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基于深度学习模型的架构用于肺瘤突变概况:系统性审查.

Samanta Ortuño-Miquel1,2, Reyes Roca1, Cristina Alenda3

  • 1Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain.

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概括

深度学习 (DL) 模型可以从H&E幻灯片中预测非小细胞肺癌 (NSCLC) 的分子变化. 虽然有希望,但需要进一步验证临床使用.

关键词:
深度学习是一种深度学习.可以解释性的解释性.图像的分类图像的分类.肺部瘤是一个肺部瘤.分子造型分析 (MOP) 是一种分子造型分析.

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科学领域:

  • 计算病理学计算病理学
  • 数字组织病理学 数字组织病理学
  • 人工智能在瘤学中的应用

背景情况:

  • 非小细胞肺癌 (NSCLC) 呈现出显著的异质性,使分子分析和治疗复杂化.
  • 深度学习 (DL) 提供了一种新的方法,可以从常规的血素和乙素 (H&E) 基因病理学图像中提取基因组洞察力.
  • 这补充了用于精密瘤学的传统下一代测序 (NGS).

研究的目的:

  • 系统地审查DL模型在预测NSCLC中分子变化的应用,使用H&E染色基因病理学幻灯片.
  • 评估当前DL方法在这个领域的性能和局限性.

主要方法:

  • 在主要科学数据库 (PubMed,Scopus,Web of Science) 进行了遵循PRISMA 2020指南的系统文献搜索.
  • 包括到2025年3月发表的利用DL用于NSCLC从H&E幻灯片的突变预测的研究.
  • 数据提取侧重于模型架构,数据集和性能指标.

主要成果:

  • 16项研究符合纳入标准,主要使用卷积神经网络 (CNN).
  • 模型经常使用癌症基因组图谱 (TCGA) 数据集进行训练,以预测EGFR,KRAS和TP53.3等突变.
  • 报告的预测性能 (曲线下的区域) 从0.65到0.95不等,表明有希望但不一致的结果.

结论:

  • 基于DL的组织病理学表明,与NSCLC中的分子签名相关联的组织形态具有显著的潜力.
  • 方法上的不一致,样本规模有限,外部验证不足,阻碍了临床转化.
  • 标准化,大型多中心研究和强大的验证对于未来的临床实施至关重要.