Contrastive virtual staining enhances deep learning-based PDAC subtyping from H&E-stained tissue cores.

Fischer, Maximilian; Muckenhuber, Alexander; Peretzke, Robin; Farah, Luay; Ulrich, Constantin; Ziegler, Sebastian; Schader, Philipp; Feineis, Lorenz; Gao, Hanno; Xiao, Shuhan; Götz, Michael; Nolden, Marco; Steiger, Katja; Sieveke, Jens T; Endrös, Lukas; Braren, Rickmer; Kleesiek, Jens; Schüffler, Peter; Neher, Peter; Maier-Hein, Klaus

doi:10.1002/path.6491

journal article

Titel:: Contrastive virtual staining enhances deep learning-based PDAC subtyping from H&E-stained tissue cores.
Dokumenttyp:: Journal Article
Autor(en):: Fischer, Maximilian; Muckenhuber, Alexander; Peretzke, Robin; Farah, Luay; Ulrich, Constantin; Ziegler, Sebastian; Schader, Philipp; Feineis, Lorenz; Gao, Hanno; Xiao, Shuhan; Götz, Michael; Nolden, Marco; Steiger, Katja; Sieveke, Jens T; Endrös, Lukas; Braren, Rickmer; Kleesiek, Jens; Schüffler, Peter; Neher, Peter; Maier-Hein, Klaus
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.
Zeitschriftentitel:: J Pathol
Jahr:: 2026
Band / Volume:: 268
Heft / Issue:: 1
Seitenangaben Beitrag:: 89-98
Volltext / DOI:: doi:10.1002/path.6491
PubMed:: http://view.ncbi.nlm.nih.gov/pubmed/41188199
Print-ISSN:: 0022-3417
TUM Einrichtung:: Institut für Allgemeine Pathologie und Pathologische Anatomie (Dr. Mogler komm.); Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski)
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