Radiogenomic Predictors of Head & Neck Cancer Treatment Response
Andrés M. Bur, M.D., FACS - Director, Robotics and Minimally Invasive Head and Neck Surgery
Head and neck squamous cell carcinoma (HNSCC) is a type of cancer that is challenging to treat because many patients experience disease progression even after treatment. One reason for this is that doctors don't have reliable ways to predict which patients are likely to fail after treatment. However, a new approach called radiomics has been used to analyze large numbers of tumor images to predict which patients are at risk for disease progression after treatment. To improve the ability to predict which patients with HNSCC will fail after treatment, this study aims to develop models that can predict how patients will do in the two years after treatment using different types of data, such as gene expression data from the tumor, liquid biopsy samples, and radiomics data from scans.
To accomplish this, the researchers will collect data from patients who have received radiation and chemotherapy for HNSCC. This data will include clinical information, scans, and samples of tissue and blood. The researchers will use this data to train machine learning models to predict how patients will do after treatment. The ultimate goal of this research is to develop models that can be used in clinical trials to identify patients who are at high risk of disease progression and who might benefit from additional or more personalized treatment approaches.
Director, Robotics and Minimally Invasive Head and Neck Surgery
Co-director, Head and Neck Oncology Fellowship
Associate Professor, Division of Head and Neck Surgery
Department of Otolaryngology – Head and Neck Surgery
Junior Faculty, Kansas Institute of Precision Medicine COBRE
University of Kansas School of Medicine