In this paper we examine a technique for developing prognostic
image characteristics, termed radiomics, for non-small cell
lung cancer based on a tumour edge region-based analysis.
Texture features were extracted from the rind of the tumour in
a publicly available 3D CT data set to predict two-year survival.
The derived models were compared against the previous
methods of training radiomic signatures that are descriptive
of the whole tumour volume. Radiomic features derived solely
from regions external, but neighbouring, the tumour were
shown to also have prognostic value. By using additional texture
features an increase in accuracy, of 3%, is shown over
previous approaches for predicting two-year survival, upon
examining the outside rind including the volume compared to
the volume without the rind. This indicates that while the centre
of the tumour is currently the main clinical target for radiotherapy
treatment, the tissue immediately around the tumour
is also clinically important.