Prediction of recurrence free survival of head and neck cancer using
  PET/CT radiomics and clinical information
Mona Furukawa Daniel R. McGowan Bartłomiej W. Papież
The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over
the past decade and one common cause of treatment failure is recurrence. In
this paper, we built Cox proportional hazard (CoxPH) models that predict the
recurrence free survival (RFS) of oropharyngeal HNC patients. Our models
utilise both clinical information and multimodal radiomics features extracted
from tumour regions in Computed Tomography (CT) and Positron Emission
Tomography (PET). Furthermore, we were one of the first studies to explore the
impact of segmentation accuracy on the predictive power of the extracted
radiomics features, through under- and over-segmentation study. Our models were
trained using the HEad and neCK TumOR (HECKTOR) challenge data, and the best
performing model achieved a concordance index (C-index) of 0.74 for the model
utilising clinical information and multimodal CT and PET radiomics features,
which compares favourably with the model that only used clinical information
(C-index of 0.67). Our under- and over-segmentation study confirms that
segmentation accuracy affects radiomics extraction, however, it affects PET and
CT differently.