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

Aggarwal A, Jana M, Singh A, Dam T, Maurya H, Pathak T, Orsulic S, Yang K, Chute D, Bishop JA, Faraji F, Thorstad WM, Koyfman S, Steward S, Shi Q, Sandulache V, Saba NF, Lewis JS, Corredor G, Madabhushi A. Artificial intelligence-based virtual staining platform for identifying tumor-associated macrophages from hematoxylin and eosin-stained images. European journal of cancer (Oxford, England : 1990). 2025 May 2; 220:115390, DOI: 10.1016/j.ejca.2025.115390.

Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects.

If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/

VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address.    Search Dimensions for VA for this citation
* Don't have VA-internal network access or a VA email address? Try searching the free-to-the-public version of Dimensions

Search for Abstract from PubMed

---

Abstract:

BACKGROUND: Virtual staining is an artificial intelligence-based approach that transforms pathology images between stain types, such as hematoxylin and eosin (HandE) to immunohistochemistry (IHC), providing a tissue-preserving and efficient alternative to traditional IHC staining. However, existing methods for translating HandE 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 HandE into virtual IHC. METHODS: We applied VISTA to identify M2-subtype tumor-associated macrophages (M2-TAMs) in HandE 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 HandE 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 HandE 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 HandE images.