H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking
Abstract 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 fo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01741-9 |
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| _version_ | 1849389328844718080 |
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| author | Pascal Klöckner José Teixeira Diana Montezuma João Fraga Hugo M. Horlings Jaime S. Cardoso Sara P. Oliveira |
| author_facet | Pascal Klöckner José Teixeira Diana Montezuma João Fraga Hugo M. Horlings Jaime S. Cardoso Sara P. Oliveira |
| author_sort | Pascal Klöckner |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-617f9b71b6b3464f951e2de73eef9b81 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-617f9b71b6b3464f951e2de73eef9b812025-08-20T03:42:00ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111810.1038/s41746-025-01741-9H&E to IHC virtual staining methods in breast cancer: an overview and benchmarkingPascal Klöckner0José Teixeira1Diana Montezuma2João Fraga3Hugo M. Horlings4Jaime S. Cardoso5Sara P. Oliveira6Computational Pathology Group, The Netherlands Cancer InstituteComputational Pathology Group, The Netherlands Cancer InstituteIMP DiagnosticsIMP DiagnosticsComputational Pathology Group, The Netherlands Cancer InstituteFaculty of Engineering, University of PortoComputational Pathology Group, The Netherlands Cancer InstituteAbstract 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.https://doi.org/10.1038/s41746-025-01741-9 |
| spellingShingle | Pascal Klöckner José Teixeira Diana Montezuma João Fraga Hugo M. Horlings Jaime S. Cardoso Sara P. Oliveira H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking npj Digital Medicine |
| title | H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking |
| title_full | H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking |
| title_fullStr | H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking |
| title_full_unstemmed | H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking |
| title_short | H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking |
| title_sort | h e to ihc virtual staining methods in breast cancer an overview and benchmarking |
| url | https://doi.org/10.1038/s41746-025-01741-9 |
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