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| Home > Publications database > Contrastive virtual staining enhances deep learning-based PDAC subtyping from H&E-stained tissue cores. |
| Home > Publications database > Contrastive virtual staining enhances deep learning-based PDAC subtyping from H&E-stained tissue cores. |
| Journal Article | DKFZ-2025-02277 |
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2026
Wiley
Bognor Regis [u.a.]
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Please use a persistent id in citations: doi:10.1002/path.6491
Abstract: Pancreatic ductal adenocarcinoma (PDAC) subtyping typically relies on immunohistochemistry (IHC) staining for critical markers like HNF1A and KRT81, a labor-intensive manual staining process that introduces variability. Virtual staining methods offer promising alternatives by generating synthetic IHC images from routine hematoxylin and eosin (H&E) slides. However, most current approaches evaluate success by image quality measures rather than assessing diagnostically relevant features. Here, we introduce a novel cycleGAN framework utilizing a contrastive-inspired approach trained on semipaired datasets derived from consecutive tissue sections. Our method significantly enhances PDAC subtyping accuracy based on synthetic IHC images generated from standard H&E inputs, improving the classification F1-score from 0.66 to 0.77 for KRT81 and from 0.61 to 0.73 for HNF1A, compared with classification directly on H&E images. This approach also substantially outperforms baseline CycleGAN models. These results underscore the clinical potential of contrastive virtual staining to streamline PDAC diagnostics and improve their robustness. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Keyword(s): CycleGAN ; H&E ; IHC ; PDAC subtyping ; contrastive learning ; digital pathology ; virtual staining
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