Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies
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 de...
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| Format: | Article |
| Language: | English |
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American Society of Clinical Oncology
2025-04-01
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| Series: | JCO Global Oncology |
| Online Access: | https://ascopubs.org/doi/10.1200/GO-24-00536 |
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| author | Iftak Hussain Juan Boza Robert Lukande Racheal Ayanga Aggrey Semeere Ethel Cesarman Jeffrey Martin Toby Maurer David Erickson |
| author_facet | Iftak Hussain Juan Boza Robert Lukande Racheal Ayanga Aggrey Semeere Ethel Cesarman Jeffrey Martin Toby Maurer David Erickson |
| author_sort | Iftak Hussain |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9afcfe0a6e9d4cc38b3fd69a4f4fc81b |
| institution | DOAJ |
| issn | 2687-8941 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | American Society of Clinical Oncology |
| record_format | Article |
| series | JCO Global Oncology |
| spelling | doaj-art-9afcfe0a6e9d4cc38b3fd69a4f4fc81b2025-08-20T03:10:27ZengAmerican Society of Clinical OncologyJCO Global Oncology2687-89412025-04-011110.1200/GO-24-00536Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin BiopsiesIftak Hussain0Juan Boza1Robert Lukande2Racheal Ayanga3Aggrey Semeere4Ethel Cesarman5Jeffrey Martin6Toby Maurer7David Erickson8Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NYMeinig School of Biomedical Engineering, Cornell University, Ithaca, NYDepartment of Pathology, Makerere University College of Health Sciences, Kampala, UgandaInfectious Diseases Institute, Makerere University College of Health Sciences, Kampala, UgandaInfectious Diseases Institute, Makerere University College of Health Sciences, Kampala, UgandaPathology and Laboratory Medicine, Weill Cornell Medical College, New York, NYDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CADepartment of Dermatology, Indiana University School of Medicine, Indianapolis, INSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NYPURPOSEImmunohistochemical 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.https://ascopubs.org/doi/10.1200/GO-24-00536 |
| spellingShingle | Iftak Hussain Juan Boza Robert Lukande Racheal Ayanga Aggrey Semeere Ethel Cesarman Jeffrey Martin Toby Maurer David Erickson Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies JCO Global Oncology |
| title | Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies |
| title_full | Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies |
| title_fullStr | Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies |
| title_full_unstemmed | Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies |
| title_short | Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies |
| title_sort | automated detection of kaposi sarcoma associated herpesvirus infected cells in immunohistochemical images of skin biopsies |
| url | https://ascopubs.org/doi/10.1200/GO-24-00536 |
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