Radiomic features analysis by digital breast tomosynthesis and contrast-enhanced dual-energy mammography to detect malignant breast lesions

•Lesion texture and morphological features can capture often-missed information regarding the characteristics of a tumor malignancy.•Lesion texture and morphological features can provide details that have prognostic or diagnostic value.•Radiomic texture features by CEDM and morphological features by DBT showed a potential in differentiating malignant to benign lesions. To detect malignant breast lesions using radiomic morphological features from Digital Breast Tomosynthesis (DBT) and radiomic textural features from Contrast-enhanced Dual-Energy Digital Mammography (CEDM). In a 8-month period, we enrolled 72 consecutive patients with breast lesions; their age ranging from 26 to 72 years (mean, 52.2; standard deviation 11.1). Ninety-three breast lesions subjected to CEDM and DBT in cranio caudal (CC) and mediolateral oblique (MLO) view were included: 36 histopathologically proven benign lesions and 59 histopathologically proven malignant lesions were analyzed. We considered a feature set including 23 textural features calculated on CEDM and 14 morphological features extracted by DBT. Non-parametric statistics, receiver operating characteristic with area under curve (AUC), Spearman correlation coefficient and Bonferroni correction were applied. At univariate analysis, the area under ROC was obtained by the best textural feature, the contrast with a value of 0.78. To differentiate malignant lesions with different grading only one textural feature had significant results: median absolute deviation (MAD) (p