Radiomics Software Tools: A comparative Analysis on Breast Cancer

Radiomics is an emerging and promising field used to describe visual information from medical images by means of numerical features. Several Radiomics software tools are available in the literature, but they return different features and make dissimilar calculations. Choosing one tool or another is not easy so a comparison for classification tasks is required. This paper compares three of these frameworks (3D Slicer, LIFEx and MaZda) on breast cancer data. In this analysis, we tested the features extracted from each tool using different pre-processing techniques and machine learning algorithms to classify the lesion as benign or malignant on more than 350 registers. Two different projections were considered, that is, craniocaudal (183 registers) and mediolateral oblique (172 registers). The results demonstrated that 3D Slicer obtained the best performance in the craniocaudal projection, while MaZda and LIFEx are more appropriate for the mediolateral oblique projection. The results are really promising for classification tasks, exceeding 85% in F1-score.