Radiomics: the facts and the challenges of image analysis

Abstract

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

Keywords: Biomarkers; Clinical decision-making; Image processing (computer-assisted); Radiomics; Texture analysis.

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Figures

Fig. 1

Graphic representation of radiomic-feature clustering. This example graph displays the absolute value of the correlation coefficient (ranging from 0 to 1, on the right side, indicating increasing degree of correlation) between each pair of radiomic features (shown as numbers on the two axes). The heat map gives a good visual representation of the high correlation observed for most radiomic features that may be grouped in the same cluster to avoid redundancy. The yellow blocks along the diagonal graphically identify the clusters including highly correlated radiomic features. Blue blocks outside the diagonal visualise the low correlation observed between radiomic features belonging to different clusters. In the present example, two major clusters with different information may be identified, with very high redundancy for radiomic features in the first cluster (high homogeneity of the yellow blocks)

Fig. 2

Axial computed tomography images showing differences in the same acquisition plane between a contrast-enhanced (a) and an unenhanced image (b), as well as for different radiation doses, lower in (c), and higher in (d)

Fig. 3

Axial T2-weighted images of the pelvis, acquired keeping unchanged all the parameters, with only exception of the echo time, which was 34 ms in (a), 90 ms in (b), and 134 ms in (c), showing that even one single parameter can change the signal intensity of tissues and fluids, as clearly depicted by the signal of the bladder (white star), with higher and higher signal intensity from a to b to c

Fig. 4

An example of manual segmentation of lung cancer on computed tomography images. Although manual segmentation is often considered ground truth, this image shows red and black regions of interest delineated by two different readers for the same tumour