Contrastive Virtual Staining Enhances Deep Learning-Based PDAC Subtyping From H&E-Stained Tissue Cores

MCML Authors

Peter Schüffler

Prof. Dr.

Core PI

Computational Pathology

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.

article FMP+26

---

Journal of Pathology

268.1. Jan. 2026.

---

Authors

M. Fischer • A. Muckenhuber • R. Peretzke • L. Farah • C. Ulrich • S. Ziegler • P. Schader • L. Feineis • H. Gao • S. Xiao • M. Götz • M. Nolden • K. Steiger • J. T. Sieveke • L. Endrös • R. Braren • J. Kleesiek • P. J. Schüffler • P. Neher • K. Maier-Hein

---

Links

DOI

---

Research Area

 C1 | Medicine