A Bayesian Network Model of Head and Neck Squamous Cell Carcinoma Incorporating Gene Expression Profiles

Radiation therapy allows precision targeting of certain groups of lymph nodes and is a treatment for metastatic head and neck squamous cell carcinoma. In current practice, there is approximately 15% probability that physicians inadvertently treat healthy tissue or leave the cancerous lymph nodes untreated. The aim of this work is to improve the accuracy of medical decision-making by extending existing predictive models to capture the probabilities offending cancerous lymph nodes at each of the six image-based surgical neck level using a patient's genetic profile, primary tumor site and tumor size. Our model was trained with publicly available data extracted from the Cancer Genome Atlas (TCGA) and validated against the TCGA dataset both with and without genetic information. Results show that genetic profile data improves model accuracy. These findings suggest that our predicitive model may improve the accuracy of clinical decision-making, especially for patients with more advanced metastasis. However, more data is needed to ensure significance of the proposed effects, as well as to improve accuracy of the overall model.