Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology

TLDR

The Gleason grading system, the most powerful prognostic predictor for prostate cancer, suffers from reproducibility issues and lacks granularity to capture potentially prognostic architectural features beyond its five patterns. This study evaluates prostate cancer architectural subtypes using topological data analysis, demonstrating that persistent homology can capture architectural features independently of Gleason patterns. By computing topological representations of graded histopathology images of Gleason patterns 3, 4, and 5 and clustering them with a ranked persistence vector, the authors produce architectural groups that can serve as inputs for future machine‑learning approaches to improve diagnosis and prognosis. Persistent homology successfully clusters prostate cancer images into unique groups aligned with the Gleason continuum and identifies sub‑architectural groups within single patterns, indicating a more granular and robust quantification method than current semi‑quantitative measures.

Abstract

The current system for evaluating prostate cancer architecture is the Gleason grading system which divides the morphology of cancer into five distinct architectural patterns, labeled 1 to 5 in increasing levels of cancer aggressiveness, and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability amongst pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We evaluate prostate cancer for architectural subtypes using techniques from topological data analysis applied to prostate cancer glandular architecture. In this work we demonstrate the use of persistent homology to capture architectural features independently of Gleason patterns. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns 3,4 and 5, and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through a ranked persistence vector. Our results indicate the ability of persistent homology to cluster prostate cancer histopathology images into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. The capability of these topological representations to segregate prostate cancer by architecture makes them an ideal candidate for use as inputs to future machine learning approaches with the intent of augmenting traditional approaches with topological features for improved diagnosis and prognosis.