Identification of gene-environment interactions that predict head and neck cancer (HNC) - a machine learning approach.
As many as 1 600 patients are annually treated for head and neck cancer (HNC) in Sweden. The standard oncological treatment of HNC is associated with adverse effects that often lead to long-term disability, reduced health-related quality of life (HRQL) and high societal costs. Moreover, approximately 30% are diagnosed with cancer recurrence within 3 years post-treatment, with poor prognosis. Thus, the HNC patient group is commonly burdened by quality-of-life and socially debilitating symptoms as well as high mortality.