  Motivation: Radiomics refers to the high-throughput mining of quantitative
features from radiographic images. It is a promising field in that it may
provide a non-invasive solution for screening and classification. Standard
machine learning classification and feature selection techniques, however, tend
to display inferior performance in terms of (the stability of) predictive
performance. This is due to the heavy multicollinearity present in radiomic
data. We set out to provide an easy-to-use approach that deals with this
problem.
  Results: We developed a four-step approach that projects the original
high-dimensional feature space onto a lower-dimensional latent-feature space,
while retaining most of the covariation in the data. It consists of (i)
penalized maximum likelihood estimation of a redundancy filtered correlation
matrix. The resulting matrix (ii) is the input for a maximum likelihood factor
analysis procedure. This two-stage maximum-likelihood approach can be used to
(iii) produce a compact set of stable features that (iv) can be directly used
in any (regression-based) classifier or predictor. It outperforms other
classification (and feature selection) techniques in both external and internal
validation settings regarding survival in squamous cell cancers.
