Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images

• URL: http://arxiv.org/abs/2506.15853v1
• Date: Wed, 18 Jun 2025 20:01:14 GMT
• Title: Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images
• Authors: Amit Das, Naofumi Tomita, Kyle J. Syme, Weijie Ma, Paige O'Connor, Kristin N. Corbett, Bing Ren, Xiaoying Liu, Saeed Hassanpour,
• Abstract summary: HistoStainAlign predicts IHC staining patterns directly from H&E whole-slide images.<n>HistoStainAlign achieved weighted F1 scores of 0.735 [95% Confidence Interval (CI): 0.670-0.799], 0.830 [95% CI: 0.772-0.886], and 0.723 [95% CI: 0.607-0.836], respectively for these three IHC stains.
• Score: 4.650292435891902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hematoxylin and Eosin (H&E) staining is a cornerstone of pathological analysis, offering reliable visualization of cellular morphology and tissue architecture for cancer diagnosis, subtyping, and grading. Immunohistochemistry (IHC) staining provides molecular insights by detecting specific proteins within tissues, enhancing diagnostic accuracy, and improving treatment planning. However, IHC staining is costly, time-consuming, and resource-intensive, requiring specialized expertise. To address these limitations, this study proposes HistoStainAlign, a novel deep learning framework that predicts IHC staining patterns directly from H&E whole-slide images (WSIs) by learning joint representations of morphological and molecular features. The framework integrates paired H&E and IHC embeddings through a contrastive training strategy, capturing complementary features across staining modalities without patch-level annotations or tissue registration. The model was evaluated on gastrointestinal and lung tissue WSIs with three commonly used IHC stains: P53, PD-L1, and Ki-67. HistoStainAlign achieved weighted F1 scores of 0.735 [95% Confidence Interval (CI): 0.670-0.799], 0.830 [95% CI: 0.772-0.886], and 0.723 [95% CI: 0.607-0.836], respectively for these three IHC stains. Embedding analyses demonstrated the robustness of the contrastive alignment in capturing meaningful cross-stain relationships. Comparisons with a baseline model further highlight the advantage of incorporating contrastive learning for improved stain pattern prediction. This study demonstrates the potential of computational approaches to serve as a pre-screening tool, helping prioritize cases for IHC staining and improving workflow efficiency.

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