Artificial intelligence-based virtual staining platform for identifying tumor-associated macrophages from hematoxylin and eosin-stained images

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Link: https://doi.org/10.1016/j.ejca.2025.115390

Authors: Arpit Aggarwal, Mayukhmala Jana, Amritpal Singh, Tanmoy Dam, Himanshu Maurya, Tilak Pathak, Sandra Orsulic, Kailin Yang, Deborah Chute, Justin A Bishop, Farhoud Faraji, Wade M Thorstad, Shlomo Koyfman, Scott Steward, Qiuying Shi, Vlad Sandulache, Nabil F Saba, James S Lewis Jr, Germán Corredor, Anant Madabhushi

Abstract: Background: Virtual staining is an artificial intelligence-based approach that transforms pathology images between stain types, such as hematoxylin and eosin (H&E) to immunohistochemistry (IHC), providing a tissue-preserving and efficient alternative to traditional IHC staining. However, existing methods for translating H&E to virtual IHC often fail to generate images of sufficient quality for accurately delineating cell nuclei and IHC+ regions. To address these limitations, we introduce VISTA, an artificial intelligence-based virtual staining platform designed to translate H&E into virtual IHC. Methods: We applied VISTA to identify M2-subtype tumor-associated macrophages (M2-TAMs) in H&E images from 968 patients with HPV+ oropharyngeal squamous cell carcinoma across six institutional cohorts. M2-TAMs are a critical component of the tumor microenvironment, and their increased presence has been linked to poor survival. Co-registered H&E and CD163 + IHC tissue microarrays were used to train (D1, N = 102) and test (D2, N = 50) the VISTA platform. M2-TAM density, defined as the ratio of M2-TAMs to total nuclei, was derived from VISTA-generated CD163 + IHC images and evaluated for prognostic significance in additional training (D3, N = 360) and testing (D4, N = 456) cohorts using biopsy or resection H&E whole slide images. Results: High M2-TAM density was associated with worse overall survival in D4 (p = 0.0152, Hazard Ratio=1.63 [1.1–2.42]). VISTA outperformed existing methods, generating higher-quality virtual CD163 + IHC images in D2, with a Structural Similarity Index of 0.72, a Peak Signal-to-Noise Ratio of 21.5, and a Fréchet Inception Distance of 41.4. Additionally, VISTA demonstrated superior performance in segmenting M2-TAMs in D2 (Dice=0.74). Conclusion: These findings establish VISTA as a computational platform for generating virtual IHC and facilitating the discovery of novel biomarkers from H&E images.

Keywords: Virtual staining; Tumor microenvironment; Tumor-associated macrophages; H&E; Immunohistochemistry; HPV+ oropharyngeal squamous cell carcinoma