The prognostic value of CT radiomic features from primary tumours and pathological lymph nodes in head and neck cancer patients

Head and neck cancer (HNC) is responsible for about 0.83 million new cancer cases and 0.43 million cancer deaths worldwide every year. Around 30%-50% of patients with locally advanced HNC experience treatment failures, predominantly occurring at the site of the primary tumor, followed by regional failures and distant metastases. In order to optimize treatment strategy, the overall aim of this thesis is to identify the patients who are at high risk of treatment failures. We developed and externally validated a series of models on the different patterns of failure to predict the risk of local failures, regional failures, distant metastasis and individual nodal failures in HNC patients. New type of radiomic features based on the CT image were included in our modelling analysis, and we firstly showed that the radiomic features improved the prognostic performance of the models containing clinical factors significantly. Our studies provide clinicians new tools to predict the risk of treatment failures. This may support optimization of treatment strategy of this disease, and subsequently improve the patient survival rate.