Anton Linnér will present his MSc thesis "Image based feature extraction to improve survival analysis in head and neck cancer" on Tuesday the 18th of June at 13.15-14.00 in MH:227

Abstract:

In this thesis we performed a pooled cohort study to investigate the role of radiomics in head and neck cancer prognosis. The aim was to investigate prognostic value for overall survival and cancer recurrence of radiomics combined with previously studied demographic and clinical risk factors. Radiomics features were extracted from the gross tumour volume on a computed tomography captured prior to radiotherapy treatment. Both standard statistical model such as Cox regression, and common machine learning methods such as random survival forest, DeepSurv and Deephit, were used. The cancer type was constricted to oropharyneal head and neck cancer due to large amount of missing data in the other head and neck cancer types. Prognostic performance for local recurrence was improved using shape related radiomics (sphericity) and clustering based methods (PCA). In contrast, the results showed no improved performance for overall survival (OS) for any model, where a possible reason might be too few events per covariate or that OS depends mainly on factors not captured by the radiomics data. These results indicate a role for radiomics in prognostic evaluation, which could prove to be useful treatment decision making and research guidance.

Keywords: Radiomics, Head and neck cancer, oropharynx, Cox regression, DeepSurv, DeepHit, Random survival forest, overall survival, local recurrence.