One of the main challenges in the integration of medical data reports is translating numerical features from different sources into a common abstract vocabulary that support a seamless combination of such data. When it comes to image analysis, a very common pipeline to describe the image involves extracting numerical features from image data and translate them into meaningful pre-defined semantic concepts. In this context, we propose a methodology for selecting numerical features and relating them to semantic features using the publicly available categorization in the lung nodules LIDC NIH database. We present several numerical features joined several classifiers, and a comparison between two feature selection methods and discuss how different features contribute to the discrimination of different semantic characteristics of lung nodules. Our results show the potential of such methodology for translating features into abstract semantic concepts for lung nodules characterization.