A total of 150 lesions, 104 malignant and 46 benign, presenting as non-mass-like enhancements were analyzed. Three radiologists performed BI-RADS reading for the morphological distribution and internal enhancement pattern. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps were generated, and PyRadiomics was applied to extract features. The radiomics model was built using 5 different machine learning algorithms. ResNet50 was implemented using three parametric maps as input. SVM yielded the highest accuracy of 80.4% in training, 77.5% in testing datasets. ResNet50 had better diagnostic performance, 91.5% in training, and 83.3% in testing datasets.