**Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions**

**Roberta Fusco 1, Elio Di Bernardo 1, Adele Piccirillo 2, Maria Rosaria Rubulotta 3, Teresa Petrosino 3, Maria Luisa Barretta 3, Mauro Mattace Raso 3, Paolo Vallone 3, Concetta Raiano 3, Raimondo Di Giacomo 4, Claudio Siani 4, Franca Avino 4, Giosuè Scognamiglio 5, Maurizio Di Bonito 5, Vincenza Granata 3,\*,† and Antonella Petrillo 3,†**


**Abstract: Purpose**:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. **Methods**: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. **Results**: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level runlength matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). **Conclusions:** Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

**Keywords:** contrast-enhanced mammography; magnetic resonance imaging; image enhancement; contrast media; radiomics; artificial intelligence

**Citation:** Fusco, R.; Di Bernardo, E.; Piccirillo, A.; Rubulotta, M.R.; Petrosino, T.; Barretta, M.L.; Mattace Raso, M.; Vallone, P.; Raiano, C.; Di Giacomo, R.; et al. Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions. *Curr. Oncol.* **2022**, *29*, 1947–1966. https://doi.org/ 10.3390/curroncol29030159

Received: 24 January 2022 Accepted: 10 March 2022 Published: 13 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
