## 4 Discussion and Conclusions
As regards the univariate dynamic analysis, higher AUC values occurred in correspondence with q2, WIN and TIC-AUC--specifically for q2_MAD (AUC = 0.68), q2_IQR (AUC = 0.68), WIN_STD (AUC = 0.68), WIN_MAD (AUC = 0.70), WIN_IQR (AUC = 0.70) and TIC-AUC_STD (AUC = 0.68). Furthermore, ME and WOUT were the least significant descriptors, with AUC values lower than 0.6. As for the multivariate dynamic analysis, the best performance (accuracy of 0.82 and AUC of 0.83) using dynamic features was reached with an SVM trained with balanced data (with ADASYN function). The small cohort of studied patients represents an initial finding to validate by increasing the sample size of the study in the future. The segmentation of the ROIs slice by slicewas manual, and this can be time-consuming; in fact, to analyze DCE-MRI images for each patient, the radiologists should segment them manually, spending about 2 min annotating all the slices of lesion. The future endpoint is to include in the analysis an automatic segmentation of the lesions. The radiomic analysis did not consider tumor histological differences, while the integration of texture, morphological and dynamic metrics combined with histopathology results may provide other important prognostic information for the classification of malignant breast lesions both in early and late phase. This could improve the performance in the classification problem and allow to classify breast lesions according grading and histotype. A future endpoint could be to correlate imaging metrics with clinical features such as the age of patient that could influence breast tissue with consequent radiomic effects and that could be used to classify the breast lesions according to density and BIRADS. In conclusion, multivariate analyses using pattern recognition approaches optimized by adaptive synthetic sampling and feature selection operations obtained the best results to separate benign and malignant lesions. Among different classifiers, the SVM has proven to be the best-performing in terms of accuracy, specificity, sensitivity and ROC curve trend. Overall, dynamic features (as a group) extracted directly from the time-intensity curve, showed the best performance. However, the best results (ACC = 0.88; AUC = 0.93) were reached by considering all characteristics of lesions (heterogeneity using textural metrics; shape and morphology features and vascularization using dynamic features), using an SVM trained with balanced data (with ADASYN function) and a subset of 37 robust features obtained with the LASSO approach. The following are available online at [[https://www.mdpic.com/2076-341](https://www.mdpic.com/2076-341)]([https://www.mdpic.com/2076-341](https://www.mdpic.com/2076-341)) 7/11/4/1880/s1, Definition of textural features. Table S1: Texture features list. Each author participated sufficiently to take public responsibility for the manuscript content. [PERSON], [PERSON] ([PERSON]) and [PERSON] performed the statistical analyses and wrote the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. ([PERSON]) performed the investigation, defined the methodology and revised the manuscript. The authors have no conflict of interest to be disclosed. The authors confirm that the article is not under consideration for publication elsewhere. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee National Cancer Institute of Naples Pascale Foundation (Deliberation N. 617 of 09/08/2016). All patients enrolled signed the informed consent. No additional data are available. The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.