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一个BERT-GNN方法用于转移性乳腺癌预测使用组织病理学报告.

Abdullah Basaad1, Shadi Basurra1, Edlira Vakaj1

  • 1School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK.

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概括

这项研究引入了一种新的BERT-GNN模型,用于使用组织病理学报告预测非侵入性转移性乳腺癌 (MBC). 该模型在识别MBC患者方面实现了高精度,为改善癌症诊断提供了有前途的工具.

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 转移性乳腺癌 (MBC) 是女性癌症死亡的重要原因.
  • 准确和早期识别MBC对于有效的治疗和患者的结果至关重要.
  • 目前的诊断方法在非侵入性地检测癌症转移时可能存在局限性.

研究的目的:

  • 开发和评估一种创新的非侵入性分类模型,用于预测转移性乳腺癌 (MBC).
  • 利用大型语言模型 (LLM) 和图形神经网络 (GNN) 的力量来识别MBC患者.
  • 探索组织病理学报告在使用先进的人工智能技术预测MBC方面的实用性.

主要方法:

  • 开发了一种新的BERT-GNN方法 (BG-MBC),集成来自BERT嵌入的组织病理学报告的图形信息.
  • 节点是从患者医疗记录中构建的,BERT嵌入式是矢量化词汇表示来捕获语义信息.
  • 特性选择方法包括单变量选择,额外树分类器和沙普利值,从676个嵌入中确定了前30个关键特性.

主要成果:

  • BG-MBC模型表现出卓越的预测性能,检测率为0.98,曲线下的面积 (AUC) 为0.98.
  • 该模型有效地利用了LLM的注意力得分,以捕捉用于分类的组织病理学报告中的相关特征.
  • 该研究确定了对MBC预测有贡献的最有影响力的特征.

结论:

  • 开发的BERT-GNN模型 (BG-MBC) 显示出作为准确转移性乳腺癌预测的非侵入性工具的显著前景.
  • 将LLM和GNN与组织病理学报告分析相结合,为改善癌症诊断提供了一种强有力的方法.
  • 需要进一步的研究来验证这些发现,并探索这种创新的模型的临床应用.