Pathomics-based machine learning model predicts interferon-gamma expression in head and neck squamous cell carcinoma patients
In conclusion, the study shows that pathomics from histopathological images can predict IFNG expression. The correlation between IFNG and pathomics provides a valuable biomarker framework for elucidating the pathophysiology of HNSCC and may inform personalized therapeutic strategies through non-invasive characterization of the immune microenvironment.
Authors
- Wei Teng
- Gang Yu (ORCID: https://orcid.org/0000-0001-7329-3594)
- Yan Wang (ORCID: https://orcid.org/0000-0002-4751-0708)
- Zheng Wang (ORCID: https://orcid.org/0000-0002-8388-6139)
- Fei Wang (ORCID: https://orcid.org/0000-0002-4498-8933)
- Bin Bai (ORCID: https://orcid.org/0000-0002-0235-1344)
- Jintao Yu (ORCID: https://orcid.org/0009-0004-4354-0426)
Institutions
- National Health and Family Planning Commission (CN)
- Theranostics (New Zealand) (NZ)
- First Hospital of China Medical University (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-11-24
- DOI
- https://doi.org/10.3389/fimmu.2025.1652003
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Liaoning Province