the treatment outcome of gliomas. Clinically, the gold standard methods for the IDH mutation detection are the immunohistochemistry and gene sequencing techniques, whereas using the histopathology images of the glioma tissues for IDH mutation identification has not been reported. In this study, we propose a convolutional neural network (CNN) model that is trained on histopathology images of glioma samples using multiple instance learning (MIL), which links the benefits of the end-to-end classification power of the deep neural network with the MIL by aggregating the scores of the instances to the bag-level score. The attention layer is also implemented to facilitate the performance of the MIL aggregation. The results show that our MIL-based CNN model has achieved good performance in the classification of the IDH1 mutation in the glioma images, with the area under the curve of 0.84. Besides, several image segmentation strategies, CNN architectures, and MIL pooling operators have been implemented and analyzed to investigate the effect of these settings on the model performance. To our knowledge, it is the first study to identify the IDH1 mutation by using the histopathology images of the glioma tissues, providing a novel and insightful method for glioma IDH mutation diagnosis.

中文翻译：

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一种基于多实例学习的卷积神经网络模型，用于检测胶质瘤组织组织病理学图像中的 IDH1 突变。

该IDH1突变是胶质瘤是最常见的体细胞突变，且对脑胶质瘤的治疗结果产生重要影响。临床上，IDH突变检测的金标准方法是免疫组化和基因测序技术，而IDH使用胶质瘤组织的组织病理学图像突变鉴定尚未见报道。在这项研究中，我们提出了一个卷积神经网络 (CNN) 模型，该模型使用多实例学习 (MIL) 对神经胶质瘤样本的组织病理学图像进行训练，该模型将深度神经网络端到端分类能力的优势与通过将实例的分数聚合到包级分数来计算 MIL。还实施了注意力层以促进 MIL 聚合的性能。结果表明，我们基于MIL的CNN模型在IDH1的分类上取得了良好的性能胶质瘤图像中的突变，曲线下面积为0.84。此外，还实施并分析了几种图像分割策略、CNN 架构和 MIL 池化算子，以研究这些设置对模型性能的影响。据我们所知，这是首次利用胶质瘤组织的组织病理学图像识别IDH1突变，为胶质瘤IDH突变的诊断提供了一种新的、有见地的方法。