Ivar: Interactive Visual Analytics Of Radiomics Features From Large-Scale Medical Images

Medical imaging enables researchers and practitioner to uncover the characteristics of diseases (e.g., human cancer) in great detail. However, the sheer size of resulting imaging data and the high dimension of derived features become a major challenge in data analysis, diagnosis, and knowledge discovery. We present a novel visual analytics system, named iVAR, targeted at observing the comprehensive quantification of tumor phenotypes by effectively exploring a large number of quantitative image features. Our system is comprised of multiple linked views combining visualization of three-dimensional volumes and tumors reconstructed by computed tomography (CT) images, and a radiomic analysis of high-dimensional features quantifying tumor image intensity, shape and texture, and three non-image clinical features. Thus, it offers insights into the overall distribution of quantitative imaging features and also enables detailed analysis of the relationship between features. We demonstrate our system through use case scenarios on a real-world large-scale CT dataset with lung cancer.