Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908754432229376 |
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| author | Zhang, Yijie Isil, Cagatay Yang, Xilin Li, Yuzhu Elia, Anna Atlan, Karin Wallace, William Dean Pillar, Nir Ozcan, Aydogan |
| author_facet | Zhang, Yijie Isil, Cagatay Yang, Xilin Li, Yuzhu Elia, Anna Atlan, Karin Wallace, William Dean Pillar, Nir Ozcan, Aydogan |
| contents | Immunohistochemistry (IHC) has transformed clinical pathology by enabling the visualization of specific proteins within tissue sections. However, traditional IHC requires one tissue section per stain, exhibits section-to-section variability, and incurs high costs and laborious staining procedures. While multiplexed IHC (mIHC) techniques enable simultaneous staining with multiple antibodies on a single slide, they are more tedious to perform and are currently unavailable in routine pathology laboratories. Here, we present a deep learning-based virtual multiplexed immunostaining framework to simultaneously generate ERG and PanCK, in addition to H&E virtual staining, enabling accurate localization and interpretation of vascular invasion in thyroid cancers. This virtual mIHC technique is based on the autofluorescence microscopy images of label-free tissue sections, and its output images closely match the histochemical staining counterparts (ERG, PanCK and H&E) of the same tissue sections. Blind evaluation by board-certified pathologists demonstrated that virtual mIHC staining achieved high concordance with the histochemical staining results, accurately highlighting epithelial cells and endothelial cells. Virtual mIHC conducted on the same tissue section also allowed the identification and localization of small vessel invasion. This multiplexed virtual IHC approach can significantly improve diagnostic accuracy and efficiency in the histopathological evaluation of vascular invasion, potentially eliminating the need for traditional staining protocols and mitigating issues related to tissue loss and heterogeneity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment Zhang, Yijie Isil, Cagatay Yang, Xilin Li, Yuzhu Elia, Anna Atlan, Karin Wallace, William Dean Pillar, Nir Ozcan, Aydogan Medical Physics Computer Vision and Pattern Recognition Machine Learning Immunohistochemistry (IHC) has transformed clinical pathology by enabling the visualization of specific proteins within tissue sections. However, traditional IHC requires one tissue section per stain, exhibits section-to-section variability, and incurs high costs and laborious staining procedures. While multiplexed IHC (mIHC) techniques enable simultaneous staining with multiple antibodies on a single slide, they are more tedious to perform and are currently unavailable in routine pathology laboratories. Here, we present a deep learning-based virtual multiplexed immunostaining framework to simultaneously generate ERG and PanCK, in addition to H&E virtual staining, enabling accurate localization and interpretation of vascular invasion in thyroid cancers. This virtual mIHC technique is based on the autofluorescence microscopy images of label-free tissue sections, and its output images closely match the histochemical staining counterparts (ERG, PanCK and H&E) of the same tissue sections. Blind evaluation by board-certified pathologists demonstrated that virtual mIHC staining achieved high concordance with the histochemical staining results, accurately highlighting epithelial cells and endothelial cells. Virtual mIHC conducted on the same tissue section also allowed the identification and localization of small vessel invasion. This multiplexed virtual IHC approach can significantly improve diagnostic accuracy and efficiency in the histopathological evaluation of vascular invasion, potentially eliminating the need for traditional staining protocols and mitigating issues related to tissue loss and heterogeneity. |
| title | Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment |
| topic | Medical Physics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.16209 |