Renal Cell Carcinoma Classification from Vascular Morphology

Renal Cell Carcinoma (RCC) is one of the most common malignancies, and pathological diagnosis is the gold standard for RCC diagnostic method. Recognizing the type of RCC tumor and the possibility of cell migration highly depends on the geometric and topological properties of the vascular network. Motivated by the diagnosis pipeline, we explore whether the vascular network visible in RCC histopathological images is sufficient to characterize the RCC subtype. To achieve this, we firstly build a new vascular network-based RCC histopathological image dataset of 7 patients, namely VRCC200, with 200 well-labeled vascular network annotations. Based on these vascular networks of RCC histopathological images, we propose new hand-crafted features, namely skeleton features and lattice features. These features well represent the geometric and topological properties of the vascular networks of RCC histopathological images. Then we build strong benchmark results with various algorithms (both traditional and deep learning models) on the VRCC200 dataset. The result of skeleton and lattice features can outperform popular deep learning models. Finally, we further prove the robustness and advantage of proposed features on an additional database VRCC60 of 20 patients, with 60 vascular annotated images. All of the results of our experiments prove that the vascular network structure of RCC is one of the most important biomarkers for RCC diagnosis.

Automated summary

This study investigates the importance of vascular network structure in RCC diagnosis, and builds a dataset and features to demonstrate the role of vascular networks in RCC histopathological images.