## 4 Discussion and Conclusions  
Breast MRI is the most sensitive imaging technique for breast cancer detection and the most accurate for assessment of disease extent. In recent years, many studies have addressed the problem of breast lesion classification by using several feature categories such as textural, morphological and textural features, in combination with different machine learning approaches, based on DCE-MRI images analysis [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. In fact, [PERSON] et al. [46] reported an accuracy of 0.93 (SENS = 0.94; SPEC = 0.91) using textural features and an SVM classifier for soft tissue tumors. [PERSON] et al. [15] achieved an accuracy of 0.75 (SENS = 0.71; SPEC = 0.78) with an artificial neural network (ANN) classifier and using texture information from short tau inversion recovery (STIR) images for distinguishing between benign and malignant soft tissue masses. [PERSON] et al. [30] reported an accuracy of 0.86 (SENS = 0.88; SPEC = 0.78) using textural features and an SVM classifier to predict histologically proven malignant breast lesions. [PERSON] et al. [20] achieved an accuracy of 0.91 (SENS = 0.90; SPEC = 0.91) using static region descriptors and a neural network classifier on 14 patients to detect breast lesions. [PERSON] et al. [48] reported a sensitivity of 0.82 and a specificity of 0.88 with dynamic features and a multilevel analysis strategy on a database of 171 breast lesions. [PERSON] et al. [51] demonstrated statistical robustness of an SVM method that involves a reformulation of the classification/prediction process with dynamic information. In addition, [PERSON] et al. [18] reported an accuracy of 0.93 (SPEC = 0.80; SENS = 0.97) by combining morphological and dynamic features and using an ANN classifier on a group of 216 patients. [PERSON] et al. [24] reported a sensitivity of 0.95 through a combination of dynamic and spatial parameters and using a linear classifier in an leave one out cross validation setting. Lastly, [PERSON] et al. [43] yielded an accuracy of 0.90 (SENS = 0.95; SPEC = 0.82) and an AUC of 0.92 by combining texture, kinetic and morphological features on a dataset of 41 breast lesions. In this study, we aimed to evaluate radiomic analysis with texture, morphological and dynamic features extracted by DCE-MRI images in the classification of malignant and benign breast lesions. We performed the evaluation considering both a univariate analysis and a multivariate analysis using pattern recognition approaches. The univariate textural analysis showed statistically positive results for MODE (AUC = 0.7), STD (AUC = 0.7) and RANGE (AUC = 0.73) among first-order gray-level statistics, in addition to GLN as regards both the Gray-Level Run-Length Matrix and the Gray-Level Size Zone Matrix (AUC values of 0.70 and 0.69, respectively). As regards multivariate texture analysis, using the unbalanced dataset showed no significant results; in particular, specificity assumed very low values. After a first balancing operation (with ADASYN function), higher values of accuracy, specificity and AUC, but lower ones of sensitivity, were measured. An LDA trained with balanced data achieved the best performance with an accuracy of 0.78 and an AUC of 0.78. Using the robust textural predictors obtained by LASSO, no improvements were reached. With the univariate morphological analysis, there were no significant results. With the multivariate morphology analysis, the best performance (accuracy of 0.75 and AUC of 0.80) was obtained using morphological features with an SVM trained with balanced data (with ADASYN function) and a subset of five robust morphological features including circularity, rectangularity, sphericity, gleaning and surface.