The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status

In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naïve Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.


Shape Feature G1
Sphericity. Similarity of the lesion shape to a sphere.

G2
Irregularity. Deviation of the lesion surface from the surface of a sphere.

G3
Surface-to-volume ratio (1∕mm). Ratio of surface area to volume.

M1
Mean of the image gradient at the lesion margin.

M2
Variance of the image gradient at the lesion margin.

M3
Indicates how well the enhancement structure in a lesion extends in a radial pattern originating from the center of the lesion.

Enhancement textures T1
Contrast. Measure of local image variations.

T2
Correlation. Measure of image linearity.

T3
Difference entropy. Measure of the randomness of the difference of neighboring voxels' gray levels.

T4
Difference variance. Measure of variations of difference of gray levels between voxel pairs. T5 Angular second moment (energy). Measure of image homogeneity.

T6
Entropy. Measure of the randomness of the gray levels.

T7
Inverse difference moment. Measure of the image homogeneity.

T8
Information measure of correlation 1. Measure of nonlinear gray-level dependence.

T9
Information measure of correlation 2. Measure of nonlinear gray-level dependence.

T10
Maximum correlation coefficient. Measure of nonlinear gray-level dependence.

T11
Sum average. Measure of the overall image brightness

T12
Sum entropy. Measure of the randomness of the sum of gray levels of neighboring voxels.

T13
Sum variance. Measure of the spread in the sum of the gray levels of voxel-pairs distribution.

T14
Sum of squares (variance). Measure of the spread in the gray-level distribution.

K2
Time to peak (s). Time at which the maximum enhancement occurs.

K5
Curve shape index. Difference between late and early enhancement.

K6
Enhancement at first postcontrast time point. Enhancement at first postcontrast time point.

K7
Signal enhancement ratio of initial enhancement to overall enhancement.

Enhancement-variation kinetics E1
Maximum variance of enhancement Maximum spatial variance of contrast enhancement over time.

E2
Time to peak at maximum variance (s). Time at which the maximum variance occurs.

E3
Enhancement variance increasing rate (1∕s). Rate of increase of the enhancement variance during uptake.