Using Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lung and Brain Cancers

Understand

Tumor shape is a key factor that affects tumor growth and metastasis.

• This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data.
• The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns.
• The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model.