Assessing the prognostic impact of 3D CT image tumour rind texture features on lung cancer survival

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.