本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02995-8

摘要内容如下：

不同中枢神经系统（CNS）肿瘤类型的精确诊断对于最佳治疗至关重要。DNA甲基化图谱可以捕捉数千个CpG位点的甲基化状态，是提高诊断准确性的最先进的数据驱动手段，但也很耗时且不能广泛使用。在这里，为了解决这些限制，我们开发了组织病理学和甲基化的深度学习（Deploy），这是一个深度学习模型，将CNS肿瘤从组织病理学分类为十个主要类别。Deploy集成了三个不同的组件：第一个组件直接从玻片图像对CNS肿瘤进行分类（“直接模型”），第二个组件最初生成DNA甲基化β值的预测，随后用于肿瘤分类（“间接模型”），第三个组件直接从常规可用的患者人口统计数据对肿瘤类型进行分类。首先，我们发现Deploy可以从组织病理学图像中准确预测β值。其次，使用在1，796名患者的内部数据集上训练的十类模型，我们在包括2，156名患者的三个独立外部测试数据集中预测肿瘤类别，在预测具有高置信度的样本上实现了95%的总体准确度和91%的平衡准确度。这些结果展示了Deploy的潜在未来用途，以帮助病理学家在临床相关的短时间内诊断CNS肿瘤。

英文原文如下：

Abstracts

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.

-----------分割线---------

点击链接：https://www.mediecogroup.com/community/user/vip/categories/ ，成为医咖会员，获取12项专属权益。