| Title: | Identification of gene-environment interactions that predict head and neck cancer (HNC) - a machine learning approach. |
| Lead Institution: | University of Gothenburg |
| Principal investigator: | Dr Lisa Tuomi |
Application 76314
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About
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. Here we intend to use machine learning methods, i.e. the development of computer systems that are able to learn and adapt by using algorithms and statistical models, to analyse and draw inferences from patterns in data. Our goal is to identify novel risk factors for development of HNC and to investigate the role of environment-gene interactions in HNC. Our models will result in novel definitions of HNC and risk factor importance.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14760 | Genetic, clinical, lifestyle and sociodemographic risk factors for head and neck cancer: A UK Biobank study | Lisa Tuomi (+5) | 2025 | PLOS ONE |
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