**4. Discussion**

Using texture features from dual-energy CEM and DCE-MRI, considered both individually and in combination, we aimed to evaluate radiomic analysis in discriminating between malignant and benign breast lesions.

In recent years, many studies have addressed the problem of breast lesion classification by using several feature categories such as textural and morphological features, in combination with different machine learning approaches, based on CEM and on DCE-MRI images analysis considered separately [30–41,56–60].

Marino et al. [61] investigated the potential of radiomic analysis of both CEM and DCE-MRI of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomic analysis was performed by using MaZda software. Radiomic parameters were correlated with tumor histology (invasive vs. noninvasive), hormonal status (HR+ vs. HR−) and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR− breast cancers and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR− breast cancers and 77.8% for G1 + G2 vs. G3 cancers. Their study, however, did not reported the combination of radiomic features extracted from CEM and DCE-MRI.

Jiang et al. [62] noninvasively evaluated the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT) and dynamic contrast-enhanced and diffusion-weighted (DW) magnetic resonance imaging images separately and combined to predict the Ki-67 level based on radiomics. Their results demonstrated that the combined intra- and peritumoral radiomic signatures improved the AUC compared with the intra- or peritumoral radiomic signature in each modality. The nomogram incorporating the multi-model radiomics signature, age and lymph node metastasis status achieved the best prediction performance in the training (AUC = 0.922) and validation (AUC = 0.866) cohorts.

Zhao et al. [63] constructed radiomic models from DCE-MRI and mammography for the values in the diagnosis of breast cancer, reporting an accuracy of the individual model of 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin and 77.2% for lesion + margin. When all features were combined, the accuracy increased to 89.6%.

Niu et al. [64] evaluated digital mammography, DBT, DCE- and DW-MRI, individually and combined, for the values in the diagnosis of breast cancer. They reported that the radiomic signature derived from DBT plus DM generated a lower AUC and sensitivity, but a higher specificity compared with that from DCE plus DWI. The nomogram integrating the combined radiomic signature, age and menstruation status achieved the best diagnostic performance in the training (AUC = 0.975) and validation (AUC = 0.983) cohorts.

Our results demonstrated that, 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 run-length matrix (AUC = 0.72).

Considering the analysis with classifiers and the 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).

This study had some limitations. The small cohort of studied patients represents a preliminary result to validate increasing the cohort of patients. Manual segmentation was time-consuming and could be operator-dependent and lose reproducibility; however, an automatic segmentation considering possible multicentric lesions or background parenchymal enhancement could be difficult to perform. In this study, the histological differences of tumors were not considered. This could improve the performance in the classification problem and allow for the classification of breast lesions according to grading and histotype.

Both DCE-MRI and CEM provide functional information on neoplastic neo-angiogenesis.CEM is an attractive alternative when MRI is not available, contraindicated or poorly tolerated. However, at our institution, a study protocol to compare DCE-MRI and CEM in staging and follow-up in breast cancer is still ongoing. Therefore, a future endpoint could be to design separate classifiers for CEM and DCE-MRI images and then merge the results in specific clinical settings, such as during patient follow-up in cases of suspicious local recurrence.
