ICEVis: Interactive Clustering Exploration for tumor sub-region analysis in multiparametric cancer imaging.

Tumor tissue characteristics derived from imaging data are gaining importance in clinical research. Tumor sub-regions may play a critical role in defining tumor types and may hold essential information about tumor aggressiveness. Depending on the tumor’s location within the body, such sub-regions can be easily identified and determined by physiology, but these sub-regions are not readily visible to others. Regions within a tumor are currently explored by comparing the image sequences and analyzing the tissue heterogeneity present. To improve the exploration of such tumor sub-regions, we propose a visual analytics tool called ICEVis. ICEVis supports the identification of tumor sub-regions and corresponding features combined with cluster visualizations highlighting cluster validity. It is often difficult to estimate the optimal number of clusters; we provide rich facilities to support this task, incorporating various statistical measures and interactive exploration of the results. We evaluated our tool with three clinical researchers to show the potential of our approach.