Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images


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 data analysis (TDA). This extends previous work on the use of these features for representing the characteristics of prostate cancer architecture in region of interest (ROI) images, and demonstrates the value of features derived from persistent homology to predict cancer aggressiveness of WSIs on an ROI basis. We compute persistence on ROI images and summarize persistence as a persistence image. Using this summary we construct a random forest classifier to predict cancer aggressiveness. We demonstrate the potential of persistent homology to capture the architectural differences between low and high grade prostate cancers in a feature representation that lends itself well to machine learning approaches.

SPIE Medical Imaging - Digital Pathology
San Diego, CA
Peter Lawson
NSF Fellow and PhD Candidate in Bioinnovation

My current research involves applying topological data analysis to gain insights into topological differences in cancer morphology at the histological level and their importance in diagnosis and prognosis.