ArticleJCO global oncology2025
Automated Detection of Kaposi Sarcoma-Associated Herpesvirus-Infected Cells in Immunohistochemical Images of Skin Biopsies.
Article in JCO global oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Kaposi sarcoma in the era of immunodeficiency: challenges and opportunities for treatment and prevention.Annals of medicine and surgery (2012) · 2026Article
- Virus infections and cancers: from mechanisms to therapeutics.Molecular biomedicine · 2026Review
- Diagnostic accuracy of machine-learning-based image analysis for early detection of Kaposi sarcoma in people living with HIV.Frontiers in artificial intelligence · 2026Review
- Emerging Trends in Point-of-Care Technology Development for Oncology in Low- and Middle-Income Countries.JCO global oncology · 2025Review
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Abstract
purposeImmunohistochemical staining for the antigen of Kaposi sarcoma (KS)-associated herpesvirus, latency-associated nuclear antigen (LANA), is helpful in diagnosing KS. A challenge lies in distinguishing anti-LANA-positive cells from morphologically similar brown counterparts. This work aims to develop an automated framework for localization and quantification of LANA positivity in whole-slide images (WSI) of skin biopsies.
methodsThe proposed framework leverages weakly supervised multiple-instance learning (MIL) to reduce false-positive predictions. A novel morphology-based slide aggregation method is introduced to improve accuracy. The framework generates interpretable heatmaps for cell localization and provides quantitative values for the percentage of positive tiles. The framework was trained and tested with a KS pathology data set prepared from skin biopsies of KS-suspected patients in Uganda.
resultsThe developed MIL framework achieved an area under the receiver operating characteristic curve of 0.99, with a sensitivity of 98.15% and specificity of 96.00% in predicting anti-LANA-positive WSIs in a test data set.
conclusionThe framework shows promise for the automated detection of LANA in skin biopsies, offering a reliable and accurate tool for identifying anti-LANA-positive cells. This method may be especially impactful in resource-limited areas that lack trained pathologists, potentially improving diagnostic capabilities in settings with limited access to expert analysis.
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