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使用深度学习对脑瘤的直接图像到亚型预测.

Katherine J Hewitt1,2, Chiara M L Löffler1,2,3, Hannah Sophie Muti2,4

  • 1Department of Medicine III, University Hospital RWTH Aachen, Aachen, North Rhine-Westphalia, Germany.

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概括

深度学习模型可以直接从例行组织病理学幻灯片中预测关键的分子变化和世界卫生组织 (WHO) 的脑瘤亚型,帮助在分子测试有限的情况下进行诊断.

关键词:
IDH IDH 是一个字母.成人类型的扩散性质瘤深度学习是一种深度学习.分子签名 分子签名亚型子类型 亚型子类型

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科学领域:

  • 计算病理学计算病理学
  • 人工智能在瘤学中的应用
  • 神经瘤学神经瘤学

背景情况:

  • 2021年世界卫生组织 (WHO) 对脑瘤的分类整合了他的病理学和分子数据.
  • 深度学习 (DL) 在预测固体瘤组织病理学的分子变化方面表现有前途.
  • 准确的脑瘤亚型取决于组织学和分子特征.

研究的目的:

  • 调查DL能够预测分子变化和WHO脑瘤亚型从H&E染色的组织病理学幻灯片的能力.
  • 为了验证DL模型在独立患者队列上的性能.

主要方法:

  • 弱监督的深度学习 (DL) 适用于三个大脑瘤队列 (N=2845名患者).
  • 组织病理学幻灯片被用作DL模型培训和验证的输入.

主要成果:

  • 在训练队列中,DL准确预测了IDH突变 (AUROC 0.95),ATRX损失 (AUROC 0.90) 和1p19q代选择 (AUROC 0.80).
  • 外部验证证实了高预测性能:IDH (AUROC 0.90),ATRX (AUROC 0.79) 和1p19q代选择 (AUROC 0.87).这些代选择具有很高的预测性能.

结论:

  • DL模型可以从常规组织学中可靠地预测关键的分子变化和WHO脑瘤亚型.
  • 这些基于DL的方法可能会增强诊断工作流程,特别是当先进的分子测试无法使用时.