Deep-Learning Based Virtual Stain Multiplexing Immunohistochemistry Slides – a Pilot Study
Keywords: Immunohistochemistry, Virtual Stain, Multiplexing, Deep Learning, Macrophages, cd68, NSCL
TL;DR: A novel deep-learning method for virtual stain multiplexing of IHC stains, allowing detection and differentiation of macrophages on PD-L1 IHC slides without additional tissue sections or stains, potentially improving pathologists' diagnostic accuracy
Abstract: In this paper, we introduce a novel deep-learning based method for virtual stain multiplexing of immunohistochemistry (IHC) stains. Traditional IHC techniques generally involve a single stain that highlights a single target protein, but this can be enriched with stain multiplexing. Our proposed method leverages sequential staining to train a model to virtually stain multiplex additional IHC on top of a digitally scanned whole slide image (WSI), without requiring a complex setup or any additional tissue sections and stains. To this end, we designed a novel model architecture, guided by the physical sequential staining process which provides superior performance. The model was optimized using a custom loss function that combines mean squared error (MSE) with semantic infor-mation, allowing the model to focus on learning the relevant differences between the input and ground truth. As an example application, we consider the problem of detecting macro-phages on PD-L1 IHC 22C3 pharmDx NSCLC WSIs. We demonstrated virtual stain multiplexing CD68 on top of PD-L1 22C3 pharmDx stained slides, which helps to detect macrophages and distinguish them from PD-L1+ tumor cells, which are often visually similar. Our pilot-study results showed significant improvement in a pathologist's ability to distinguish macrophages when using the virtually stain multiplexed CD68 decision supporting layer.
Submission Number: 11
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