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乳腺癌组织病理学基于图像的基因表达预测使用空间转录组学数据和深度学习.

Md Mamunur Rahaman1, Ewan K A Millar2,3,4, Erik Meijering5

  • 1School of Computer Science and Engineering, University of New South Wales, Kensington, Sydney, NSW 2052, Australia.

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概括

使用深度学习从乳腺癌组织学图像中预测基因表达,为昂贵的空间转录组学提供了具有成本效益的替代方案. BrST-Net准确地预测基因表达,改善结果和治疗反应预测.

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科学领域:

  • 在瘤学瘤学.
  • 计算生物学 计算生物学
  • 医疗成像医学成像

背景情况:

  • 乳腺癌中的瘤异质性使治疗和预后复杂化.
  • 空间转录组学提供了详细的基因表达数据,但对于大型研究来说是昂贵的.
  • 血素和乙素 (H&E) 染色组织学图像为基因表达预测提供了一个负担得起的替代方案.

研究的目的:

  • 开发一个深度学习框架,BrST-Net,用于从基因病理图像预测基因表达.
  • 为此任务评估各种深度学习架构的性能.
  • 改善基因表达的预测,以获得更好的临床瘤学应用.

主要方法:

  • 开发了BrST-Net,这是一个使用空间转录组学数据的深度学习框架.
  • 训练和评估了四种架构:ResNet101,Inception-v3,EfficientNet和视觉变压器,没有预训练的权重.
  • 整合了一个辅助网络,以提高主预测网络的通用化性能.

主要成果:

  • 成功预测了250个基因的组织病理学图像的基因表达.
  • 对237个基因实现了正相关性,显著优于以前的方法.
  • 确定了24个基因,其中位相关系数大于0.50,这与之前的研究相比有了显著的改善.

结论:

  • BrST-Net提供了一种强大且具有成本效益的方法,用于从H&E染色乳腺癌图像中预测基因表达.
  • 这种方法可以通过克服空间转录组学的成本限制,促进大规模的临床瘤学研究.
  • 增强的预测准确性有望改善乳腺癌的结果和治疗反应预测.