Automated detection of Kaposi sarcoma-associated herpesvirus infected cells in immunohistochemical images of skin biopsies.

Immunohistochemical (IHC) staining for the antigen of Kaposi sarcoma-associated herpesvirus (KSHV), latency-associated nuclear antigen (LANA), is helpful in diagnosing Kaposi sarcoma (KS). A challenge, however, lies in distinguishing anti-LANA-positive cells from morphologically similar brown counterparts. In this work, we demonstrate a framework for automated localization and quantification of LANA positivity in whole slide images (WSI) of skin biopsies, leveraging weakly supervised multiple instance learning (MIL) while reducing false positive predictions by introducing a novel morphology-based slide aggregation method. Our framework generates interpretable heatmaps, offering insights into precise anti-LANA-positive cell localization within WSIs and a quantitative value for the percentage of positive tiles, which may assist with histological subtyping. We trained and tested our framework with an anti-LANA-stained KS pathology dataset prepared by pathologists in the United States from skin biopsies of KS-suspected patients investigated in Uganda. We achieved an area under the receiver operating characteristic curve (AUC) of 0.99 with a sensitivity and specificity of 98.15% and 96.00% in predicting anti-LANA-positive WSIs in a test dataset. We believe that the framework can provide promise for automated detection of LANA in skin biopsies, which may be especially impactful in resource-limited areas that lack trained pathologists.