In the context of breast cancer, the detection and segmentation of cancerous lymph nodes in PET/CT imaging is of crucial importance, in particular for staging issues. In order to guide such image analysis procedures, some dedicated descriptors can be considered, especially region-based features. In this article, we focus on the issue of choosing which features should be embedded for lymph node tumor segmentation from PET/CT. This study is divided into two steps. We first investigate the relevance of various features by considering a Random Forest framework. In a second time, we validate the expected relevance of the best scored features by involving them in a U-Net segmentation architecture. We handle the region-based definition of these features thanks to a hierarchical modeling of the PET images. This analysis emphasizes a set of features that can significantly improve / guide the segmentation of lymph nodes in PET/CT.