Early Radiomic Experiences in Classifying Prostate Cancer Aggressiveness using 3D Local Binary Patterns

Abstract

Prostate cancer is the most common form of cancer in Western countries and there is the need to develop clinical decision support systems able to support physicians in the diagnosis of clinical relevant prostate cancer and avoid useless invasive prostate biopsies. In this respect, this paper introduces a radiomic approach that classifies the prostate cancer aggressiveness by combining Three Orthogonal Planes-Local Binary Pattern (TOP - LBP) with other texture measures. Furthermore, to combat the skewed nature of class priors, our proposal employs a data augmentation technique. The results achieved on 99 samples are up-and-coming, they favorably compare against conventional PI-RADS-based approach, and they show also the benefit given by the introduction of TOP-LBP in the radiomic signature.