深度学习可直接从胃肠道癌症的组织学预测微卫星不稳定性。
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.
机构信息
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
出版信息
Nat Med. 2019 Jul;25(7):1054-1056. doi: 10.1038/s41591-019-0462-y. Epub 2019 Jun 3.
Abstract
Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.
摘要
微卫星不稳定性决定了胃肠道癌症患者对免疫疗法的反应是否异常良好。然而，在临床实践中，并非每个患者都接受 MSI 检测，因为这需要额外的基因或免疫组织化学检测。在这里，我们展示了深度学习可以直接从普遍可用的 H&E 组织学预测 MSI。这种方法有可能为更多胃肠道癌症患者提供免疫治疗。
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