In this study, we present an automated approach to classify prostate cancer (Pca) whole slide images (WSIs)as high or low cancer aggressiveness using features derived from persistent homology, a tool of topological dataanalysis (TDA). This extends previous work on the use of these features for representing the characteristics ofprostate cancer architecture in region of interest (ROI) images, and demonstrates the value of features derivedfrom persistent homology to predict cancer aggressiveness of WSIs on an ROI basis. We compute persistenceon ROI images and summarize persistence as a persistence image. Using this summary we construct a randomforest classier to predict cancer aggressiveness. We demonstrate the potential of persistent homology to capturethe architectural dierences between low and high grade prostate cancers in a feature representation that lendsitself well to machine learning approaches.
展开▼
