Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images
This work addresses the need for accurate HPV status determination in head and neck squamous cell carcinoma to influence prognosis and treatment, representing a domain-specific advancement.
The authors tackled the problem of detecting HPV infection in head and neck cancers using routine H&E images, achieving state-of-the-art performance on two cohorts with a novel triplet-ranking loss function and multiple instance learning pipeline.
The aetiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens such as alcohol, tobacco and infection with human papillomavirus (HPV). As the HPV infection influences the prognosis, treatment and survival of patients with HNSCC, it is important to determine the HPV status of these tumours. In this paper, we propose a novel triplet-ranking loss function and a multiple instance learning pipeline for HPV status prediction. This achieves a new state-of-the-art performance in HPV detection using only the routine H&E stained WSIs on two HNSCC cohorts. Furthermore, a comprehensive tumour microenvironment profiling was performed, which characterised the unique patterns between HPV+/- HNSCC from genomic, immunology and cellular perspectives. Positive correlations of the proposed score with different subtypes of T cells (e.g. T cells follicular helper, CD8+ T cells), and negative correlations with macrophages and connective cells (e.g. fibroblast) were identified, which is in line with clinical findings. Unique gene expression profiles were also identified with respect to HPV infection status, and is in line with existing findings.