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

Abstract

Radiation therapy is a treatment for metastatic Head and Neck Squamous Cell Carcinoma, which allows precision targeting of certain groups of lymph nodes. A Bayesian network predictive model was developed aiming to help achieve such precision using information on the primary site and size of the tumor, representing the current decision making process in clinical settings. Additional risk factors, the patient’s genetic profile and smoking history, were added to examine their predictability of metastasis through the improvement in prediction accuracies. The model was trained with publicly available data extracted from the Cancer Genome Atlas (TCGA) and validated against the TCGA dataset as well as clinical data reported to the University of Washington Tumor Board. Results show that genetic profile data improves model accuracy and such improvement may affect clinical decision making especially for patients with more advanced metastasis. A prototype for decision support application was built based on the results to demonstrate the clinical significance of the model. However, more data is needed to show significance of the proposed effects, as well as to improve accuracy of the overall model.