Evidence map›Paper›PMID 40158294›Full record

ArticleEuropean journal of cancer (Oxford, England : 1990)2025

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

Arpit Aggarwal, Mayukhmala Jana, Amritpal Singh, Tanmoy Dam, Himanshu Maurya, Tilak Pathak, Sandra Orsulic, Kailin Yang, Deborah Chute, Justin A Bishop and 10 more

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In one paragraph

Article in European journal of cancer (Oxford, England : 1990), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
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  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Arpit AggarwalDepartment of Biomedical Engineering, Georgia Tech, GA, USA; Department of Biomedical Engineering, Emory University, GA, USA.
Mayukhmala JanaDepartment of Biomedical Engineering, Georgia Tech, GA, USA; Department of Biomedical Engineering, Emory University, GA, USA.
Amritpal SinghDepartment of Biomedical Engineering, Emory University, GA, USA.
Tanmoy DamDepartment of Biomedical Engineering, Emory University, GA, USA.
Himanshu MauryaDepartment of Biomedical Engineering, Emory University, GA, USA.
Tilak PathakDepartment of Biomedical Engineering, Emory University, GA, USA.
Sandra OrsulicGreater Los Angeles Veterans Administration, CA, USA.
Kailin YangDepartment of Radiation Oncology, Holden Comprehensive Cancer Center, Iowa Neuroscience Institute, University of Iowa, Iowa, IA, USA.
Deborah ChuteDepartment of Anatomic Pathology, Cleveland Clinic, Cleveland, OH, USA.
Justin A BishopDepartment of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Farhoud FarajiDepartment of Otolaryngology-Head and Neck Surgery, UC San Diego Health, La Jolla, CA, USA.
Wade M ThorstadDepartment of Radiation Oncology, Washington University in St. Louis, St. Louis, MS, USA.
Shlomo KoyfmanDepartment of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA.
Scott StewardDepartment of Pathology and Laboratory Medicine, Emory University School of Medicine, GA, USA.
Qiuying ShiDepartment of Pathology and Laboratory Medicine, Oregon Health and Science University School of Medicine, OR, USA.
Vlad SandulacheBobby R. Alford Department of Otolaryngology Head and Neck Surgery, Baylor College of Medicine, TX, USA; ENT Section, Operative CareLine, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA.
Nabil F SabaDepartment of Hematology and Medical Oncology, Emory University School of Medicine, GA, USA.
James S LewisDepartment of Laboratory Medicine and Pathology, Mayo Clinic, AZ, USA.
Germán CorredorDepartment of Biomedical Engineering, Georgia Tech, GA, USA; Department of Biomedical Engineering, Emory University, GA, USA; Atlanta VA Medical Center, GA, USA.
Anant MadabhushiDepartment of Biomedical Engineering, Georgia Tech, GA, USA; Department of Biomedical Engineering, Emory University, GA, USA; Atlanta VA Medical Center, GA, USA. Electronic address: anantm@emory.edu.

Funding

P-CARRS-BRAIN: Multi-domain (genetic, socio-behavioral, vascular) risk factors and prediction of Alzheimer’s Disease continuum in South Asians in IndiaR01AG089759 · NIA · EMORY UNIVERSITY · PI Suvarna Alladi, ALLAN I LEVEY · 2024 to 2026
$12.8M
KPMP Kidney Mapping and Atlas Project (KMAP)U01DK133090 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$10.4M
Spatial Transcriptomics Explorer (STE): An open-source resource for visualizing spatial gene expression dataU24CA274494 · NCI · SAGE BIONETWORKS · PI Jineta Banerjee, Susheel Varma · 2022 to 2026
$10.1M
Pathology CoreU54CA254566 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2020 to 2024
$5.0M
Computational Pathology of Proteinuric DiseasesR01DK118431 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI BARISONI, LAURA MARIACHIARA, HODGIN, JEFFREY BENTON · 2018 to 2025
$3.6M
Computer-Assisted Histologic Evaluation of Cardiac Allograft RejectionR01HL151277 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MADABHUSHI, ANANT, MARGULIES, KENNETH BER · 2020 to 2023
$3.2M
Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC)R01CA249992 · NCI · EMORY UNIVERSITY · PI LEWIS, JAMES, MADABHUSHI, ANANT · 2021 to 2025
$3.2M
Prostate cancer risk stratification via computational 3D pathologyR01CA268207 · NCI · UNIVERSITY OF WASHINGTON · PI Jonathan T.C. Liu, Anant Madabhushi · 2022 to 2026
$3.1M
Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal CarcinomaR01CA220581 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYFMAN, SHLOMO, LEWIS, JAMES · 2018 to 2023
$3.1M
Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung CancersR01CA216579 · NCI · EMORY UNIVERSITY · PI FU, PINGFU, LLOYD, MARK · 2018 to 2023
$3.1M
Prognostic and Predictive Digital Tissue Image Assay for Prostate CancerR01CA268287 · NCI · EMORY UNIVERSITY · PI GUPTA, SHILPA, LAL, PRITI · 2022 to 2025
$3.0M
RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATIONR01HL158071 · NHLBI · CLEVELAND CLINIC LERNER COM-CWRU · PI BARNARD, JOHN, CHUNG, MINA KAY · 2021 to 2024
$2.9M
BLRD VA I01 BX004121BLRD VA I01 BX006020BLRD VA IK6 BX006185CSRD VA I01 CX002622CSRD VA I01 CX002776NCI NIH HHS R01 CA216579NCI NIH HHS R01 CA220581NCI NIH HHS R01 CA249992NCI NIH HHS R01 CA257612NCI NIH HHS R01 CA264017NCI NIH HHS R01 CA268207NCI NIH HHS R01 CA268287NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA269181NCI NIH HHS U24 CA274494NCI NIH HHS U54 CA254566NHLBI NIH HHS R01 HL151277NHLBI NIH HHS R01 HL158071NIAID NIH HHS R01 AI175555NIA NIH HHS R01 AG089759NIDCR NIH HHS R21 DE032344NIDDK NIH HHS R01 DK118431NIDDK NIH HHS U01 DK133090NLM NIH HHS R01 LM013864
6 · The paper itself

Abstract

backgroundVirtual 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.

methodsWe 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.

resultsHigh 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).

conclusionThese findings establish VISTA as a computational platform for generating virtual IHC and facilitating the discovery of novel biomarkers from H&E images.

Indexed as

Artificial IntelligenceEosine Yellowish-(YS)HematoxylinOropharyngeal NeoplasmsSquamous Cell Carcinoma of Head and NeckStaining and LabelingTumor-Associated MacrophagesAntigens, Differentiation, MyelomonocyticCD163 AntigenFemaleHumansImmunohistochemistryMaleTumor MicroenvironmentAntigens, Differentiation, MyelomonocyticCD163 AntigenEosine Yellowish-(YS)HematoxylinH&EHPV+ oropharyngeal squamous cell carcinomaImmunohistochemistryTumor-associated macrophagesTumor microenvironmentVirtual staining

Identifiers

PMID40158294
PMCPMC12021545

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.