从组织学到诊断:利用病理基础模型进行质瘤分类
Camillo Saueressig1, Claire Delbridge2, Daniel Scholz1
1AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany; School of Computation, Information, and Technology, Technical University of Munich, Germany.
Computers in biology and medicine
|September 4, 2025
概括
计算病理学基础模型显示了从H&E图像进行质瘤分类的前景,减少了基因测试需求. 组合模型嵌入可以提高性能,但数据集多样性是通用化的关键.
科学领域:
- 计算病理学
- 数字病理学
- 在瘤学中使用人工智能
背景情况:
- 世界卫生组织对脑瘤的分类越来越依赖于基因检测来诊断质瘤.
- 计算病理学基础模型 (FMs) 提供了一个从H&E染色组织学图像中推断分子特征的潜在方法,可能减少对遗传分析的需求.
研究的目的:
- 评估五个基础模型在生成下游质瘤分类中的有效性.
- 评估这些基础模型在各种数据集中的性能和概括能力.
主要方法:
- 使用了三个不同的质瘤数据集 (TCGA,EBRAINS,TUM),分别包括839个,786个和250个样本.
- 使用最先进的数据增强技术来训练和评估基础模型.
- 研究了数据集多样性,组织含量,模型大小和数据集大小对模型性能和概括性的影响.
主要成果:
- 即使训练数据有限,基础模型嵌入也实现了具有竞争力的质瘤分类性能 (AUC > 0. 93).
- 在评估的基础模型中观察到下游性能,对干扰的稳定性和跨数据集的一致性.
- 数据集的多样性和中枢神经系统组织含量与改善的概括相关,而模型和数据集大小没有显著影响.
- 基础模型倾向于捕捉数据集特定的特征,并通过增强视图集成嵌入来增强下游分类器的性能.
结论:
- 计算病理学基础模型对质瘤分类具有重大承诺,为广泛的遗传测试提供了潜在的替代方案.
- 基础模型的性能和通用性受到训练数据的组成和下游增强技术的战略使用的关键影响.
- 解决特定数据集偏差和利用整体方法对于最大限度地利用数字病理学基础模型进行脑瘤诊断至关重要.
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