Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era,
with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual
information – based method for quantifying reproducibility of features, a necessary step for qualification before their
inclusion in big data systems.
Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7
time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic
method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was
assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema.
Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume
ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the
same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values
was unable to make a difference between features.
Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner.
This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a
promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.