Pathomics-based machine learning model predicts interferon-gamma expression in head and neck squamous cell carcinoma patients.

ABSTRACT:

Introduction

Interferon-gamma (IFNG) plays a key role in immune responses in head and neck squamous cell carcinoma (HNSCC) and impacts the effectiveness of immune checkpoint inhibitors. This study developed a machine learning model that leverages pathomics to forecast IFNG expression based on histopathological images while thoroughly examining the tumor immune microenvironment in HNSCC.

Methods

The analysis involved 271 cases from The Cancer Genome Atlas (TCGA)-HNSCC, with validation using 71 patients from the Hospital. Significant links were found between IFNG expression, clinical features, and survival outcomes. For histopathological image processing, tumor regions were segmented using the OTSU algorithm, and 1,488 features were extracted with PyRadiomics. A feature select

SUBMITTER: Yu J 

PROVIDER: S-EPMC12682786 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature