Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis

DOI10.1080/01621459.2019.1671198zbMath1441.62316arXiv1611.06818OpenAlexW3102583149WikidataQ127010661 ScholiaQ127010661MaRDI QIDQ5120654

Sayan Mukherjee, Lorin Crawford, Anthea Monod, Andrew X. Chen, Raúl Rabadán

Publication date: 15 September 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1611.06818

zbMATH Keywords

Euler characteristictopological data analysisfunctional dataglioblastoma multiformeshape statistics

Mathematics Subject Classification ID

Functional data analysis (62R10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Topological data analysis (62R40)

Related Items (18)

How Many Directions Determine a Shape and other Sufficiency Results for Two Topological Transforms ⋮ Discussion of “LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures” ⋮ Topological learning for brain networks ⋮ Euler characteristic surfaces ⋮ The (homological) persistence of gerrymandering ⋮ Using persistent homology topological features to characterize medical images: case studies on lung and brain cancers ⋮ Statistical embedding: beyond principal components ⋮ Persistence and the Sheaf-Function Correspondence ⋮ Bootstrapping persistent Betti numbers and other stabilizing statistics ⋮ On approximation theorems for the Euler characteristic with applications to the bootstrap ⋮ Persistent homology and the shape of evolutionary games ⋮ A statistical pipeline for identifying physical features that differentiate classes of 3D shapes ⋮ Realizations of Indecomposable Persistence Modules of Arbitrarily Large Dimension ⋮ On the law of the iterated logarithm and strong invariance principles in stochastic geometry ⋮ SECT ⋮ Decorated merge trees for persistent topology ⋮ Functional limit theorems for the euler characteristic process in the critical regime ⋮ Supervised learning with indefinite topological Kernels

Uses Software

• MITK
• MITKats

Cites Work

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