A Weakly Supervised Approach for HPV Status Prediction in Oropharyngeal Carcinoma from H&E-Stained Slides.

ABSTRACT: Background: Human papillomavirus (HPV) plays a crucial role in the pathogenesis of oropharyngeal squamous cell carcinomas (OPSCC). Accurate HPV status classification is essential for therapeutic stratification. While p16 immunohistochemistry (IHC) is the clinical surrogate marker, it has limited specificity. Methods: In this study, we implemented a weakly supervised deep learning approach using the Clustering-constrained Attention Multiple-Instance Learning (CLAM) framework to directly predict HPV status from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) of OPSCC. A total of 123 WSIs from two cohorts (The Cancer Genome Atlas (TCGA) cohort and OPSCC cohort from the University of Naples Federico II (OPSCC-UNINA)) were used. Results: Attention heatmaps rev

SUBMITTER: Crispino A 

PROVIDER: S-EPMC12730384 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature