乳腺癌中苏木精-伊红染色至免疫组化的虚拟染色方法：综述与基准测试
H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking.
作者信息
Klöckner Pascal, Teixeira José, Montezuma Diana, Fraga João, Horlings Hugo M, Cardoso Jaime S, Oliveira Sara P
机构信息
Computational Pathology Group, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Faculty of Engineering, University of Porto, Porto, Portugal.
出版信息
NPJ Digit Med. 2025 Jul 2;8(1):384. doi: 10.1038/s41746-025-01741-9.
Immunohistochemistry (IHC) is crucial for the clinical categorisation of breast cancer cases. Deep generative models may offer a cost-effective alternative by virtually generating IHC images from hematoxylin and eosin samples. This review explores the state-of-the-art in virtual staining for breast cancer biomarkers (HER2, PgR, ER and Ki-67) and benchmarks several models on public datasets. It serves as a resource for researchers and clinicians interested in applying or developing virtual staining techniques.
免疫组织化学（IHC）对于乳腺癌病例的临床分类至关重要。深度生成模型或许能提供一种经济高效的替代方法，即通过苏木精和伊红样本虚拟生成免疫组织化学图像。本综述探讨了乳腺癌生物标志物（HER2、PgR、ER和Ki-67）虚拟染色的最新技术，并在公共数据集上对多个模型进行了基准测试。它为有兴趣应用或开发虚拟染色技术的研究人员和临床医生提供了参考资源。
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