Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients' survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer. Graphical abstract A DCNN model was generated to accurately predict survival rates of brain cancer patients (classified in 4 different classes) accurately. After training the model using images from H&E stained tissue biopsies from The Cancer Genome Atlas database (TCGA, left), the model can predict for each patient, based on a histological image (top right), its survival class accurately (bottom right).

中文翻译：

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DeepSurvNet：基于组织病理学图像的脑癌生存率分类的深度生存卷积网络。


苏木精和伊红 (H&E) 染色的活检组织病理学全切片图像包含与癌症疾病及其临床结果相关的有价值的信息。尽管如此，还没有高度准确的自动化方法将组织病理学图像与脑癌患者的生存相关联，这可以帮助安排患者的治疗并分配临床前研究的时间以指导个性化治疗。我们现在提出了一种新的分类器，即由深度卷积神经网络提供支持的 DeepSurvNet，可根据组织病理学图像将脑癌患者的生存率准确分类为 4 类（I 类，0-6 个月；II 类，6-12 个月；II 类，6-12 个月）。 III 级，12-24 个月；IV 级，诊断后存活 >24 个月）。在公共脑癌数据集《癌症基因组图谱》上对 DeepSurvNet 模型进行训练和测试后，我们通过对未见样本的独立测试对其进行了概括。使用 DeepSurvNet，我们在上述数据集的测试阶段分别获得了 0.99 和 0.8 的精度，这表明 DeepSurvNet 是基于组织病理学图像的脑癌患者生存率分类的可靠分类器。最后，对突变频率的分析揭示了与每个类别相关的基因频率和类型的差异，支持了与患者生存相关的不同基因指纹的想法。我们得出的结论是，DeepSurvNet 构成了一种新的人工智能工具，用于评估脑癌的生存率。图解摘要 生成 DCNN 模型来准确预测脑癌患者（分为 4 个不同类别）的生存率。 使用癌症基因组图谱数据库（TCGA，左）中的 H&E 染色组织活检图像训练模型后，该模型可以根据组织学图像（右上）准确预测每位患者的生存类别（右下）。