The renal cell carcinoma (RCC) is the most frequent type of kidney cancer (between 90% and 95%). Twelve subtypes of RCC can be distinguished, among which the clear cell carcinoma (ccRCC) and the papillary carcinoma (pRCC) are the two most common ones (75% and 10% of the cases, respectively). After resection (i.e., surgical removal), the tumor is prepared for histological examination (fixation, slicing, staining, observation with a microscope). Along with protein expression and genetic tests, the histological study allows to classify the tumor and define its grade in order to make a prognosis and to take decisions for a potential additional chemotherapy treatment. Digital histology is a recent domain, since routinely, histological slices are studied directly under the microscope. The pioneer works deal with the automatic analysis of cells. However, a crucial factor for RCC classification is the tumoral architecture relying on the structure of the vascular network. For example, coarsely speaking, ccRCC is characterized by a ``fishnet'' structure while the pRCC has a tree-like structure. To our knowledge, no computerized analysis of the vascular network has been proposed yet. In this context, we developed a complete pipeline to extract the vascular network of a given histological slice and compute features of the underlying graph structure. Then, we studied the potential of such a feature-based approach in classifying a tumor into ccRCC or pRCC. Preliminary results on patient data are encouraging.