Automated Detection of Kaposi Sarcoma-Associated Herpesvirus-Infected Cells in Immunohistochemical Images of Skin Biopsies.

Purpose: Immunohistochemical 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.

Methods: The 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.

Results: The 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.

Conclusion: The 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.